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Learn to Build a ChatGPT Clone, Machine Learning Bundle+
LEVEL 0 - Intro
001 Build ChatGPT Clone In Minutes With OpenAI API (8:54)
02 Format ChatGPT API Response On Multiple Lines (7:55)
Source files
✅ LEVEL 1 - Python for Beginners
00. Introduction (4:42)
Code Python on the Web
02.02 What If I Get Errors (2:40)
02.01 What is Google Colab (4:24)
02.03 How Do I Terminate a Session (2:40)
Python Language Fundamentals
03. Type Conversion Examples (10:04)
02. Variables (19:17)
04. Operators (7:04)
05. Operators Examples (21:52)
06. Collections (8:23)
07. Lists (11:38)
08. Multidimensional List Examples (8:05)
09. Tuples Examples (8:34)
10. Dictionaries Examples (14:24)
11. Ranges Examples (8:30)
12. Conditionals (6:41)
13. If Statement Examples (10:16)
14. If Statement Variants Examples (11:18)
15. Loops (7:00)
16. While Loops Examples (11:30)
17. For Loops Examples (11:18)
18. Functions (7:47)
19. Functions Examples (9:16)
20. Parameters And Return Values Examples (13:46)
21. Classes And Objects (11:13)
22. Classes Example (13:11)
23. Objects Examples (9:54)
24. Inheritance Examples (17:26)
25. Static Members Example (11:03)
26. Summary And Outro (4:06)
Source code
✅ LEVEL 2 - Data Science and Machine Learning
01. Intro to Tensorflow.mov (5:33)
00. Course Intro (6:10)
02. Installing Tensorflow (3:52)
03. Intro to Linear Regression (9:26)
04. Linear Regression Model - Creating Dataset (5:49)
05. Linear Regression Model - Building the Model (7:22)
06. Linear Regression Model - Creating a Loss Function (5:57)
07. Linear Regression Model - Training the Model (12:42)
08. Linear Regression Model - Testing the Model (5:22)
09. Summary and Outro (2:55)
Source Files
Machine Learning theory
00. Course Intro.mp4 (6:05)
01. Quick Intro to Machine Learning (9:01)
02. Deep Dive into Machine Learning (6:01)
03. Problems Solved with Machine Learning Part 1 (13:26)
04. Problems Solved with Machine Learning Part 2 (16:25)
05. Types of Machine Learning (10:15)
06. How Machine Learning Works (11:40)
07. Common Machine Learning Structures (13:51)
08. Steps to Build a Machine Learning Program (16:34)
09. Summary and Outro (2:49)
Intro to Machine Learning Slides
NumPy
00. Course Intro (5:11)
01. Intro to Numpy (6:20)
02. Installing Numpy (3:59)
03. Creating Numpy Arrays (16:55)
04. Creating Numpy Matrices (11:57)
05. Getting and Setting Numpy Elements (16:59)
06. Arithmetic Operations on Numpy Arrays (11:56)
07. Numpy Functions Part 1 (19:13)
08. Numpy Functions Part 2 (12:36)
09. Summary and Outro (3:01)
Source Files
Review Sentiment Analysis
01. How Machines Interpret Text (15:23)
00. Course Intro (6:19)
02. Building the Model Part 1 - Examining Dataset (12:27)
03. Building the Model Part 2 - Formatting Dataset (15:14)
04. Building the Model Part 3 - Building the Model (10:30)
05. Building the Model Part 4 - Training the Model (5:42)
06. Building the Model Part 5 - Testing the Model.mp4 (9:26)
07. Course Summary and Outro (3:29)
Source Files
Learn to Graph Data with Python and Matplotlib
01. Intro to Pyplot (5:11)
00. Course Intro (5:30)
02. Installing Matplotlib (5:51)
03. Basic Line Plot (7:53)
04. Customizing Graphs (10:47)
05. Plotting Multiple Datasets (8:10)
06. Bar Chart (6:26)
07. Pie Chart (9:13)
08. Histogram (10:14)
09. 3D Plotting (6:28)
10. Course Outro (4:09)
Pyplot Code
Complete Beginners Data Analysis with Pandas and Python
00. Panda Course Introduction (5:43)
01. Intro To Pandas (7:55)
02. Installing Pandas (5:28)
03. Creating Pandas Series (20:34)
04. Date Ranges (11:29)
05. Getting Elements From Series (19:21)
06. Getting Properties Of Series (13:04)
07. Modifying Series (19:02)
08. Operations On Series (11:48)
09. Creating Pandas Dataframes (22:57)
10. Getting Elements From Dataframes (25:12)
11. Getting Properties From Dataframes (17:44)
12. Dataframe Modification (36:24)
13. Dataframe Operations (20:09)
14 Dataframe Comparisons And Iteration (15:35)
15. Reading Csvs (12:00)
16.Summary And Outro (4:14)
Source Files
Beginners R Programming: Data Science and Machine Learning
2nd Hour- Control Flow and Core Concepts (64:28)
1st Hour - Intro to R (51:17)
3rd Hour Matrices, Dataframes, Lists and Data ManipulationB (77:00)
4th Hour - GGplot and Intro to Machine learning (68:55)
5th Hour - Conclusion (47:25)
Source Code
R Programming: Practical Data Science and Modeling
2) 2nd Hour - Functions in R (54:57)
1) 1st Hour - Course Overview and Data Setup (57:35)
3) 3rd Hour - Regression Model (63:39)
4) 4th Hour - Regression Models Continued and Classification Models (57:04)
5) 5th Hour - Classification Models Continued, RMark Down and Excel (78:31)
Datasets - Mammoth Interactive
Beginner Data Science and Machine Learning Bootcamp
01 Project Preview (3:29)
03-01 What Is Machine Learning (5:26)
03-02 What Is Unsupervised Learning (8:17)
04-01 Create A Dataset (5:17)
04-02 Vectorize Text (16:27)
04-03 Build A Word Cloud (7:08)
04-04 Reduce Data Dimensionality With Principal Component Analysis (6:08)
04-05 Perform Unsupervised Classification With K-Means Clusters (17:33)
Source Files
Machine Learning Theory for Business
01-02 Types Of Machine Learning (12:09)
01-01 Hash Table Or Dictionary Visualized With Time And Space Complexity (4:19)
01-03 What Is Supervised Learning (9:59)
01-04 What Is Unsupervised Learning (7:43)
02 What Machine Learning Can And Cannot Do (11:27)
03a-01 What Is Linear Regression (4:37)
03a-02 What Is Logistic Regression (3:54)
03a-03 Make Decisions With Decision Trees (10:31)
03b-01 What Is Deep Learning (5:44)
03b-02 What Is A Neural Network (7:07)
04 What Are Machine Learning Libraries (11:59)
Machine Learning Fundamentals
00 Course Overview (13:46)
03-01 Probability And Information Theory Overview (5:15)
03-02 Combinatorics For Probability (8:44)
03-03 Law Of Large Numbers (10:38)
03-04 Calculate Center Of Distribution (7:40)
04-01 Uniform Distribution (5:25)
04-02 Gaussian Distribution (3:45)
04-03 Log-Normal Distribution (3:28)
04-04 Exponential Distribution (3:04)
04-05 Laplace Distribution (1:54)
04-06 Binomial Distribution (9:05)
04-07 Multinomial Distribution (3:59)
04-08 Poisson Distribution (4:21)
05 Calculate Error Of Machine Learning Model (8:44)
Source Files
Data Engineering and Machine Learning Masterclass
00-01. Intro To Python (4:37)
00-00B What Is Python (4:48)
00b-00 Course Overview (3:26)
03-01 Load And Clean A Public Dataset (8:55)
03-01B What Is One-Hot Encoding (10:02)
03-02 Build X And Y Data With One Hot Encoding (4:57)
03-03 Logistic Regression With One Hot Encoding (2:20)
04-04 Scale And Encode Data With Scikit-Learn (3:47)
04-04 What Is Scaling Data (6:36)
04-05 Build, Train And Test A Machine Learning Model (4:37)
05-01 Compare Decision Tree And Linear Regression Models (6:26)
05-01C What Is The Kbins Discretizer (4:54)
05-02 Bin Data With Kbins Discretizer (3:42)
05-03 Compare Binned Regression Models (3:39)
05-04 Build A Linear Regression Model On Stacked Data (3:20)
05-05A What Is K Means Clustering (11:58)
06-01 Build Univariate Nonlinear Transformatio (1:55)
06-01 What Is Gaussian Probability Distribution- (2:31)
06-01B What Is Poisson Distribution (1:08)
06-02 Build X Y Data With Poisson Distribution In Numpy (3:34)
06-02C What Is Logarithmic Data Transformation (2:34)
06-03 Build A Ridge Regression Model (3:41)
Build Machine Learning Models
01-01 Course Overview (3:30)
01-02 Build Models On The Web (5:06)
02-01 What Are Search Algorithms (7:21)
02-02 Depth First Search (9:00)
02-02b Build A Depth First Search Algorithm (8:26)
02-03 What Is Breadth First Search (bfs) (5:08)
02-03b Build A Breadth First Search Algorithm (6:56)
02-04 Depth Limited Search (3:58)
02-05 Iterative Deepening Depth First Search (5:32)
02-06 What Is Uniform Cost Search (6:04)
02-06b Build A Uniform Cost Search Algorithm (8:07)
02-07 Bidirectional Search (4:44)
03-01 What Are Informed Search Algorithms (4:07)
03-02 What Is Greedy Best-first Search (8:16)
03-02b Build A Greedy Best First Search Algorithm (10:43)
03-03 What Is A Search (5:10)
04-01 How Does A Machine Learning Agent Learn (7:37)
04-02 What Is Inductive Learning (4:10)
04-03 Make Decisions With Decision Trees (10:50)
04-04 Performance Of A Machine Learning Algorithm (4:13)
04-05 Handle Noise In Data (5:20)
04-06 Statistical Learning (3:56)
05-01 What Is Logistic Regression (4:26)
05-03 Prepare Data For Logistic Regression (12:19)
05-03a How To Prepare Data (8:52)
05-04 Build A Logistic Regression Model (5:29)
05-04a How To Build A Logistic Regression Model (3:28)
05-04b What Is Optimization (12:10)
05-05 Optimize The Logistic Regression Model (12:44)
05-05a How To Optimize A Logistic Regression Model (12:45)
05-06 Train The Model (10:09)
05-07 Test The Model (2:33)
05-08 Visualize Results (5:38)
06.01 What Is Gradient Boosting-1 (1:54)
06.02 Prepare Data For Gradient Boosted Classification-2 (7:19)
06.03 Build Binary Classes-3 (6:12)
06.04a How To Shape Data For Classification-4 (2:58)
06.04b Shape Data For Classification-5 (7:06)
06.05a How To Build A Boosted Trees Classifier-6 (4:03)
06.05b Build A Boosted Trees Classifier-7 (4:37)
07.01 Build Input Functions-1 (3:55)
07.02 Build A Boosted Trees Regressor-2 (3:02)
07.03 Train And Evaluate The Model-3 (4:07)
Source Files
Text to Speech with Python Machine Learning, Deep Learning and Neural Networks
01-01 How Text To Speech Works (5:43)
01-00 Course Overview - Text To Speech (1:13)
01-02 What You-ll Need - Text To Speech (3:25)
03 Convert Text To Speech With Gtts (5:45)
04-00 What Are Pytorch, Tacotron 2 And Waveglow (4:29)
04-01 Load Models (3:50)
04-02 Convert Text To Speech With Pytorch (7:45)
05-00 What Is Pyttsx3 (1:20)
05-01 Load Available Voices (4:32)
05-02 Convert Text To Speech With Pyttsx3 (4:48)
Google Cloud Professional Machine Learning Engineer Certification Introduction
00-Course Preview (4:02)
02a-01 Why use the cloud for machine learning (2:38)
02a-02 Benefits of cloud computing- (1:23)
02a-03 Public vs private cloud computing (3:18)
02a-04 Managed vs unmanaged cloud computing (1:30)
02a-05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
02a-06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
02b-01 Build a Google Cloud project for machine learning (6:45)
02b-02a What is a service account in Google Cloud Platform (1:59)
02b-02b Build a service account and key in Google Cloud (6:52)
02c-01 Image dataset for machine learning from Cloud Storage (2:12)
02c-02 Build an image dataset for classification from a Cloud Storage bucket (5:36)
02d-01 Train an AutoML image classifier machine learning model (6:27)
02d-02 Deploy machine learning model to Cloud endpoint (3:38)
02d-03 Make a prediction with a Cloud machine learning model (5:14)
03-01 Sign in to Google Cloud (2:46)
03-02 Build a BigQuery dataset in Google Cloud Console (8:24)
03-03 Build a Cloud Storage bucket in Google Cloud (8:15)
04-01 What is Dataflow API in Google Cloud (2:44)
04-02 What is PubSub in Google Cloud (4:24)
04-03 Build data streaming Dataflow Pipeline with Google Cloud API (9:39)
05-01 Analyze streaming data with BigQuery Google Standard SQL (6:39)
05-02 Visualize BigQuery Cloud data with Google Data Studio (3:54)
Source Files
Microsoft Certified Azure Data Scientist Associate Preparation
00a-01 What is Microsoft Azure Machine Learning (3:24)
00a Course Overview (3:09)
00a-02 What is Microsoft Certified Azure Data Scientist Associate (5:10)
02-01 Why use the cloud for machine learning (2:38)
02-03 Public vs private cloud computing (3:18)
02-04 Managed vs unmanaged cloud computing (1:30)
02-05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
02-06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
03 What is Azure Machine Learning studio (2:17)
04-01 Build an Azure Machine Learning workspace (12:51)
04-02 Build a new compute cluster in Microsoft Azure ML (6:08)
04-03 Build a pipeline in Microsoft Azure ML Designer (4:25)
04-03a What is Azure Machine Learning designer (3:16)
05-01 Build a dataset in Microsoft Azure ML Designer (3:48)
05-02 Clean missing data in Microsoft Azure ML Designer (10:26)
05-03 Normalize data in Microsoft Azure ML Studio (4:24)
05-04 Run a data transformation pipeline in Microsoft Azure ML Designer (2:09)
06-00 What is Linear Regression (5:03)
06-01 Build a model training pipeline in Microsoft Azure ML Studio (5:03)
06-02 Evaluate a machine learning model in Microsoft Azure ML (7:08)
Source Files
Beginners Machine Learning Masterclass with Tensorflow JS
00-02 What Is Tensorflow Js (4:28)
00-01b What You-ll Learn (7:12)
00-03 Load Tensorflow Object (4:28)
01 What Is Machine Learning (6:39)
01b-01 Build A Scatter Plot (8:41)
01b-02 Build A Bar Chart (5:33)
01b-03 Build A Histogram (6:39)
01c-01 Build Sample Data (5:16)
01c-02 Build The Model (11:14)
01c-03 Make A Prediction (7:47)
01d-01 Generate Data (13:38)
01d-02 Visualize Data (16:10)
02-00 What Is Linear Regression (7:52)
02-01 Prepare Training Data (7:10)
02-02 Build The Model (14:05)
02-03 Make A Prediction (3:53)
02b-01 Set Up The Canvas (3:48)
02b-02 Draw A Data Sample (6:20)
02b-03 Create Loss And Prediction Functions (6:00)
02b-04 Collect User Input For Data (8:50)
02b-05 Visualize Linear Regression With Dynamic Data (6:46)
03-01 Set Up The Canvas (11:00)
03-02 Visualize Linear Regression With Dynamic Data (16:33)
04-01 Generate Samples (6:21)
04-02 Generate A Prediction Equation With Weights (6:54)
04-03 Train The Model (5:26)
04-04 Visualize Predictions (18:01)
04-05 Visualize Prediction Error (10:00)
05-01 Load Models Into Html (5:51)
05-02 Train Model On Images (13:13)
05-03 Make A Prediction (6:58)
Source Files
Beginners Guide to Neural Networks in Tensorflow JS
04-00a What Is Deep Learning (6:08)
00 What You-ll Learn (7:44)
04-00b What Is A Neural Network (8:06)
04-01 Build A Perceptron (13:26)
04-02 Build A Sigmoid Function (8:01)
04-03 Build A Sigmoid Perceptron (7:35)
04-04 Build A Relu Activation Function (7:12)
04-05 Build A Leaky Relu Activation Function (6:10)
05-01 Build Neural Network Layers (9:57)
05-02 Train And Test The Neural Network (11:24)
06-01 Build A Dataset (8:26)
06-02 Build A Neural Network (5:35)
06-03 Train The Neural Network (10:05)
06-04 Make A Prediction With The Neural Network (8:43)
07-00 What Is Cross Validation (8:24)
07-01 Load A Model Into HTML (4:57)
07-02 Use A Neural Network In Your Website (8:49)
07-03 Show Neural Network Results On Website (5:34)
08-01 Build A Dataset For XOR (6:32)
08-02 Build A Neural Network For XOR (5:19)
08-03 Train And Test The Neural Network (11:06)
09-01 Load An Rnn Into Your Website (5:37)
09-02 Set Up The Canvas (7:06)
09-03 Draw With A Neural Network (8:50)
10-01 Load An Image For Object Detection (6:13)
10-02 Load A Neural Network For Object Detection (6:15)
10-03 Outline Objects In The Image (12:17)
11-01 Build A Deep Neural Network With Gradient Descent From Scratch (9:21)
11-03 Build A Deep Neural Network With Gradient Descent With Tensorflow JS (11:24)
11-04 Build A Deep Neural Network With Backpropagation (7:03)
11-05 Build The Backpropagation (16:56)
12-01 Reduce Neural Network Error (17:12)
12-02 Build A Gradient Descent Algorithm (8:48)
13 Train The Deep Neural Network With Gradient Descent (15:24)
Tensorflow JS Source Files
Advanced Machine Learning with TensorFlow.js
02-02 View Model Results Of Text Toxicity (6:40)
02-01 Load The Model With Text (4:18)
02-03 Clean Up Prediction Results (6:18)
03-01 Set Up The Speed Recognition Model (6:00)
03-02 Set Up The Canvas (3:26)
03-03 Classify Words Through Microphone (6:55)
03-04 Draw From User Commands (7:35)
03-05 Optimize The Drawing (5:53)
04-01 Tidy Tensors (6:26)
04-02 Keep Tensors (3:10)
04-03 Dispose Tensors (2:41)
04-04 Build A Memory Leak Example (4:35)
05-01 Load Json Data (7:34)
05-02 Convert Json Data To Tensor (9:08)
05-03 Visualize Dataset With Tf-Vis (5:38)
05-04 Build And Train Model (10:22)
05-05 Visualize Model-s Training Epochs (9:12)
05-06 Make A Prediction (13:49)
05-07 Visualize Prediction (9:09)
06-01 Load Dataset From Json File (6:48)
06-02 Visualize Dataset-s Features (9:26)
06-03 Build A Multi Layer Model (7:43)
06-04 Extract Inputs And Outputs (7:10)
06-05 Normalize Data (4:47)
06-06 Train The Model (6:01)
06-07 Evaluate Model Performance (6:12)
07-00 What Is Logistic Regression (4:32)
07-00B Calculate Logistic Regression Accuracy (5:20)
07-01 Build A Logistic Regression Model (7:08)
07-02 Train The Logistic Regression Model (15:20)
07-03 Visualize Logistic Regression Results (12:52)
07-04 Visualize Original Data (12:13)
07-05 Visualize Model Error (7:37)
08-00 What Is Fast Fourier Transform (2:42)
08-01 Build And Visualize A Dataset (10:48)
08-02 Visualize Frequencies With Fast Fourier Transform (11:53)
08-03 Visualize Inverse Fast Fourier Transform (5:44)
09-00 What Is Principal Component Analysis (6:13)
09-01 Build Principal Component Analysis (6:24)
09-02 Calculate Variance Of Data And Principal Component Analysis (9:28)
09-03 Visualize Data Slices (12:01)
09-04 Visualize Principal Component Analysis Results (3:03)
Source Files
Advanced Neural Networks with TensorFlow.js
02-01 Build Training Data (7:34)
02-00 What Is One Hot Encoding (6:53)
02-02 Build The Neural Network (6:48)
02-03 Train The Neural Network (9:33)
02-04 Make A Prediction (10:11)
03-01 Build Training Data To Represent Images (12:15)
03-02 Build The Convolutional Neural Network (10:39)
03-03 Train The Convolutional Neural Network (9:06)
03-04 Make A Prediction Of Number Of Lines (15:05)
04-00 What Is A Recurrent Neural Network (6:38)
04-01 Generate Sequence And Label (6:25)
04-02 Generate Dataset (6:02)
04-03 Build The Lstm Model (4:55)
04-04 Train The Model (11:25)
06-01 Process Iris Data (7:37)
06-02 Convert Data To Tensors (8:45)
06-03 Separate Training And Testing Data (8:54)
06-04 Create Training And Testing Datasets (4:42)
06-05 Build The Model (9:29)
06-06 Train The Model (4:11)
06-07 Make A Prediction (8:45)
07-01 Load Model And Dataset (5:57)
07-02 Get User Input For Sentiment Analysis (10:59)
07-03 Make A Prediction (7:11)
08-00 What Is A Convolutional Neural Network (19:29)
08-01 Set Up Canvas To Load Image Data (10:36)
08-02 Load Mnist Dataset (6:47)
08-03 Separate Training And Testing Data (5:40)
08-04 Build The Model (6:48)
08-04A What Are The Network-s Layers (14:14)
08-05 Train The Model (11:27)
08-06 Create Training Batches (6:14)
08-07 Create Testing Batches (11:31)
08-08 Fit Neural Network Through Data (8:54)
Source Files
Python and Android Tensor Flow Lite - Machine Learning for App Development
00-01 What You-ll Need (4:29)
00-00 Course Overview (3:12)
04b Project Preview (2:17)
05-01 Build A Linear Regression Model With Python (15:06)
05-02 Convert Python Model To Tensorflow Lite (5:38)
06-03 Build A New Android Studio App (7:39)
06-04 Build App Layout (10:18)
07-05 Load Machine Learning Model (4:53)
07-06 Use Machine Learning Model (5:18)
07-07 Connect App Layout To Model (6:08)
08-00 Project Preview (1:49)
08-00 What Is Logistic Regression (4:32)
09-01 Load And Process Data For Logistic Regression With Scikit Learn (9:14)
09-02 Build A Logistic Regression Model With Python (8:01)
09-03 Convert Logistic Regression Model To Tensorflow Lite (2:38)
10-04 Build A New Android Studio App With Tf Lite Model (5:48)
10-05 Build App Layout For Logistic Regression (9:26)
11-06 Load Logistic Regression Model In Android Studio (5:01)
11-07 Use Logistic Regression Model In Android (8:46)
11-08 Enable App User Interaction With Machine Learning Model (9:54)
Source files
CoreML SwiftUI Masterclass - Machine Learning App Development
00-01 What You-ll Need (5:56)
00-00 Course Overview (6:54)
00-02 What Is Coreml (6:43)
01-00A What Is Sentiment Analysis (4:39)
01-00B Natural Language Framework (4:32)
01-01 Build A New Swiftui App For Sentiment Analysis (8:59)
01-01 Train A Model With CreateML (12:13)
01-02 Perform Sentiment Analysis In SwiftUI (7:38)
01-02 Test The Model With CoreML In An App (14:17)
01-03 Change Color Depending On Sentiment (4:56)
01-03 Display Prediction Accuracy (6:41)
03-01 What Is Deep Learning (6:10)
03-02 What Is A Neural Network (8:08)
04-01 Load A CoreML Model Into A New Xcode Project (11:00)
04-02 Add Images For Classification (6:31)
04-03 Enable User To Loop Through Image (5:40)
04-04 Import CoreML Model Into The View (5:28)
04-05 Resize Image For Model (6:26)
04-05A Resizing Image Overview (7:44)
04-06 Convert Image To Buffer For Model (8:55)
04-06A Image To Buffer Overview (6:55)
04-07 Test The Model On Image Classification (14:31)
05-00 Tip - How To Unhide Library Folder (1:22)
05-01 Build A New Xcode Project To Compile Model (4:44)
05-02 Build A Playground With Object Detection Model (4:28)
05-03 Instantiate A Model 05-Object (6:12)
05-04 Build An Image Analysis Request (7:23)
05-05 Resize Image For Model (9:36)
05-06 Convert Image To Buffer For Model (9:47)
05-07 Test Object Detection On Image (4:53)
✅ LEVEL 3 - Build Chatbots from Scratch
01 Projects Preview (4:49)
01 What Is Natural Language Processing (5:39)
02 What Is Text Vectorization (7:34)
03-01 Train A Vectorizer (8:50)
03-02 Chat With The User (11:04)
04-01 Define A Basic Intent Classifier (7:42)
04-02 Define A Basic Generative Model (4:05)
04-03 Test The Chatbot (9:33)
Source Files
Build a Machine Learning Chatbot from Scratch
01-01 Build patterns and responses training data (6:34)
01-02 Tokenize chat data for training (4:30)
02-01 Clean chat data for machine learning (3:04)
02-02 Build bag of words for ML model (4:24)
02-03 Split data for machine learning (3:34)
03-01 Build a TensorFlow machine learning model for chat (4:54)
03-02 Test chatbot machine learning model (9:09)
03-03 Categorize chat question with ML (7:25)
03-04 Pick a chatbot response in top category (8:18)
Source Files
Build Advanced Chatbot with Transformer Neural Network
00-02 Transformer Project Overview (7:59)
00-01 Introduction to Transformer Neural Networks (4:31)
01-01 Connect To Google Drive Dataset In Colab (3:48)
01-02 Read Text Files In Python (9:17)
01-03 Read Movie Conversation Text File In Python (10:58)
01-04 Clean Text Data For NLP (6:09)
01-05 Remove Contractions From Text Data With Python (9:35)
01-06 Preprocess Text Data For Transformer Chatbot Ml (6:16)
02-01 Build Tokenizer With Tfds (7:31)
02-02 Add Padding To Tokenized Sentences With Python (3:06)
02-03 Build Tensorflow Dataset For ML (3:13)
03-01 Calculate Scaled Dot Product Attention (4:56)
03-02 Set Up Multi Head Attention Layer In Python Nn (5:22)
03-03 Split Attention Layer Into Multiple Heads (4:08)
03-04 Add Scaled Dot Product Attention And Final Layer (5:22)
04-01 Mask Padding Tokens With Python (4:38)
04-02 Build Lookahead Mask For Future Tokens (3:53)
05-01 Set Up Positional Encoding Layer In Neural Network (3:03)
05-02 Build Positional Encoding Layer With Tensorflow Keras (5:31)
06-01 Build Input Encoder For Neural Network (5:28)
06-02 Combine Input And Positional Encoding (5:36)
07-01 Set Up Decoder Layer With Python (6:31)
07-02 Combine Output And Positional Encoding For Decoder (5:28)
08-01 Combine encoding and decoding in NN (7:08)
08-02 Build custom ML model learning rate (3:37)
08-03 Build custom model loss function (3:17)
08-04 Compile neural network with Python (4:30)
08-04b Zero out padding tokens in attention (1:44)
08-05 Limit and pad tokenized sentences (5:30)
09-01 Handle new chatbot question input (5:13)
09-02 Decode tokens into words (2:30)
Source files
✅ LEVEL 4 - ChatGPT 4 AI Prompt Engineering for Entrepreneurs
01 01 What Is Chatgpt (7:50)
00-01 Introduction Of The Instructor (2:25)
01 02 Intro To Prompt Engineering-Prompt Types (8:28)
01 03 Intro To Prompt Engineering-Effective Prompts (8:41)
01B 01 Project Preview (2:04)
01B 02A Simplify Complex Information (8:38)
01B 02B Simplify Complex Information-Other Strategies (8:41)
02 03 Proofread-Email And Business Proposals (8:39)
02.03 Proofread-More Use Cases (8:24)
02.04 Re-Organize Data-Benefits And First Sample Use Case (6:22)
02.04 Re-Organize Data-Potential Use Cases Case (10:44)
02.05 Work With Spreadsheets-Automating Data Entry (7:46)
02.05 Work With Spreadsheets-Formulas And Other Use Cases (7:31)
03 01 Project Preview (1:23)
03.02 Create Content (4:03)
03.03 Social Media (4:26)
03.04 Write Ad Copy (8:17)
03.05 Write Email Marketing Campaigns (4:55)
03.06 Write An Outreach Message (5:08)
03.07 Copyrighting (4:29)
03.08 Seo (5:09)
03.09 Video Scripts (8:49)
03.10 Generate Text In Your Writing Style (3:25)
04 01 Project Preview (1:51)
04.02 Research-Chatgpt Usecase And Benefits (7:05)
04.02 Research-More Examples And Explanation (7:49)
04.03 Write An Article-Add Role To Chatgpt (7:17)
04.03 Write An Article-Generate High Quality Content (8:02)
04.04 Check Plagiarism (10:56)
04.05 Prepare For Job Opportunities-Cv And Cover Letter (8:28)
04.05 Prepare For Job Opportunities-Interview Questions, Connection And Task Generator (8:36)
05 01 Project Preview (2:33)
05.02 Generate Code-Javascript And Python Code Snippets (9:26)
05.02 Generate Code-Stylesheet, Html, C++ And Conversion (9:20)
05.03 Build Algorithms-Algorithm To Pseudocode (4:03)
05.03 Build Algorithms-Realworld Use Cases (8:11)
05.04 Debug-Python Use Case (6:51)
05.04 Debug-React, Api, Javascript, Html And Css (6:56)
05.05 Write Code Documentation (9:51)
05.06 Use Chatgpt As A Linux Terminal (8:32)
05.07 Use Chatgpt As A Unix Terminal (9:08)
05.08 Use Chatgpt As A Microsoft Dos Terminal (5:28)
05.09 Use Chatgpt To Suggest Uxui Designs (8:10)
05.10 Use Chatgpt To Suggest Cybersecurity Solutions (10:05)
Source Files
ChatGPT 4 for Marketing Professionals
01.01 Setting Up Your Chatgpt Account - A Step-By-Step Guide (6:04)
00 01 Introduction Of The Instructor (1:53)
01.02 Tips For Getting The Best Responses From Chatgpt (9:55)
02 01 Building A Marketing Campaign Content Calendar With Chatgpt (10:34)
03 01 The Importance Of Identifying Your Target Audience_1 (3:08)
03.02 Using Chatgpt For Target Audience Research And Assessment (11:54)
04 01 Project Preview (1:21)
04.02 Exploring Social Media Marketing And Automation (5:59)
04.03 Generating Social Media Posts (10:38)
04.04 Social Media Automation Tool - Socialbee - -Bonus- (6:40)
04.05 Automating Social Media Post Scheduling - -Bonus- (8:26)
04.06 Automating Social Media Reposting - -Bonus- (8:28)
04.07 Configuring Your Social Media Automation Timetable – -Bonus- (4:00)
05 01 Project Preview (1:11)
05.02 Generate Optimized Keywords And Blog Headlines (7:39)
05.03 Building An Seo-Enhanced Blog Post Quickly (10:07)
06 01 Introduction To Email Marketing And Its Significance_1 (3:37)
06.02 Building Effective Email Sequences (7:27)
07 01 Crafting Sales Page Copy (8:05)
08 01 Project Preview (1:07)
08.02 Producing Facebook Ads (10:00)
08.03 Generating Google Ads (9:44)
08.04 Generate Ads For Instagram And Twitter (7:36)
09 01 Project Preview (1:09)
09.02 Generating Unlimited Video Concepts (10:33)
09.03 Crafting A Full Youtube Video Script (10:00)
09.04 Youtube Seo Strategies (8:54)
10 01 Project Preview (1:26)
10.02 Guides To Building Effective Marketing Funnels (4:01)
10.03 Defining Your Buyer Persona (9:38)
10.04 Generating A Lead Magnet (8:57)
10.05 Building Landing Page And Social Media Copy (9:02)
10.06 Composing A Comprehensive Email Sequence For Your Funnel (4:49)
11 01 Review Analysis And Optimization Of Products And Services (7:24)
12 01 Project Preview (1:57)
12.02 Homepage, About Us, And Contact Us Page Copy (10:14)
12.03 Generate Meta Title And Descriptions (4:58)
12.04 Website Development With Chatgpt Crash Course (25:32)
13 01 Project Preview (1:10)
13.02 Creating Product And Business Names (7:52)
13.03 Developing Professional Taglines And Slogans For Your Brand (7:32)
13.04 Writing Product Descriptions For Your Online Store (4:46)
13.05 Building Faq’S For Services Or Products (4:54)
14 01 Conclusion (2:08)
Bonus - Tips And Tricks (7:43)
Source file
Master the API
00b-02 How Openai Api Works (2:09)
00b-01 Openai Api Models To Work With (2:53)
00b-03 Adjust Openai Api Model Parameters (7:58)
01-01 Use Openai Api To Answer Questions Like Chatgtp (10:19)
01-02 Correct Grammar With Openai Api (3:30)
01-03 Summarize And Simplify Text With Openai Api (4:03)
01-04 Translate Text With Openai Api (3:04)
02-01 Generate Code With Openai Api (7:11)
02-02 Explain Code With Openai Api (5:24)
02-03 Calculate Time Complexity With Openai Api (3:40)
02-04 Translate Programming Languages With OpenAI API (4:24)
02-05 Fix Bugs In Code With Openai Api (3:19)
03-01 Generate Sql Queries With Openai Py (5:15)
03-02 Build Structured Table Data From Long Form Text (4:29)
03-03 Classify Items Into Categories With Openai Api (4:50)
03-04 Generate Spreadsheets And Lists With Chatgpt Openai Api (5:46)
04-01 Convert Notes To Summary With Openai Api (5:40)
04-02 Add Emotional Sentiment To Text With Openai Models (9:40)
04-03 Generate Questions On A Topic With Gpt Turbo (9:26)
04-04 Generate Text Conversation With Chatgpt Api (5:19)
05-01 Classify Text Emotion Sentiment With Chatgpt Models (5:09)
05-02 Extract Keywords From Text With Chatgpt Api (4:31)
05-03 Convert Product Description To Ad With Chatgpt Python (3:57)
05-04 Generate Product Names With Chatgpt In Python (4:04)
05-05 Extract Information From Text With Chatgpt Api (2:57)
06-01 Build Html Parser With Python (4:31)
06-02 Scrape Hyperlinks From Url Webpage With Python (4:09)
06-03 Filter Out Urls Not Part Of Domain (7:03)
06-04 Save Web Content To Files With Python (10:07)
07-01 Convert Text To Csv With Python (6:36)
07-02 Remove Whitespace And Lines From Text With Python (4:58)
07-03 Tokenize Text With Python For Machine Learning Models (2:50)
07-04 Split Long Lines With Python (4:11)
07-05 Split Pandas Dataframe Into Sections With Python (7:19)
07-06 Embed Text For Machine Learning With Openai Api (8:05)
08-01 Embed Question With Python (5:48)
08-02 Answer Questions About Your Data With Customized Openai Model (10:36)
09-01 Load And Read Pdf In Python (3:40)
09-02 Build Vector Index From Pdf Text In Python (4:32)
09-03 Answer Questions About Pdf With Chatgpt Model In Python (5:10)
10-01 Generate Review Data With Chatgpt Api (8:14)
10-02 Format Python Text To Multidimensional Pandas Dataframe (11:50)
10-03 Change Column Data Type In Pandas Dataframe (2:40)
10-04 Embed Text Data With Openai Api (6:25)
Source files
✅ LEVEL 5 - Pass the Chatbot Coding Interview
01 01 What is Tokenization (1:18)
01 02 What text can cause tokenization problems (3:42)
01 03 What is sentence segmentation (1:40)
02 01 What is Stemming in NLP (3:40)
02 02 What are issues with stemming in NLP (1:03)
02 03 What is Lemmatization in NLP (2:20)
03 01 What is text normalization in NLP (1:28)
03 02 What is named entity recognition in NLP (3:27)
03 03 What is relation recognition in NLP (2:10)
03 04 What is a parser in NLP (1:51)
04 01 What is Term Frequency-Inverse Document Frequency in NLP (4:12)
04 02 What is TF-IDF vectorization in NLP (1:21)
04 03 What are issues with TF-IDF in NLP (3:46)
05 01 What is a Bag of Words in NLP (3:52)
05 02 Give an example of Bag of Words in NLP (3:10)
05 03 What are issues with the Bag of Words NLP approach- (2:54)
06 01 What is Sequence Classification in NLP (1:55)
06 02 What are Hidden Markov Models in NLP (1:55)
07 01 What are regular expressions in programming (2:00)
07 02 Regular Expression Operators in Code (9:11)
07 03 Common Regular Expression Symbols Overview (7:43)
Complete Course Source Files
Essential Algorithms and Data Structures
00-02 Fizzbuzz Kotlin (5:26)
00-01. Kotlin Course Introduction (7:04)
01-01 Reverse Words In A String Kotlin (3:53)
01-02 Rotate Array Kotlin (7:31)
01-03 Kth Largest Element In An Array Kotlin (4:26)
02-01 Set Matrix Zeros Kotlin (12:20)
02-02 Spiral Matrix Kotlin (21:56)
03 Queue With A Linkedlist Kotlin (10:43)
04-00 Build A Binary Tree (15:46)
04-01 Delete Tree Node Kotlin (17:20)
05-01 Delete Tree Node Kotlin (17:20)
05-02 Selection Sort Algorithm Kotlin (6:01)
05-03 Insertion Sort Kotlin (6:15)
05-04 Merge Sort Algorithm Kotlin (15:10)
06 Build A Graph Kotlin (7:28)
07-01 Coin Change Kotlin (8:02)
07-02 Maximum Sum Subarray Kotlin (7:06)
07-03 Edit Distance Kotlin (9:37)
08-01 Single Number Kotlin (7:29)
08-02 Number Of 1 Bits Kotlin (7:24)
08-03 Bitwise And Of A Range Kotlin (7:23)
09-01 Permutations Kotlin (16:12)
09-02 Combinations Kotlin (9:28)
09-03 Letter Combinations Of A Phone Number Kotlin (10:31)
10-01 Reverse Integer Kotlin (11:52)
10-02 Palindrome Number Kotlin (9:53)
10-03 Excel Sheet Column Number Kotlin (5:23)
Source Code
Machine Learning Interview Questions
01-00. Intro (1:54)
00. Course Intro (5:09)
01-01. What is Machine Learning (17:47)
01-02. Types Of Machine Learning (10:48)
01-03. Building A Machine Learning Model (17:02)
02-00. Intro (2:44)
02-01. How To Choose An Algorithm (16:42)
02-02. Common Machine Learning Algorithms Part 1 (15:58)
02-03. Common Machine Learning Algorithms Part 2 (22:52)
02-04. Common Machine Learning Algorithms Part 3 (13:03)
02-05. Comparison Interview Questions (16:20)
03-00. Intro (2:08)
03-01. Data Related Errors (16:55)
03-02. Model Related Errors (11:34)
03-03. Results Testing Techniques (11:18)
04-00. Intro (2:14)
04-01. Missing_Corrupted Data (5:08)
04-02. Selecting Important Variables (3:18)
04-03. Fixing Multicollinearity- (3:56)
04-04. Kernel Tick (3:21)
04-05. Slow Machine_Limited Memory (4:59)
04-06. Classification and Random Sampling (3:38)
04-07. Low Training Error with High Validation Error (4:40)
04-08. Cross Validation on Time Series Data (3:38)
04-09. Amazon Recommendation System (5:26)
05. Course Summary and Outro (3:12)
Machine Learning Interview Questions
Math Interview Questions with JavaScript
01-01 Happy Number (15:33)
00 Introduction (4:08)
01-02 Trailing Zeros In Factorial (11:10)
01-03 Count Primes (6:57)
01-04 Sorted Permutation Rank With Repeats (10:23)
01-05 Sqrt(x) (18:19)
01-06 Greatest Common Divisor (7:27)
02-01 Integer To Roman (17:16)
02-02 Roman To Integer (12:04)
02-03 Rearrange Array (9:11)
03-01 N-th Tribonacci Number (14:00)
03-02 Maximum Product Of Three Numbers (10:58)
03-03 Grid Unique Paths (17:22)
03-04 City Tour (5:51)
04-01 Next Greater Element (15:28)
04-02 Ugly Number (9:02)
04-03 Power Of Two Integers (11:28)
04-04 Prime Sum (12:23)
04-05 Sum Of Bit Differences Among All Pairs (20:53)
Source Files
SQL Interview Questions
01-00 Build A Table And Display All Records (10:26)
00 Course Overview (4:21)
01-01 List Alphabetically (4:13)
01-02 List All Not In Specified Range (4:03)
01-03 List All In Range By Multiple Properties (7:39)
01-04 Display All With Characters In Property (6:10)
01-05 Select From Where In This And Not In That (6:09)
01-06 List All With Exact Number Of Characters (5:44)
01-07 List All With Specific Second Character (5:11)
01-08 List All Distinct Ids Available (3:36)
01-09 Calculate Percentage (4:45)
01-10 Consecutive Numbers (9:03)
02-01 List Streams With Viewer Information (9:33)
02-02 Join Two Tables (5:19)
02-03 Find Same Value In Another Property (8:08)
02-04 List Values Across Tables (6:35)
02-05 Join Three Tables (6:44)
02-06 Find Average Across Tables (6:59)
02-07 Display Difference Across Tables (6:27)
02-08 Find More Than Value Across Tables (6:11)
02-09 Product Sales Analysis (6:50)
02-10 List All After A Certain Date (6:33)
02-11 Capital Gain Loss (6:33)
03-01 Count Distinct Properties (3:47)
03-02 List Total Sum (3:40)
03-03 Get Minimum (3:12)
03-04 Get Maximum From A Distinct Type (4:12)
03-05 Get Average And Count At An ID (3:22)
03-06 Get Number Of Same Type (3:05)
03-07 Get Difference Between Max And Min (2:30)
03-08 Find Minimum Of Each Type (5:13)
03-09 Get Sum At Each ID (3:39)
03-10 Get Average For Each ID (4:40)
03-11 Get Different Properties (4:29)
03-12 List Where Maximum Is Greater Than (4:34)
03-13 List Average Where Greater Than (6:16)
04-01 Find Higher Values (8:03)
04-02 Find All Of A Type (6:40)
04-03 List All Above Average (5:10)
04-04 List All Greater Than Minimum (7:52)
04-05 List All Part Of Any (7:57)
04-06 List All Who Have More (6:24)
04-07 List All With Same As Minimum (4:41)
04-08 List All Above Average In Their Group (6:35)
04-09 Get Kth Maximum Value (6:48)
04-10 Get Kth Minimum Value (5:27)
04-11 Select Last K Records From A Table (5:01)
04-12 List All Not In A Group (5:12)
04-13 Get K Maximum Values (4:58)
04-14 Get K Minimum Values (3:48)
Python Interview Questions with Time and Space Complexity
00A Course Overview (1:18)
01-01A Question - Find Maximum Sum Subarray Of Size K (2:00)
01-01B Find Maximum Sum Subarray Of Size K (5:18)
01-01C Explanation - Find Maximum Sum Subarray Of Size K (4:00)
01-02A Question - Find Smallest Subarray With Given Sum (2:06)
01-02B Find Smallest Subarray With Given Sum (5:50)
01-02C Explanation - Find Smallest Subarray With Given Sum (3:41)
02-01A Question - Find Pair With Target Sum (1:17)
02-01B Find Pair With Target Sum (5:29)
02-01C Explanation - Find Pair With Target Sum (2:39)
02-02A Question - Remove Duplicates From List (1:26)
02-02B Remove Duplicates From List (3:30)
02-02C Explanation - Remove Duplicates From List (3:37)
02-03A Question - Remove Targets From Array (1:17)
02-03B Remove Targets From Array (3:50)
02-03C Explanation - Remove Targets From Array (2:53)
02-04A Question - Square A Sorted Array (1:40)
02-04B Square A Sorted Array (4:55)
02-04C Explanation - Square A Sorted Array (5:11)
03A Course Overview (1:24)
03C-01A Question - Cyclic Sort (1:36)
03C-01B Cyclic Sort (4:19)
03C-01C Explanation - Cyclic Sort (5:00)
03C-02A Question - Find Duplicate Number (1:06)
03C-02B Find Duplicate Number (3:32)
03C-02C Explanation - Find Duplicate Number (2:49)
03C-03A Question - Find Duplicate Without Modifying Array (1:45)
03C-03B Find Duplicate Without Modifying Array (4:43)
03C-03C Explanation - Find Duplicate Without Modifying Array (4:50)
03C-04A Question - Find All Duplicate Numbers (0:59)
03C-04B Find All Duplicate Numbers (3:47)
03C-04C Explanation - Find All Duplicate Numbers (6:51)
03C-05A Question - Find Missing Number (1:07)
03C-05B Find Missing Number (4:23)
03C-05C Explanation - Find Missing Number (9:18)
03C-06A Question - Find All Missing Numbers (1:12)
03C-06B Find All Missing Numbers (3:32)
03C-06C Explanation - Find All Missing Numbers (7:46)
03C-07A Question - Find Corrupt Pair (1:14)
03C-07B Find Corrupt Pair (4:00)
03C-07C Explanation - Find Corrupt Pair (4:52)
06A Course Overview (1:24)
06C-01A Question - Traverse Binary Tree Level Order (2:34)
06C-01B Traverse Binary Tree Level Order (6:49)
06C-01C Explanation - Traverse Binary Tree Level Order (4:54)
06C-02A Question - Traverse Binary Tree Reverse Level Order (1:58)
06C-02B Traverse Binary Tree Reverse Level Order (7:01)
06C-02C Explanation - Traverse Binary Tree Reverse Level Order (4:20)
06C-03A Question - Find Level Averages In Binary Tree (1:16)
06C-03B Find Level Averages In Binary Tree (7:30)
06C-04A Question - Find Level Order Successor (1:53)
06C-04B Find Level Order Successor (8:11)
06C-05A Question - Find Minimum Depth Of Binary Tree (1:33)
06C-05B Find Minimum Depth Of Binary Tree (5:29)
06C-06A Question - Find Maximum Depth Of Binary Tree Traversing All Levels (0:59)
06C-06B Find Maximum Depth Of Binary Tree Traversing All Levels (5:33)
06C-07A Question - Find Right View Of Binary Tree (1:15)
06C-07B Find Right View Of Binary Tree (6:27)
04A Course Overview (1:14)
04C-01A Question - Find Top K Numbers (0:47)
04C-01B Find Top K Numbers (4:03)
04C-01C Explanation - Find Top K Numbers (2:44)
04C-02A Question - Find Kth Smallest Number (1:03)
04C-02B Find Kth Smallest Number (4:32)
04C-02C Explanation - Find Kth Smallest Number (2:16)
04C-02C Explanation - Find Kth Smallest Number_1 (2:16)
04C-03A Question - K Closest Points To Origin (1:03)
04C-03B K Closest Points To Origin (7:59)
04C-03C Explanation - K Closest Points To Origin (2:01)
04C-04A Question - Find Minimum Cost To Connect Ropes (2:00)
04C-04B Find Minimum Cost To Connect Ropes (4:42)
04C-04C Explanation - Find Minimum Cost To Connect Ropes (2:11)
08-01A Question - Find Bitonic Array Maximum (1:50)
08-01B Find Bitonic Array Maximum (4:34)
08-02A Question - Order Agnostic Binary Search (1:27)
08-02B Order Agnostic Binary Search (5:47)
05A Course Overview (1:14)
06-01A Question - Is Linked List Cycle (1:15)
06-01B Is Linked List Cycle (5:01)
06-01C Explanation - Is Linked List Cycle (2:41)
06-02A Question - Find Length Of Linked List Cycle (1:17)
06-02B Find Length Of Linked List Cycle (6:25)
06-02C Explanation - Find Length Of Linked List Cycle (3:43)
06-03A Question - Find Middle Node Of Linked List (1:08)
06-03B Find Middle Node Of Linked List (4:25)
06-03C Explanation - Find Middle Node Of Linked List (1:36)
07-01A Question - Find Distinct Subsets (1:19)
07-01B Find Distinct Subsets (5:48)
07-02A Question - Find Subsets With Duplicates (2:02)
07-02B Find Subsets With Duplicates (4:06)
Source Files
Essential JavaScript Software Developer Interview Guide - Section 0 Course Introduction
00 Course Introduction (10:06)
01. Fizzbuzz (6:33)
Source Code
Section 1 String and Array Interview Questions
02 Rotate Array (7:44)
01 Reverse Words In A String (4:31)
03 Isomorphic Strings (6:49)
04 Kth Largest Element In An Array (10:11)
Source Code
Section 2 Matrix Interview Questions
02 Spiral Matrix (10:46)
01 Set Matrix Zero (8:15)
03 Number Of Islands (14:31)
Source Code
Section 3 Linked List Interview Questions
01 Implement A Stack Using An Array (11:58)
02 Add Two Numbers As Reversed Linked Lists (14:07)
03 Reverse A Linked List (7:56)
Source Code
Section 4 Binary Tree Interview Questions
01 Inorder Traversal (17:35)
02 Preorder Traversal (12:26)
03 Postorder Traversal (9:38)
Source Code
Section 5 Graph Interview Questions
02 Clone An Undirected Graph - Build A Queue (3:44)
01 Clone An Undirected Graph - Build A Graph (11:15)
03 Clone An Undirected Graph - Breadth First Traversal (7:31)
04 Clone An Undirected Graph - Depth First Traversal (5:58)
Source Code
Section 6 Sorting and Time Complexity
01 Types Of Time Complexity (13:27)
02 Types Of Better Time Complexity (18:15)
03 Bubble Sort Algorithm (6:41)
04 Selection Sort Algorithm (6:15)
05 Insertion Sort Algorithm (6:51)
06 Quicksort Algorithm (9:18)
07 Merge Sort Algorithm (8:43)
08 Time Complexity Of Different Sorting Algorithms (2:55)
Source Code
Section 7 Dynamic Programming
02 Edit Distance (15:15)
01 Coin Change (8:41)
03 Distinct Subsequences (7:26)
Source Code
Section 8 Bit Manipulation Interview Questions
01 Bitwise And Shift Operators (7:19)
02 Single Number (4:51)
03 Number Of 1 Bits (13:26)
04A Sum Of Two Integers (9:26)
04B Maximum Sum Subarray (6:52)
05 Reverse Bits (5:54)
06 Bitwise And Of A Range (4:28)
Source Code
Section 9 Combinations and Permutations Questions
01 Permutations (9:37)
02 Distinct Permutations Of A String (9:15)
03 Letter Combinations Of A Phone Number (14:36)
04 Factor Combinations (9:08)
Source Code
Section 10 Math Interview Questions
02 Palindrome Number (10:10)
01 Reverse Integer (10:07)
03 Excel Sheet Column Number (6:21)
Source Code
5) 5th Hour - Classification Models Continued, RMark Down and Excel
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