Learn to Build a ChatGPT Clone, Machine Learning Bundle+
Build chatbots with artificial intelligence! Learn to code in Python. Master data science and supercharge your career! With source code and prompts!
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Is your career stuck in a dead end? Do you worry about automation?
Transform your life by taking an online course. Add a new skill to your resume. Crack that job interview and find financial freedom.
ChatGPT and machine learning are taking the world by storm! 🧠
🤖 Do you want to learn the secrets behind chatbots?
Do you want to build the next viral machine learning project? 🧑💻
Do you want to get hands-on with AI?
This 100+ hour video course bundle is for you!
- Level 1 - Python for Beginners
- Level 2 - Data Science and Machine Learning
- Level 3 - Build Chatbots from Scratch
- Level 4 - Master the ChatGPT API
- Level 5 - Pass the Chatbot Coding Interview
Why ChatGPT?
ChatGPT is the fastest-growing consumer software app ever, reaching over 100 million users in 2 months.
- It took TikTok 4 times as long to do the same.
- Over 13 million unique visitors use ChatGPT every day.
ChatGPT is a tool driven by artificial intelligence that provides human-like answers to any question and can even write content for you.
- Learn to build your own AI chatbots and more high-level projects in this all-levels masterclass.
AI Chatbots Are a Huge Success
OpenAI, the owner of ChatGPT is currently valued at around $29 billion.
- The value of the AI market is projected to hit $150+ billion dollars in 2023 and $1.5 trillion by 2030.
- 47% of the workforce could potentially be replaced by AI over the next few decades.
Machine learning is the future!
You must equip yourself with modern skills.
There's an AI war, and the world NEEDS machine learning specialists.
Google is launching competitor "Bard", Chinese tech giant Baidu is releasing "Ernie", and other huge companies are investing billions into AI.
- This is the perfect time to become a machine learning specialist.
Either learn how machine learning works, or GET LEFT OUT!
🎁 This bundle:
- does not assume any level of experience
- is perfect for beginners
THE COMPLETE SOURCE CODE WILL BE AVAILABLE.
Take a look at the 5-level curriculum!
Level 1 - Python for Beginners (5 Hours)
ChatGPT was primarily written in the Python programming language.
- Learn how to code in Python, the #1 language for finance, machine learning and data science.
- Anyone can become a coder.
- Program in the most hireable language of the decade!
Why learn Python?
- Simple, friendly coding language
- Powerful for projects that rely on data
- Flexible for a range of data science skills
- Easy to try online
- Large salaries
Level 2 - Data Science and Machine Learning (65 Hours)
Learn how to analyze data, visualize data, and get valuable information, insights and predictions from datasets.
Learn how to use popular Python libraries:
- NumPy - fundamental package for scientific computing in Python
- Matplotlib's Pyplot - data visualization with plots, graphs and charts
- Pandas - fast, powerful, flexible and easy to use data analysis and manipulation tool
Work with data using powerful Python libraries like Pandas, NumPy and MORE!
- Complete Beginners Data Analysis with Pandas and Python
- Learn to Graph Data with Python and Matplotlib
- Data Science with Python and NumPy
- Data Mining with Python! Real-Life Data Science Exercises
Work with R (10 Hours)
- Beginners R Programming: Data Science and Machine Learning!
- R Programming: Practical Data Science and Modeling
Learn machine learning and artificial intelligence from scratch.
- Learn how machine learning can solve problems in all disciplines.
- Learn how to build a machine learning program.
Learn to build AI models from scratch! No experience necessary.
- Machine Learning Theory for Business
- Machine Learning Fundamentals
- Introduction to Machine Learning and Python Data Science
Data Engineering and Machine Learning Masterclass
- Load, clean and encode data
- Build regression and discretizer models
- Data transformation and feature selection
Build Machine Learning Models and Neural Networks
- Image Recognition with MNIST dataset and Python
- AI Uninformed Search Algorithms
- Build regression and classification models with Python
Build Neural Networks with Python
- Linear algebra for deep learning
- Build convolutional neural networks for image classification
- Build a recurrent neural network
- Classify emotional sentiment of text
- Text to Speech with Python Machine Learning, Deep Learning and Neural Networks
Google Cloud Professional Machine Learning Engineer Certification Introduction
- Introduction to Cloud Computing for Machine Learning
- Image classification with AutoML and Vertex AI in Google Cloud
- Query and visualize data with BigQuery SQL
Microsoft Certified Azure Data Scientist Associate Preparation
- Build a cluster and pipeline in Azure Machine Learning
- Build a dataset in Microsoft Azure ML Studio
- Build a regression machine learning model with Azure Machine Learning
Build TensorFlow.js models for the web (20 Hours)
- Introduction to HTML
- Introduction to CSS
- Introduction to JavaScript, the #1 language for the web.
- Build Your First Tensors
- Visualize Data
- Train a Simple Model
- Generate and Visualize Data
- Build a Linear Regression Model
- Visualize Linear Regression with User Input
- Visualize Polynomial Regression with User Input
- Build a polynomial regression machine learning model
- K Nearest Neighbors Image Classification with Tensorflow JS
Use machine learning models and neural networks in websites with Tensorflow.js.
- Build Neural Network Components
- Build a Neural Network with Cross Validation
- Image Classification with a Neural Network
- Build a Neural Network for the XOR Algorithm
- Use Recurrent Neural Networks with Tensorflow JS
- Detect Objects in Images with a Neural Network
- Build a Deep Neural Network with Backpropagation
- Build a Neural Network with Gradient Descent
Build beginner to advanced projects!
- Identify Text Toxicity Scores
- Build a Speech Recognition Drawing Site
- Manage TensorFlow Memory
- Build a Housing Linear Regression Project
- Build a Model on a Large Dataset
- Build a Logistic Regression Model
- Visualize Fast Fourier Transform
- Visualize Principal Component Analysis
- Build a Neural Network with One Hot Encoding
- Build a Neural Network to Detect Lines in Images
- Build an LSTM Recurrent Neural Network
- Build a Model to Classify Iris Species
- Build a Neural Network to Recognize Handwriting
- Build a Positive vs Negative Text Classifier
Build models for mobile apps (10 Hours)
- Python and Android Tensor Flow Lite - Machine Learning for App Development
- CoreML SwiftUI Masterclass - Machine Learning App Development
Level 3 - Build Chatbots from Scratch
Build beginner, intermediate and advanced chatbots with natural language processing 🤖
- Build a chatbot with NLP from a Frequently Asked Questions dataset
- Build a Context Aware chatbot with a basic generative model
- Build a chatbot with machine learning
Level 4 - Master the ChatGPT API
Start by learning the fundamentals of ChatGPT prompt engineering.
- Then connect to OpenAI's models programmatically with Python.
Connect to ChatGPT with code to use its powerful technology in your software!
- OpenAI has tools to quickly implement chatbots, text completion, code completion, image generation, and more.
AI apps you can build quickly and easily with the OpenAI API:
- Q&A - answer questions
- Correct grammar
- Summarize and simplify text
- Translate text
AI for coders:
- Generate code
- Explain code
- Calculate time complexity
- Translate programming languages
- Fix bugs in code
AI for data scientists:
- Generate SQL queries
- Build structured table data from long form text
- Classify items into categories
- Generate spreadsheets and lists
AI for marketers:
- Emotion sentiment classification of text
- Extract keywords
- Turn a product description into an ad
- Generate product names
- Extract information from text
AI for copywriters:
- Convert notes to summary
- Add sarcasm to text
- Generate questions on a topic
- Emulate a text message conversation
- And much more!
Build projects with OpenAI API
- Scrape web data in text form with Python
- Process web data for OpenAI machine learning
- Customize OpenAI model to learn from your data
- Answer questions about PDF with ChatGPT model in Python
Level 5 - Pass the Chatbot Coding Interview (30 hours)
- Pass the Chatbot Coding Interview
- Essential Algorithms and Data Structures
- Python Interview Questions
- Machine Learning Interview Questions
- Essential JavaScript Software Interview Questions
- Math Interview Questions with JavaScript
- SQL Interview Questions
Dive into deep learning and master highly desirable skills.
- Add projects to your resume in no time.
- Learn a hireable skill and powerful technology
- Help businesses find customer trends, leverage data to cut costs, and much more.
All pledge tiers are STACKING. You get more and more courses with each pledge tier.
Requirements
- No programming experience needed - We'll teach you everything you need to know.
- We'll walk you through, step-by-step how to get all the software installed and set up.
Welcome to the Build a ChatGPT Clone, the only course you need to learn programming. With over 50,000 reviews, our courses are some of the HIGHEST RATED courses online!
99 days, learn to build 1 project per day, this is how you master coding.
This masterclass is without a doubt the most comprehensive course available anywhere online. Even if you have zero programming experience, this course will take you from beginner to professional. Here's why:
- The course is a taught by 7+ instructors with decades of experience in their fields.
- We've taught over 800,000 students how to code and many have gone on to become professional developers or start their own tech startup.
- You'll save $72,000, the average cost of 5 coding bootcamps. You'll learn completely online at your own pace. You'll get lifetime access to content that never expires.
- The course has been updated to be 2023 ready. You'll learn the latest tools and technologies.
We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed as a software developer.
The course includes hours of HD video tutorials and builds your programming knowledge while making real-world projects.
Sign up today, and look forward to:
- Video Lectures
- All Source Code
- Coding Challenges and Exercises
- Fully Fledged Projects
- Programming Resources
- Downloads
Testimonials
📙 Successful people are always learning.
Do you like to invest?
This course is the best self development investment you'll ever make.
📦 Sign up today, and look forward to:
- HD Video Lectures
- Easy to view on mobile
- Source files
- Fully Fledged Projects
- Resources and Downloads
Frequently Asked Questions
How do I obtain a certificate?
Each certificate in this bundle is only awarded after you complete every lecture of the course.
Many of our students post their Mammoth Interactive certifications on LinkedIn. Not only that, but you will have projects to show employers on top of the certification.
Is this an eBook or videos?
The majority of this bundle will be video tutorials (screencasts of practical projects step by step.) You will also get PDFs and ALL SOURCE FILES!
Can't I just learn via YouTube?
YouTube tutorials prioritize clickbait, shock factor, and hacking the recommendation algorithm. This makes it hard to find quality content.
Our online courses are completely about education. You'll be taken from absolute beginner to advanced programmer. With no ads, clickbait or shock factor.
This bundle is much more streamlined and efficient than learning via Google or YouTube. We have curated a massive curriculum to take you from zero to starting a high-paying career.
How will I practice to ensure I'm learning?
With each section there will be a project, so if you can build the project along with us you are succeeding. There is also a challenge at the end of each section that you can take on to add more features to the project and advance the project in your own time.
Your Instructor
John has been programming since 1997 and teaching since 2002. He has been contracted by many different companies to provide game design, audio, programming, level design and project management.
To this day John has contributed to 40 commercial games. Several of the games he has produced have risen to the Top 10 in the Apple's App Store.
His expertise is in e-learning, entrepreneurship, programming, software development, and game development. He is also a new father of two kids.
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.
Over 14 years, Mammoth Interactive has built a global student community with over 8 million courses sold. Mammoth Interactive has released over 1,000 course and 5,000 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching
since 2002. John has created top-selling applications for iOS, Xbox and
more. John also runs SaaS company Devonian Apps, building
efficiency-minded software for technology workers like you.
Course Curriculum
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Start03. Type Conversion Examples (10:04)
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Start02. Variables (19:17)
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Start04. Operators (7:04)
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Start05. Operators Examples (21:52)
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Start06. Collections (8:23)
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Start07. Lists (11:38)
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Start08. Multidimensional List Examples (8:05)
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Start09. Tuples Examples (8:34)
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Start10. Dictionaries Examples (14:24)
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Start11. Ranges Examples (8:30)
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Start12. Conditionals (6:41)
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Start13. If Statement Examples (10:16)
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Start14. If Statement Variants Examples (11:18)
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Start15. Loops (7:00)
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Start16. While Loops Examples (11:30)
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Start17. For Loops Examples (11:18)
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Start18. Functions (7:47)
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Start19. Functions Examples (9:16)
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Start20. Parameters And Return Values Examples (13:46)
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Start21. Classes And Objects (11:13)
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Start22. Classes Example (13:11)
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Start23. Objects Examples (9:54)
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Start24. Inheritance Examples (17:26)
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Start25. Static Members Example (11:03)
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Start26. Summary And Outro (4:06)
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StartSource code
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Start01. Intro to Tensorflow.mov (5:33)
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Start00. Course Intro (6:10)
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Start02. Installing Tensorflow (3:52)
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Start03. Intro to Linear Regression (9:26)
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Start04. Linear Regression Model - Creating Dataset (5:49)
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Start05. Linear Regression Model - Building the Model (7:22)
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Start06. Linear Regression Model - Creating a Loss Function (5:57)
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Start07. Linear Regression Model - Training the Model (12:42)
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Start08. Linear Regression Model - Testing the Model (5:22)
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Start09. Summary and Outro (2:55)
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StartSource Files
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Start00. Course Intro.mp4 (6:05)
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Start01. Quick Intro to Machine Learning (9:01)
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Start02. Deep Dive into Machine Learning (6:01)
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Start03. Problems Solved with Machine Learning Part 1 (13:26)
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Start04. Problems Solved with Machine Learning Part 2 (16:25)
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Start05. Types of Machine Learning (10:15)
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Start06. How Machine Learning Works (11:40)
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Start07. Common Machine Learning Structures (13:51)
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Start08. Steps to Build a Machine Learning Program (16:34)
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Start09. Summary and Outro (2:49)
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StartIntro to Machine Learning Slides
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Start00. Course Intro (5:11)
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Start01. Intro to Numpy (6:20)
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Start02. Installing Numpy (3:59)
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Start03. Creating Numpy Arrays (16:55)
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Start04. Creating Numpy Matrices (11:57)
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Start05. Getting and Setting Numpy Elements (16:59)
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Start06. Arithmetic Operations on Numpy Arrays (11:56)
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Start07. Numpy Functions Part 1 (19:13)
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Start08. Numpy Functions Part 2 (12:36)
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Start09. Summary and Outro (3:01)
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StartSource Files
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Start01. How Machines Interpret Text (15:23)
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Start00. Course Intro (6:19)
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Start02. Building the Model Part 1 - Examining Dataset (12:27)
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Start03. Building the Model Part 2 - Formatting Dataset (15:14)
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Start04. Building the Model Part 3 - Building the Model (10:30)
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Start05. Building the Model Part 4 - Training the Model (5:42)
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Start06. Building the Model Part 5 - Testing the Model.mp4 (9:26)
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Start07. Course Summary and Outro (3:29)
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StartSource Files
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Start01. Intro to Pyplot (5:11)
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Start00. Course Intro (5:30)
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Start02. Installing Matplotlib (5:51)
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Start03. Basic Line Plot (7:53)
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Start04. Customizing Graphs (10:47)
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Start05. Plotting Multiple Datasets (8:10)
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Start06. Bar Chart (6:26)
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Start07. Pie Chart (9:13)
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Start08. Histogram (10:14)
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Start09. 3D Plotting (6:28)
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Start10. Course Outro (4:09)
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StartPyplot Code
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Start00. Panda Course Introduction (5:43)
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Start01. Intro To Pandas (7:55)
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Start02. Installing Pandas (5:28)
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Start03. Creating Pandas Series (20:34)
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Start04. Date Ranges (11:29)
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Start05. Getting Elements From Series (19:21)
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Start06. Getting Properties Of Series (13:04)
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Start07. Modifying Series (19:02)
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Start08. Operations On Series (11:48)
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Start09. Creating Pandas Dataframes (22:57)
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Start10. Getting Elements From Dataframes (25:12)
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Start11. Getting Properties From Dataframes (17:44)
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Start12. Dataframe Modification (36:24)
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Start13. Dataframe Operations (20:09)
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Start14 Dataframe Comparisons And Iteration (15:35)
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Start15. Reading Csvs (12:00)
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Start16.Summary And Outro (4:14)
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StartSource Files
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Start2) 2nd Hour - Functions in R (54:57)
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Start1) 1st Hour - Course Overview and Data Setup (57:35)
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Start3) 3rd Hour - Regression Model (63:39)
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Start4) 4th Hour - Regression Models Continued and Classification Models (57:04)
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Start5) 5th Hour - Classification Models Continued, RMark Down and Excel (78:31)
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StartDatasets - Mammoth Interactive
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Start01 Project Preview (3:29)
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Start03-01 What Is Machine Learning (5:26)
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Start03-02 What Is Unsupervised Learning (8:17)
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Start04-01 Create A Dataset (5:17)
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Start04-02 Vectorize Text (16:27)
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Start04-03 Build A Word Cloud (7:08)
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Start04-04 Reduce Data Dimensionality With Principal Component Analysis (6:08)
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Start04-05 Perform Unsupervised Classification With K-Means Clusters (17:33)
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StartSource Files
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Start01-02 Types Of Machine Learning (12:09)
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Start01-01 Hash Table Or Dictionary Visualized With Time And Space Complexity (4:19)
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Start01-03 What Is Supervised Learning (9:59)
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Start01-04 What Is Unsupervised Learning (7:43)
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Start02 What Machine Learning Can And Cannot Do (11:27)
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Start03a-01 What Is Linear Regression (4:37)
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Start03a-02 What Is Logistic Regression (3:54)
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Start03a-03 Make Decisions With Decision Trees (10:31)
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Start03b-01 What Is Deep Learning (5:44)
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Start03b-02 What Is A Neural Network (7:07)
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Start04 What Are Machine Learning Libraries (11:59)
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Start00 Course Overview (13:46)
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Start03-01 Probability And Information Theory Overview (5:15)
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Start03-02 Combinatorics For Probability (8:44)
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Start03-03 Law Of Large Numbers (10:38)
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Start03-04 Calculate Center Of Distribution (7:40)
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Start04-01 Uniform Distribution (5:25)
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Start04-02 Gaussian Distribution (3:45)
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Start04-03 Log-Normal Distribution (3:28)
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Start04-04 Exponential Distribution (3:04)
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Start04-05 Laplace Distribution (1:54)
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Start04-06 Binomial Distribution (9:05)
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Start04-07 Multinomial Distribution (3:59)
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Start04-08 Poisson Distribution (4:21)
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Start05 Calculate Error Of Machine Learning Model (8:44)
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StartSource Files
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Start00-01. Intro To Python (4:37)
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Start00-00B What Is Python (4:48)
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Start00b-00 Course Overview (3:26)
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Start03-01 Load And Clean A Public Dataset (8:55)
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Start03-01B What Is One-Hot Encoding (10:02)
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Start03-02 Build X And Y Data With One Hot Encoding (4:57)
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Start03-03 Logistic Regression With One Hot Encoding (2:20)
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Start04-04 Scale And Encode Data With Scikit-Learn (3:47)
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Start04-04 What Is Scaling Data (6:36)
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Start04-05 Build, Train And Test A Machine Learning Model (4:37)
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Start05-01 Compare Decision Tree And Linear Regression Models (6:26)
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Start05-01C What Is The Kbins Discretizer (4:54)
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Start05-02 Bin Data With Kbins Discretizer (3:42)
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Start05-03 Compare Binned Regression Models (3:39)
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Start05-04 Build A Linear Regression Model On Stacked Data (3:20)
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Start05-05A What Is K Means Clustering (11:58)
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Start06-01 Build Univariate Nonlinear Transformatio (1:55)
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Start06-01 What Is Gaussian Probability Distribution- (2:31)
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Start06-01B What Is Poisson Distribution (1:08)
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Start06-02 Build X Y Data With Poisson Distribution In Numpy (3:34)
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Start06-02C What Is Logarithmic Data Transformation (2:34)
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Start06-03 Build A Ridge Regression Model (3:41)
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Start01-01 Course Overview (3:30)
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Start01-02 Build Models On The Web (5:06)
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Start02-01 What Are Search Algorithms (7:21)
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Start02-02 Depth First Search (9:00)
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Start02-02b Build A Depth First Search Algorithm (8:26)
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Start02-03 What Is Breadth First Search (bfs) (5:08)
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Start02-03b Build A Breadth First Search Algorithm (6:56)
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Start02-04 Depth Limited Search (3:58)
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Start02-05 Iterative Deepening Depth First Search (5:32)
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Start02-06 What Is Uniform Cost Search (6:04)
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Start02-06b Build A Uniform Cost Search Algorithm (8:07)
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Start02-07 Bidirectional Search (4:44)
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Start03-01 What Are Informed Search Algorithms (4:07)
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Start03-02 What Is Greedy Best-first Search (8:16)
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Start03-02b Build A Greedy Best First Search Algorithm (10:43)
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Start03-03 What Is A Search (5:10)
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Start04-01 How Does A Machine Learning Agent Learn (7:37)
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Start04-02 What Is Inductive Learning (4:10)
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Start04-03 Make Decisions With Decision Trees (10:50)
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Start04-04 Performance Of A Machine Learning Algorithm (4:13)
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Start04-05 Handle Noise In Data (5:20)
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Start04-06 Statistical Learning (3:56)
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Start05-01 What Is Logistic Regression (4:26)
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Start05-03 Prepare Data For Logistic Regression (12:19)
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Start05-03a How To Prepare Data (8:52)
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Start05-04 Build A Logistic Regression Model (5:29)
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Start05-04a How To Build A Logistic Regression Model (3:28)
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Start05-04b What Is Optimization (12:10)
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Start05-05 Optimize The Logistic Regression Model (12:44)
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Start05-05a How To Optimize A Logistic Regression Model (12:45)
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Start05-06 Train The Model (10:09)
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Start05-07 Test The Model (2:33)
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Start05-08 Visualize Results (5:38)
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Start06.01 What Is Gradient Boosting-1 (1:54)
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Start06.02 Prepare Data For Gradient Boosted Classification-2 (7:19)
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Start06.03 Build Binary Classes-3 (6:12)
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Start06.04a How To Shape Data For Classification-4 (2:58)
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Start06.04b Shape Data For Classification-5 (7:06)
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Start06.05a How To Build A Boosted Trees Classifier-6 (4:03)
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Start06.05b Build A Boosted Trees Classifier-7 (4:37)
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Start07.01 Build Input Functions-1 (3:55)
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Start07.02 Build A Boosted Trees Regressor-2 (3:02)
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Start07.03 Train And Evaluate The Model-3 (4:07)
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StartSource Files
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Start01-01 How Text To Speech Works (5:43)
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Start01-00 Course Overview - Text To Speech (1:13)
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Start01-02 What You-ll Need - Text To Speech (3:25)
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Start03 Convert Text To Speech With Gtts (5:45)
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Start04-00 What Are Pytorch, Tacotron 2 And Waveglow (4:29)
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Start04-01 Load Models (3:50)
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Start04-02 Convert Text To Speech With Pytorch (7:45)
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Start05-00 What Is Pyttsx3 (1:20)
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Start05-01 Load Available Voices (4:32)
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Start05-02 Convert Text To Speech With Pyttsx3 (4:48)
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Start00-Course Preview (4:02)
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Start02a-01 Why use the cloud for machine learning (2:38)
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Start02a-02 Benefits of cloud computing- (1:23)
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Start02a-03 Public vs private cloud computing (3:18)
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Start02a-04 Managed vs unmanaged cloud computing (1:30)
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Start02a-05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
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Start02a-06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
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Start02b-01 Build a Google Cloud project for machine learning (6:45)
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Start02b-02a What is a service account in Google Cloud Platform (1:59)
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Start02b-02b Build a service account and key in Google Cloud (6:52)
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Start02c-01 Image dataset for machine learning from Cloud Storage (2:12)
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Start02c-02 Build an image dataset for classification from a Cloud Storage bucket (5:36)
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Start02d-01 Train an AutoML image classifier machine learning model (6:27)
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Start02d-02 Deploy machine learning model to Cloud endpoint (3:38)
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Start02d-03 Make a prediction with a Cloud machine learning model (5:14)
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Start03-01 Sign in to Google Cloud (2:46)
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Start03-02 Build a BigQuery dataset in Google Cloud Console (8:24)
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Start03-03 Build a Cloud Storage bucket in Google Cloud (8:15)
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Start04-01 What is Dataflow API in Google Cloud (2:44)
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Start04-02 What is PubSub in Google Cloud (4:24)
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Start04-03 Build data streaming Dataflow Pipeline with Google Cloud API (9:39)
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Start05-01 Analyze streaming data with BigQuery Google Standard SQL (6:39)
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Start05-02 Visualize BigQuery Cloud data with Google Data Studio (3:54)
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StartSource Files
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Start00a-01 What is Microsoft Azure Machine Learning (3:24)
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Start00a Course Overview (3:09)
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Start00a-02 What is Microsoft Certified Azure Data Scientist Associate (5:10)
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Start02-01 Why use the cloud for machine learning (2:38)
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Start02-03 Public vs private cloud computing (3:18)
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Start02-04 Managed vs unmanaged cloud computing (1:30)
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Start02-05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
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Start02-06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
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Start03 What is Azure Machine Learning studio (2:17)
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Start04-01 Build an Azure Machine Learning workspace (12:51)
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Start04-02 Build a new compute cluster in Microsoft Azure ML (6:08)
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Start04-03 Build a pipeline in Microsoft Azure ML Designer (4:25)
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Start04-03a What is Azure Machine Learning designer (3:16)
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Start05-01 Build a dataset in Microsoft Azure ML Designer (3:48)
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Start05-02 Clean missing data in Microsoft Azure ML Designer (10:26)
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Start05-03 Normalize data in Microsoft Azure ML Studio (4:24)
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Start05-04 Run a data transformation pipeline in Microsoft Azure ML Designer (2:09)
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Start06-00 What is Linear Regression (5:03)
-
Start06-01 Build a model training pipeline in Microsoft Azure ML Studio (5:03)
-
Start06-02 Evaluate a machine learning model in Microsoft Azure ML (7:08)
-
StartSource Files
-
Start00-02 What Is Tensorflow Js (4:28)
-
Start00-01b What You-ll Learn (7:12)
-
Start00-03 Load Tensorflow Object (4:28)
-
Start01 What Is Machine Learning (6:39)
-
Start01b-01 Build A Scatter Plot (8:41)
-
Start01b-02 Build A Bar Chart (5:33)
-
Start01b-03 Build A Histogram (6:39)
-
Start01c-01 Build Sample Data (5:16)
-
Start01c-02 Build The Model (11:14)
-
Start01c-03 Make A Prediction (7:47)
-
Start01d-01 Generate Data (13:38)
-
Start01d-02 Visualize Data (16:10)
-
Start02-00 What Is Linear Regression (7:52)
-
Start02-01 Prepare Training Data (7:10)
-
Start02-02 Build The Model (14:05)
-
Start02-03 Make A Prediction (3:53)
-
Start02b-01 Set Up The Canvas (3:48)
-
Start02b-02 Draw A Data Sample (6:20)
-
Start02b-03 Create Loss And Prediction Functions (6:00)
-
Start02b-04 Collect User Input For Data (8:50)
-
Start02b-05 Visualize Linear Regression With Dynamic Data (6:46)
-
Start03-01 Set Up The Canvas (11:00)
-
Start03-02 Visualize Linear Regression With Dynamic Data (16:33)
-
Start04-01 Generate Samples (6:21)
-
Start04-02 Generate A Prediction Equation With Weights (6:54)
-
Start04-03 Train The Model (5:26)
-
Start04-04 Visualize Predictions (18:01)
-
Start04-05 Visualize Prediction Error (10:00)
-
Start05-01 Load Models Into Html (5:51)
-
Start05-02 Train Model On Images (13:13)
-
Start05-03 Make A Prediction (6:58)
-
StartSource Files
-
Start04-00a What Is Deep Learning (6:08)
-
Start00 What You-ll Learn (7:44)
-
Start04-00b What Is A Neural Network (8:06)
-
Start04-01 Build A Perceptron (13:26)
-
Start04-02 Build A Sigmoid Function (8:01)
-
Start04-03 Build A Sigmoid Perceptron (7:35)
-
Start04-04 Build A Relu Activation Function (7:12)
-
Start04-05 Build A Leaky Relu Activation Function (6:10)
-
Start05-01 Build Neural Network Layers (9:57)
-
Start05-02 Train And Test The Neural Network (11:24)
-
Start06-01 Build A Dataset (8:26)
-
Start06-02 Build A Neural Network (5:35)
-
Start06-03 Train The Neural Network (10:05)
-
Start06-04 Make A Prediction With The Neural Network (8:43)
-
Start07-00 What Is Cross Validation (8:24)
-
Start07-01 Load A Model Into HTML (4:57)
-
Start07-02 Use A Neural Network In Your Website (8:49)
-
Start07-03 Show Neural Network Results On Website (5:34)
-
Start08-01 Build A Dataset For XOR (6:32)
-
Start08-02 Build A Neural Network For XOR (5:19)
-
Start08-03 Train And Test The Neural Network (11:06)
-
Start09-01 Load An Rnn Into Your Website (5:37)
-
Start09-02 Set Up The Canvas (7:06)
-
Start09-03 Draw With A Neural Network (8:50)
-
Start10-01 Load An Image For Object Detection (6:13)
-
Start10-02 Load A Neural Network For Object Detection (6:15)
-
Start10-03 Outline Objects In The Image (12:17)
-
Start11-01 Build A Deep Neural Network With Gradient Descent From Scratch (9:21)
-
Start11-03 Build A Deep Neural Network With Gradient Descent With Tensorflow JS (11:24)
-
Start11-04 Build A Deep Neural Network With Backpropagation (7:03)
-
Start11-05 Build The Backpropagation (16:56)
-
Start12-01 Reduce Neural Network Error (17:12)
-
Start12-02 Build A Gradient Descent Algorithm (8:48)
-
Start13 Train The Deep Neural Network With Gradient Descent (15:24)
-
StartTensorflow JS Source Files
-
Start02-02 View Model Results Of Text Toxicity (6:40)
-
Start02-01 Load The Model With Text (4:18)
-
Start02-03 Clean Up Prediction Results (6:18)
-
Start03-01 Set Up The Speed Recognition Model (6:00)
-
Start03-02 Set Up The Canvas (3:26)
-
Start03-03 Classify Words Through Microphone (6:55)
-
Start03-04 Draw From User Commands (7:35)
-
Start03-05 Optimize The Drawing (5:53)
-
Start04-01 Tidy Tensors (6:26)
-
Start04-02 Keep Tensors (3:10)
-
Start04-03 Dispose Tensors (2:41)
-
Start04-04 Build A Memory Leak Example (4:35)
-
Start05-01 Load Json Data (7:34)
-
Start05-02 Convert Json Data To Tensor (9:08)
-
Start05-03 Visualize Dataset With Tf-Vis (5:38)
-
Start05-04 Build And Train Model (10:22)
-
Start05-05 Visualize Model-s Training Epochs (9:12)
-
Start05-06 Make A Prediction (13:49)
-
Start05-07 Visualize Prediction (9:09)
-
Start06-01 Load Dataset From Json File (6:48)
-
Start06-02 Visualize Dataset-s Features (9:26)
-
Start06-03 Build A Multi Layer Model (7:43)
-
Start06-04 Extract Inputs And Outputs (7:10)
-
Start06-05 Normalize Data (4:47)
-
Start06-06 Train The Model (6:01)
-
Start06-07 Evaluate Model Performance (6:12)
-
Start07-00 What Is Logistic Regression (4:32)
-
Start07-00B Calculate Logistic Regression Accuracy (5:20)
-
Start07-01 Build A Logistic Regression Model (7:08)
-
Start07-02 Train The Logistic Regression Model (15:20)
-
Start07-03 Visualize Logistic Regression Results (12:52)
-
Start07-04 Visualize Original Data (12:13)
-
Start07-05 Visualize Model Error (7:37)
-
Start08-00 What Is Fast Fourier Transform (2:42)
-
Start08-01 Build And Visualize A Dataset (10:48)
-
Start08-02 Visualize Frequencies With Fast Fourier Transform (11:53)
-
Start08-03 Visualize Inverse Fast Fourier Transform (5:44)
-
Start09-00 What Is Principal Component Analysis (6:13)
-
Start09-01 Build Principal Component Analysis (6:24)
-
Start09-02 Calculate Variance Of Data And Principal Component Analysis (9:28)
-
Start09-03 Visualize Data Slices (12:01)
-
Start09-04 Visualize Principal Component Analysis Results (3:03)
-
StartSource Files
-
Start02-01 Build Training Data (7:34)
-
Start02-00 What Is One Hot Encoding (6:53)
-
Start02-02 Build The Neural Network (6:48)
-
Start02-03 Train The Neural Network (9:33)
-
Start02-04 Make A Prediction (10:11)
-
Start03-01 Build Training Data To Represent Images (12:15)
-
Start03-02 Build The Convolutional Neural Network (10:39)
-
Start03-03 Train The Convolutional Neural Network (9:06)
-
Start03-04 Make A Prediction Of Number Of Lines (15:05)
-
Start04-00 What Is A Recurrent Neural Network (6:38)
-
Start04-01 Generate Sequence And Label (6:25)
-
Start04-02 Generate Dataset (6:02)
-
Start04-03 Build The Lstm Model (4:55)
-
Start04-04 Train The Model (11:25)
-
Start06-01 Process Iris Data (7:37)
-
Start06-02 Convert Data To Tensors (8:45)
-
Start06-03 Separate Training And Testing Data (8:54)
-
Start06-04 Create Training And Testing Datasets (4:42)
-
Start06-05 Build The Model (9:29)
-
Start06-06 Train The Model (4:11)
-
Start06-07 Make A Prediction (8:45)
-
Start07-01 Load Model And Dataset (5:57)
-
Start07-02 Get User Input For Sentiment Analysis (10:59)
-
Start07-03 Make A Prediction (7:11)
-
Start08-00 What Is A Convolutional Neural Network (19:29)
-
Start08-01 Set Up Canvas To Load Image Data (10:36)
-
Start08-02 Load Mnist Dataset (6:47)
-
Start08-03 Separate Training And Testing Data (5:40)
-
Start08-04 Build The Model (6:48)
-
Start08-04A What Are The Network-s Layers (14:14)
-
Start08-05 Train The Model (11:27)
-
Start08-06 Create Training Batches (6:14)
-
Start08-07 Create Testing Batches (11:31)
-
Start08-08 Fit Neural Network Through Data (8:54)
-
StartSource Files
-
Start00-01 What You-ll Need (4:29)
-
Start00-00 Course Overview (3:12)
-
Start04b Project Preview (2:17)
-
Start05-01 Build A Linear Regression Model With Python (15:06)
-
Start05-02 Convert Python Model To Tensorflow Lite (5:38)
-
Start06-03 Build A New Android Studio App (7:39)
-
Start06-04 Build App Layout (10:18)
-
Start07-05 Load Machine Learning Model (4:53)
-
Start07-06 Use Machine Learning Model (5:18)
-
Start07-07 Connect App Layout To Model (6:08)
-
Start08-00 Project Preview (1:49)
-
Start08-00 What Is Logistic Regression (4:32)
-
Start09-01 Load And Process Data For Logistic Regression With Scikit Learn (9:14)
-
Start09-02 Build A Logistic Regression Model With Python (8:01)
-
Start09-03 Convert Logistic Regression Model To Tensorflow Lite (2:38)
-
Start10-04 Build A New Android Studio App With Tf Lite Model (5:48)
-
Start10-05 Build App Layout For Logistic Regression (9:26)
-
Start11-06 Load Logistic Regression Model In Android Studio (5:01)
-
Start11-07 Use Logistic Regression Model In Android (8:46)
-
Start11-08 Enable App User Interaction With Machine Learning Model (9:54)
-
StartSource files
-
Start00-01 What You-ll Need (5:56)
-
Start00-00 Course Overview (6:54)
-
Start00-02 What Is Coreml (6:43)
-
Start01-00A What Is Sentiment Analysis (4:39)
-
Start01-00B Natural Language Framework (4:32)
-
Start01-01 Build A New Swiftui App For Sentiment Analysis (8:59)
-
Start01-01 Train A Model With CreateML (12:13)
-
Start01-02 Perform Sentiment Analysis In SwiftUI (7:38)
-
Start01-02 Test The Model With CoreML In An App (14:17)
-
Start01-03 Change Color Depending On Sentiment (4:56)
-
Start01-03 Display Prediction Accuracy (6:41)
-
Start03-01 What Is Deep Learning (6:10)
-
Start03-02 What Is A Neural Network (8:08)
-
Start04-01 Load A CoreML Model Into A New Xcode Project (11:00)
-
Start04-02 Add Images For Classification (6:31)
-
Start04-03 Enable User To Loop Through Image (5:40)
-
Start04-04 Import CoreML Model Into The View (5:28)
-
Start04-05 Resize Image For Model (6:26)
-
Start04-05A Resizing Image Overview (7:44)
-
Start04-06 Convert Image To Buffer For Model (8:55)
-
Start04-06A Image To Buffer Overview (6:55)
-
Start04-07 Test The Model On Image Classification (14:31)
-
Start05-00 Tip - How To Unhide Library Folder (1:22)
-
Start05-01 Build A New Xcode Project To Compile Model (4:44)
-
Start05-02 Build A Playground With Object Detection Model (4:28)
-
Start05-03 Instantiate A Model 05-Object (6:12)
-
Start05-04 Build An Image Analysis Request (7:23)
-
Start05-05 Resize Image For Model (9:36)
-
Start05-06 Convert Image To Buffer For Model (9:47)
-
Start05-07 Test Object Detection On Image (4:53)
-
Start01 Projects Preview (4:49)
-
Start01 What Is Natural Language Processing (5:39)
-
Start02 What Is Text Vectorization (7:34)
-
Start03-01 Train A Vectorizer (8:50)
-
Start03-02 Chat With The User (11:04)
-
Start04-01 Define A Basic Intent Classifier (7:42)
-
Start04-02 Define A Basic Generative Model (4:05)
-
Start04-03 Test The Chatbot (9:33)
-
StartSource Files
-
Start01-01 Build patterns and responses training data (6:34)
-
Start01-02 Tokenize chat data for training (4:30)
-
Start02-01 Clean chat data for machine learning (3:04)
-
Start02-02 Build bag of words for ML model (4:24)
-
Start02-03 Split data for machine learning (3:34)
-
Start03-01 Build a TensorFlow machine learning model for chat (4:54)
-
Start03-02 Test chatbot machine learning model (9:09)
-
Start03-03 Categorize chat question with ML (7:25)
-
Start03-04 Pick a chatbot response in top category (8:18)
-
StartSource Files
-
Start00-02 Transformer Project Overview (7:59)
-
Start00-01 Introduction to Transformer Neural Networks (4:31)
-
Start01-01 Connect To Google Drive Dataset In Colab (3:48)
-
Start01-02 Read Text Files In Python (9:17)
-
Start01-03 Read Movie Conversation Text File In Python (10:58)
-
Start01-04 Clean Text Data For NLP (6:09)
-
Start01-05 Remove Contractions From Text Data With Python (9:35)
-
Start01-06 Preprocess Text Data For Transformer Chatbot Ml (6:16)
-
Start02-01 Build Tokenizer With Tfds (7:31)
-
Start02-02 Add Padding To Tokenized Sentences With Python (3:06)
-
Start02-03 Build Tensorflow Dataset For ML (3:13)
-
Start03-01 Calculate Scaled Dot Product Attention (4:56)
-
Start03-02 Set Up Multi Head Attention Layer In Python Nn (5:22)
-
Start03-03 Split Attention Layer Into Multiple Heads (4:08)
-
Start03-04 Add Scaled Dot Product Attention And Final Layer (5:22)
-
Start04-01 Mask Padding Tokens With Python (4:38)
-
Start04-02 Build Lookahead Mask For Future Tokens (3:53)
-
Start05-01 Set Up Positional Encoding Layer In Neural Network (3:03)
-
Start05-02 Build Positional Encoding Layer With Tensorflow Keras (5:31)
-
Start06-01 Build Input Encoder For Neural Network (5:28)
-
Start06-02 Combine Input And Positional Encoding (5:36)
-
Start07-01 Set Up Decoder Layer With Python (6:31)
-
Start07-02 Combine Output And Positional Encoding For Decoder (5:28)
-
Start08-01 Combine encoding and decoding in NN (7:08)
-
Start08-02 Build custom ML model learning rate (3:37)
-
Start08-03 Build custom model loss function (3:17)
-
Start08-04 Compile neural network with Python (4:30)
-
Start08-04b Zero out padding tokens in attention (1:44)
-
Start08-05 Limit and pad tokenized sentences (5:30)
-
Start09-01 Handle new chatbot question input (5:13)
-
Start09-02 Decode tokens into words (2:30)
-
StartSource files
-
Start01 01 What Is Chatgpt (7:50)
-
Start00-01 Introduction Of The Instructor (2:25)
-
Start01 02 Intro To Prompt Engineering-Prompt Types (8:28)
-
Start01 03 Intro To Prompt Engineering-Effective Prompts (8:41)
-
Start01B 01 Project Preview (2:04)
-
Start01B 02A Simplify Complex Information (8:38)
-
Start01B 02B Simplify Complex Information-Other Strategies (8:41)
-
Start02 03 Proofread-Email And Business Proposals (8:39)
-
Start02.03 Proofread-More Use Cases (8:24)
-
Start02.04 Re-Organize Data-Benefits And First Sample Use Case (6:22)
-
Start02.04 Re-Organize Data-Potential Use Cases Case (10:44)
-
Start02.05 Work With Spreadsheets-Automating Data Entry (7:46)
-
Start02.05 Work With Spreadsheets-Formulas And Other Use Cases (7:31)
-
Start03 01 Project Preview (1:23)
-
Start03.02 Create Content (4:03)
-
Start03.03 Social Media (4:26)
-
Start03.04 Write Ad Copy (8:17)
-
Start03.05 Write Email Marketing Campaigns (4:55)
-
Start03.06 Write An Outreach Message (5:08)
-
Start03.07 Copyrighting (4:29)
-
Start03.08 Seo (5:09)
-
Start03.09 Video Scripts (8:49)
-
Start03.10 Generate Text In Your Writing Style (3:25)
-
Start04 01 Project Preview (1:51)
-
Start04.02 Research-Chatgpt Usecase And Benefits (7:05)
-
Start04.02 Research-More Examples And Explanation (7:49)
-
Start04.03 Write An Article-Add Role To Chatgpt (7:17)
-
Start04.03 Write An Article-Generate High Quality Content (8:02)
-
Start04.04 Check Plagiarism (10:56)
-
Start04.05 Prepare For Job Opportunities-Cv And Cover Letter (8:28)
-
Start04.05 Prepare For Job Opportunities-Interview Questions, Connection And Task Generator (8:36)
-
Start05 01 Project Preview (2:33)
-
Start05.02 Generate Code-Javascript And Python Code Snippets (9:26)
-
Start05.02 Generate Code-Stylesheet, Html, C++ And Conversion (9:20)
-
Start05.03 Build Algorithms-Algorithm To Pseudocode (4:03)
-
Start05.03 Build Algorithms-Realworld Use Cases (8:11)
-
Start05.04 Debug-Python Use Case (6:51)
-
Start05.04 Debug-React, Api, Javascript, Html And Css (6:56)
-
Start05.05 Write Code Documentation (9:51)
-
Start05.06 Use Chatgpt As A Linux Terminal (8:32)
-
Start05.07 Use Chatgpt As A Unix Terminal (9:08)
-
Start05.08 Use Chatgpt As A Microsoft Dos Terminal (5:28)
-
Start05.09 Use Chatgpt To Suggest Uxui Designs (8:10)
-
Start05.10 Use Chatgpt To Suggest Cybersecurity Solutions (10:05)
-
StartSource Files
-
Start01.01 Setting Up Your Chatgpt Account - A Step-By-Step Guide (6:04)
-
Start00 01 Introduction Of The Instructor (1:53)
-
Start01.02 Tips For Getting The Best Responses From Chatgpt (9:55)
-
Start02 01 Building A Marketing Campaign Content Calendar With Chatgpt (10:34)
-
Start03 01 The Importance Of Identifying Your Target Audience_1 (3:08)
-
Start03.02 Using Chatgpt For Target Audience Research And Assessment (11:54)
-
Start04 01 Project Preview (1:21)
-
Start04.02 Exploring Social Media Marketing And Automation (5:59)
-
Start04.03 Generating Social Media Posts (10:38)
-
Start04.04 Social Media Automation Tool - Socialbee - -Bonus- (6:40)
-
Start04.05 Automating Social Media Post Scheduling - -Bonus- (8:26)
-
Start04.06 Automating Social Media Reposting - -Bonus- (8:28)
-
Start04.07 Configuring Your Social Media Automation Timetable – -Bonus- (4:00)
-
Start05 01 Project Preview (1:11)
-
Start05.02 Generate Optimized Keywords And Blog Headlines (7:39)
-
Start05.03 Building An Seo-Enhanced Blog Post Quickly (10:07)
-
Start06 01 Introduction To Email Marketing And Its Significance_1 (3:37)
-
Start06.02 Building Effective Email Sequences (7:27)
-
Start07 01 Crafting Sales Page Copy (8:05)
-
Start08 01 Project Preview (1:07)
-
Start08.02 Producing Facebook Ads (10:00)
-
Start08.03 Generating Google Ads (9:44)
-
Start08.04 Generate Ads For Instagram And Twitter (7:36)
-
Start09 01 Project Preview (1:09)
-
Start09.02 Generating Unlimited Video Concepts (10:33)
-
Start09.03 Crafting A Full Youtube Video Script (10:00)
-
Start09.04 Youtube Seo Strategies (8:54)
-
Start10 01 Project Preview (1:26)
-
Start10.02 Guides To Building Effective Marketing Funnels (4:01)
-
Start10.03 Defining Your Buyer Persona (9:38)
-
Start10.04 Generating A Lead Magnet (8:57)
-
Start10.05 Building Landing Page And Social Media Copy (9:02)
-
Start10.06 Composing A Comprehensive Email Sequence For Your Funnel (4:49)
-
Start11 01 Review Analysis And Optimization Of Products And Services (7:24)
-
Start12 01 Project Preview (1:57)
-
Start12.02 Homepage, About Us, And Contact Us Page Copy (10:14)
-
Start12.03 Generate Meta Title And Descriptions (4:58)
-
Start12.04 Website Development With Chatgpt Crash Course (25:32)
-
Start13 01 Project Preview (1:10)
-
Start13.02 Creating Product And Business Names (7:52)
-
Start13.03 Developing Professional Taglines And Slogans For Your Brand (7:32)
-
Start13.04 Writing Product Descriptions For Your Online Store (4:46)
-
Start13.05 Building Faq’S For Services Or Products (4:54)
-
Start14 01 Conclusion (2:08)
-
StartBonus - Tips And Tricks (7:43)
-
StartSource file
-
Start00b-02 How Openai Api Works (2:09)
-
Start00b-01 Openai Api Models To Work With (2:53)
-
Start00b-03 Adjust Openai Api Model Parameters (7:58)
-
Start01-01 Use Openai Api To Answer Questions Like Chatgtp (10:19)
-
Start01-02 Correct Grammar With Openai Api (3:30)
-
Start01-03 Summarize And Simplify Text With Openai Api (4:03)
-
Start01-04 Translate Text With Openai Api (3:04)
-
Start02-01 Generate Code With Openai Api (7:11)
-
Start02-02 Explain Code With Openai Api (5:24)
-
Start02-03 Calculate Time Complexity With Openai Api (3:40)
-
Start02-04 Translate Programming Languages With OpenAI API (4:24)
-
Start02-05 Fix Bugs In Code With Openai Api (3:19)
-
Start03-01 Generate Sql Queries With Openai Py (5:15)
-
Start03-02 Build Structured Table Data From Long Form Text (4:29)
-
Start03-03 Classify Items Into Categories With Openai Api (4:50)
-
Start03-04 Generate Spreadsheets And Lists With Chatgpt Openai Api (5:46)
-
Start04-01 Convert Notes To Summary With Openai Api (5:40)
-
Start04-02 Add Emotional Sentiment To Text With Openai Models (9:40)
-
Start04-03 Generate Questions On A Topic With Gpt Turbo (9:26)
-
Start04-04 Generate Text Conversation With Chatgpt Api (5:19)
-
Start05-01 Classify Text Emotion Sentiment With Chatgpt Models (5:09)
-
Start05-02 Extract Keywords From Text With Chatgpt Api (4:31)
-
Start05-03 Convert Product Description To Ad With Chatgpt Python (3:57)
-
Start05-04 Generate Product Names With Chatgpt In Python (4:04)
-
Start05-05 Extract Information From Text With Chatgpt Api (2:57)
-
Start06-01 Build Html Parser With Python (4:31)
-
Start06-02 Scrape Hyperlinks From Url Webpage With Python (4:09)
-
Start06-03 Filter Out Urls Not Part Of Domain (7:03)
-
Start06-04 Save Web Content To Files With Python (10:07)
-
Start07-01 Convert Text To Csv With Python (6:36)
-
Start07-02 Remove Whitespace And Lines From Text With Python (4:58)
-
Start07-03 Tokenize Text With Python For Machine Learning Models (2:50)
-
Start07-04 Split Long Lines With Python (4:11)
-
Start07-05 Split Pandas Dataframe Into Sections With Python (7:19)
-
Start07-06 Embed Text For Machine Learning With Openai Api (8:05)
-
Start08-01 Embed Question With Python (5:48)
-
Start08-02 Answer Questions About Your Data With Customized Openai Model (10:36)
-
Start09-01 Load And Read Pdf In Python (3:40)
-
Start09-02 Build Vector Index From Pdf Text In Python (4:32)
-
Start09-03 Answer Questions About Pdf With Chatgpt Model In Python (5:10)
-
Start10-01 Generate Review Data With Chatgpt Api (8:14)
-
Start10-02 Format Python Text To Multidimensional Pandas Dataframe (11:50)
-
Start10-03 Change Column Data Type In Pandas Dataframe (2:40)
-
Start10-04 Embed Text Data With Openai Api (6:25)
-
StartSource files
-
Start01 01 What is Tokenization (1:18)
-
Start01 02 What text can cause tokenization problems (3:42)
-
Start01 03 What is sentence segmentation (1:40)
-
Start02 01 What is Stemming in NLP (3:40)
-
Start02 02 What are issues with stemming in NLP (1:03)
-
Start02 03 What is Lemmatization in NLP (2:20)
-
Start03 01 What is text normalization in NLP (1:28)
-
Start03 02 What is named entity recognition in NLP (3:27)
-
Start03 03 What is relation recognition in NLP (2:10)
-
Start03 04 What is a parser in NLP (1:51)
-
Start04 01 What is Term Frequency-Inverse Document Frequency in NLP (4:12)
-
Start04 02 What is TF-IDF vectorization in NLP (1:21)
-
Start04 03 What are issues with TF-IDF in NLP (3:46)
-
Start05 01 What is a Bag of Words in NLP (3:52)
-
Start05 02 Give an example of Bag of Words in NLP (3:10)
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Start05 03 What are issues with the Bag of Words NLP approach- (2:54)
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Start06 01 What is Sequence Classification in NLP (1:55)
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Start06 02 What are Hidden Markov Models in NLP (1:55)
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Start07 01 What are regular expressions in programming (2:00)
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Start07 02 Regular Expression Operators in Code (9:11)
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Start07 03 Common Regular Expression Symbols Overview (7:43)
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StartComplete Course Source Files
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Start00-02 Fizzbuzz Kotlin (5:26)
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Start00-01. Kotlin Course Introduction (7:04)
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Start01-01 Reverse Words In A String Kotlin (3:53)
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Start01-02 Rotate Array Kotlin (7:31)
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Start01-03 Kth Largest Element In An Array Kotlin (4:26)
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Start02-01 Set Matrix Zeros Kotlin (12:20)
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Start02-02 Spiral Matrix Kotlin (21:56)
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Start03 Queue With A Linkedlist Kotlin (10:43)
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Start04-00 Build A Binary Tree (15:46)
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Start04-01 Delete Tree Node Kotlin (17:20)
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Start05-01 Delete Tree Node Kotlin (17:20)
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Start05-02 Selection Sort Algorithm Kotlin (6:01)
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Start05-03 Insertion Sort Kotlin (6:15)
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Start05-04 Merge Sort Algorithm Kotlin (15:10)
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Start06 Build A Graph Kotlin (7:28)
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Start07-01 Coin Change Kotlin (8:02)
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Start07-02 Maximum Sum Subarray Kotlin (7:06)
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Start07-03 Edit Distance Kotlin (9:37)
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Start08-01 Single Number Kotlin (7:29)
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Start08-02 Number Of 1 Bits Kotlin (7:24)
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Start08-03 Bitwise And Of A Range Kotlin (7:23)
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Start09-01 Permutations Kotlin (16:12)
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Start09-02 Combinations Kotlin (9:28)
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Start09-03 Letter Combinations Of A Phone Number Kotlin (10:31)
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Start10-01 Reverse Integer Kotlin (11:52)
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Start10-02 Palindrome Number Kotlin (9:53)
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Start10-03 Excel Sheet Column Number Kotlin (5:23)
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StartSource Code
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Start01-00. Intro (1:54)
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Start00. Course Intro (5:09)
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Start01-01. What is Machine Learning (17:47)
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Start01-02. Types Of Machine Learning (10:48)
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Start01-03. Building A Machine Learning Model (17:02)
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Start02-00. Intro (2:44)
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Start02-01. How To Choose An Algorithm (16:42)
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Start02-02. Common Machine Learning Algorithms Part 1 (15:58)
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Start02-03. Common Machine Learning Algorithms Part 2 (22:52)
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Start02-04. Common Machine Learning Algorithms Part 3 (13:03)
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Start02-05. Comparison Interview Questions (16:20)
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Start03-00. Intro (2:08)
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Start03-01. Data Related Errors (16:55)
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Start03-02. Model Related Errors (11:34)
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Start03-03. Results Testing Techniques (11:18)
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Start04-00. Intro (2:14)
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Start04-01. Missing_Corrupted Data (5:08)
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Start04-02. Selecting Important Variables (3:18)
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Start04-03. Fixing Multicollinearity- (3:56)
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Start04-04. Kernel Tick (3:21)
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Start04-05. Slow Machine_Limited Memory (4:59)
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Start04-06. Classification and Random Sampling (3:38)
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Start04-07. Low Training Error with High Validation Error (4:40)
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Start04-08. Cross Validation on Time Series Data (3:38)
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Start04-09. Amazon Recommendation System (5:26)
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Start05. Course Summary and Outro (3:12)
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StartMachine Learning Interview Questions
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Start01-01 Happy Number (15:33)
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Start00 Introduction (4:08)
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Start01-02 Trailing Zeros In Factorial (11:10)
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Start01-03 Count Primes (6:57)
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Start01-04 Sorted Permutation Rank With Repeats (10:23)
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Start01-05 Sqrt(x) (18:19)
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Start01-06 Greatest Common Divisor (7:27)
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Start02-01 Integer To Roman (17:16)
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Start02-02 Roman To Integer (12:04)
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Start02-03 Rearrange Array (9:11)
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Start03-01 N-th Tribonacci Number (14:00)
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Start03-02 Maximum Product Of Three Numbers (10:58)
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Start03-03 Grid Unique Paths (17:22)
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Start03-04 City Tour (5:51)
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Start04-01 Next Greater Element (15:28)
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Start04-02 Ugly Number (9:02)
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Start04-03 Power Of Two Integers (11:28)
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Start04-04 Prime Sum (12:23)
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Start04-05 Sum Of Bit Differences Among All Pairs (20:53)
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StartSource Files
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Start01-00 Build A Table And Display All Records (10:26)
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Start00 Course Overview (4:21)
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Start01-01 List Alphabetically (4:13)
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Start01-02 List All Not In Specified Range (4:03)
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Start01-03 List All In Range By Multiple Properties (7:39)
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Start01-04 Display All With Characters In Property (6:10)
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Start01-05 Select From Where In This And Not In That (6:09)
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Start01-06 List All With Exact Number Of Characters (5:44)
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Start01-07 List All With Specific Second Character (5:11)
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Start01-08 List All Distinct Ids Available (3:36)
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Start01-09 Calculate Percentage (4:45)
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Start01-10 Consecutive Numbers (9:03)
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Start02-01 List Streams With Viewer Information (9:33)
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Start02-02 Join Two Tables (5:19)
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Start02-03 Find Same Value In Another Property (8:08)
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Start02-04 List Values Across Tables (6:35)
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Start02-05 Join Three Tables (6:44)
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Start02-06 Find Average Across Tables (6:59)
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Start02-07 Display Difference Across Tables (6:27)
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Start02-08 Find More Than Value Across Tables (6:11)
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Start02-09 Product Sales Analysis (6:50)
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Start02-10 List All After A Certain Date (6:33)
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Start02-11 Capital Gain Loss (6:33)
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Start03-01 Count Distinct Properties (3:47)
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Start03-02 List Total Sum (3:40)
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Start03-03 Get Minimum (3:12)
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Start03-04 Get Maximum From A Distinct Type (4:12)
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Start03-05 Get Average And Count At An ID (3:22)
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Start03-06 Get Number Of Same Type (3:05)
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Start03-07 Get Difference Between Max And Min (2:30)
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Start03-08 Find Minimum Of Each Type (5:13)
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Start03-09 Get Sum At Each ID (3:39)
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Start03-10 Get Average For Each ID (4:40)
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Start03-11 Get Different Properties (4:29)
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Start03-12 List Where Maximum Is Greater Than (4:34)
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Start03-13 List Average Where Greater Than (6:16)
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Start04-01 Find Higher Values (8:03)
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Start04-02 Find All Of A Type (6:40)
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Start04-03 List All Above Average (5:10)
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Start04-04 List All Greater Than Minimum (7:52)
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Start04-05 List All Part Of Any (7:57)
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Start04-06 List All Who Have More (6:24)
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Start04-07 List All With Same As Minimum (4:41)
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Start04-08 List All Above Average In Their Group (6:35)
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Start04-09 Get Kth Maximum Value (6:48)
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Start04-10 Get Kth Minimum Value (5:27)
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Start04-11 Select Last K Records From A Table (5:01)
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Start04-12 List All Not In A Group (5:12)
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Start04-13 Get K Maximum Values (4:58)
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Start04-14 Get K Minimum Values (3:48)
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Start00A Course Overview (1:18)
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Start01-01A Question - Find Maximum Sum Subarray Of Size K (2:00)
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Start01-01B Find Maximum Sum Subarray Of Size K (5:18)
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Start01-01C Explanation - Find Maximum Sum Subarray Of Size K (4:00)
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Start01-02A Question - Find Smallest Subarray With Given Sum (2:06)
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Start01-02B Find Smallest Subarray With Given Sum (5:50)
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Start01-02C Explanation - Find Smallest Subarray With Given Sum (3:41)
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Start02-01A Question - Find Pair With Target Sum (1:17)
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Start02-01B Find Pair With Target Sum (5:29)
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Start02-01C Explanation - Find Pair With Target Sum (2:39)
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Start02-02A Question - Remove Duplicates From List (1:26)
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Start02-02B Remove Duplicates From List (3:30)
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Start02-02C Explanation - Remove Duplicates From List (3:37)
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Start02-03A Question - Remove Targets From Array (1:17)
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Start02-03B Remove Targets From Array (3:50)
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Start02-03C Explanation - Remove Targets From Array (2:53)
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Start02-04A Question - Square A Sorted Array (1:40)
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Start02-04B Square A Sorted Array (4:55)
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Start02-04C Explanation - Square A Sorted Array (5:11)
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Start03A Course Overview (1:24)
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Start03C-01A Question - Cyclic Sort (1:36)
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Start03C-01B Cyclic Sort (4:19)
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Start03C-01C Explanation - Cyclic Sort (5:00)
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Start03C-02A Question - Find Duplicate Number (1:06)
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Start03C-02B Find Duplicate Number (3:32)
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Start03C-02C Explanation - Find Duplicate Number (2:49)
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Start03C-03A Question - Find Duplicate Without Modifying Array (1:45)
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Start03C-03B Find Duplicate Without Modifying Array (4:43)
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Start03C-03C Explanation - Find Duplicate Without Modifying Array (4:50)
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Start03C-04A Question - Find All Duplicate Numbers (0:59)
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Start03C-04B Find All Duplicate Numbers (3:47)
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Start03C-04C Explanation - Find All Duplicate Numbers (6:51)
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Start03C-05A Question - Find Missing Number (1:07)
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Start03C-05B Find Missing Number (4:23)
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Start03C-05C Explanation - Find Missing Number (9:18)
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Start03C-06A Question - Find All Missing Numbers (1:12)
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Start03C-06B Find All Missing Numbers (3:32)
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Start03C-06C Explanation - Find All Missing Numbers (7:46)
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Start03C-07A Question - Find Corrupt Pair (1:14)
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Start03C-07B Find Corrupt Pair (4:00)
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Start03C-07C Explanation - Find Corrupt Pair (4:52)
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Start06A Course Overview (1:24)
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Start06C-01A Question - Traverse Binary Tree Level Order (2:34)
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Start06C-01B Traverse Binary Tree Level Order (6:49)
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Start06C-01C Explanation - Traverse Binary Tree Level Order (4:54)
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Start06C-02A Question - Traverse Binary Tree Reverse Level Order (1:58)
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Start06C-02B Traverse Binary Tree Reverse Level Order (7:01)
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Start06C-02C Explanation - Traverse Binary Tree Reverse Level Order (4:20)
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Start06C-03A Question - Find Level Averages In Binary Tree (1:16)
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Start06C-03B Find Level Averages In Binary Tree (7:30)
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Start06C-04A Question - Find Level Order Successor (1:53)
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Start06C-04B Find Level Order Successor (8:11)
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Start06C-05A Question - Find Minimum Depth Of Binary Tree (1:33)
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Start06C-05B Find Minimum Depth Of Binary Tree (5:29)
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Start06C-06A Question - Find Maximum Depth Of Binary Tree Traversing All Levels (0:59)
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Start06C-06B Find Maximum Depth Of Binary Tree Traversing All Levels (5:33)
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Start06C-07A Question - Find Right View Of Binary Tree (1:15)
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Start06C-07B Find Right View Of Binary Tree (6:27)
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Start04A Course Overview (1:14)
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Start04C-01A Question - Find Top K Numbers (0:47)
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Start04C-01B Find Top K Numbers (4:03)
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Start04C-01C Explanation - Find Top K Numbers (2:44)
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Start04C-02A Question - Find Kth Smallest Number (1:03)
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Start04C-02B Find Kth Smallest Number (4:32)
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Start04C-02C Explanation - Find Kth Smallest Number (2:16)
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Start04C-02C Explanation - Find Kth Smallest Number_1 (2:16)
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Start04C-03A Question - K Closest Points To Origin (1:03)
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Start04C-03B K Closest Points To Origin (7:59)
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Start04C-03C Explanation - K Closest Points To Origin (2:01)
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Start04C-04A Question - Find Minimum Cost To Connect Ropes (2:00)
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Start04C-04B Find Minimum Cost To Connect Ropes (4:42)
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Start04C-04C Explanation - Find Minimum Cost To Connect Ropes (2:11)
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Start08-01A Question - Find Bitonic Array Maximum (1:50)
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Start08-01B Find Bitonic Array Maximum (4:34)
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Start08-02A Question - Order Agnostic Binary Search (1:27)
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Start08-02B Order Agnostic Binary Search (5:47)
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Start05A Course Overview (1:14)
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Start06-01A Question - Is Linked List Cycle (1:15)
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Start06-01B Is Linked List Cycle (5:01)
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Start06-01C Explanation - Is Linked List Cycle (2:41)
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Start06-02A Question - Find Length Of Linked List Cycle (1:17)
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Start06-02B Find Length Of Linked List Cycle (6:25)
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Start06-02C Explanation - Find Length Of Linked List Cycle (3:43)
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Start06-03A Question - Find Middle Node Of Linked List (1:08)
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Start06-03B Find Middle Node Of Linked List (4:25)
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Start06-03C Explanation - Find Middle Node Of Linked List (1:36)
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Start07-01A Question - Find Distinct Subsets (1:19)
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Start07-01B Find Distinct Subsets (5:48)
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Start07-02A Question - Find Subsets With Duplicates (2:02)
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Start07-02B Find Subsets With Duplicates (4:06)
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StartSource Files
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Start01 Types Of Time Complexity (13:27)
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Start02 Types Of Better Time Complexity (18:15)
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Start03 Bubble Sort Algorithm (6:41)
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Start04 Selection Sort Algorithm (6:15)
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Start05 Insertion Sort Algorithm (6:51)
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Start06 Quicksort Algorithm (9:18)
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Start07 Merge Sort Algorithm (8:43)
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Start08 Time Complexity Of Different Sorting Algorithms (2:55)
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StartSource Code