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The Complete Python Automation and Machine Learning Bundle
LEVEL 0 - Intro to Python
00. Introduction (4:47)
01. Intro To Python (5:46)
0 - 01 Code Python on the Web
02.01 What is Google Colab (4:24)
02.02 What If I Get Errors (2:39)
02.03 How Do I Terminate a Session (2:40)
0 - 02 Python Language Fundamentals
02. Variables (19:34)
03. Type Conversion Examples (10:21)
04. Operators (7:21)
05. Operators Examples (22:09)
06. Collections (8:39)
07. Lists (11:55)
08. Multidimensional List Examples (8:22)
09. Tuples Examples (8:51)
10. Dictionaries Examples (14:41)
11. Ranges Examples (8:46)
12. Conditionals (6:58)
13. If Statement Examples (10:32)
14. If Statement Variants Examples (11:35)
15. Loops (7:17)
16. While Loops Examples (11:47)
17. For Loops Examples (11:35)
18. Functions (8:04)
19. Functions Examples (9:33)
20. Parameters And Return Values Examples (14:08)
21. Classes and Objects (11:30)
22. Classes Example (13:28)
23. Objects Examples (10:10)
24. Inheritance Examples (17:43)
25. Static Members Example (11:20)
26. Summary and Outro (4:23)
Python_Language_Basics
Intro to Python Slides
LEVEL 1 - 01 Work with text files
01 Create And Read A New Text File (6:29)
02 Read And Write A Text File With A Loop (7:14)
03 Copy Contents Of A File (4:05)
Source files
02 Work with csv files
01 Print Csv Contents (4:25)
02 Print Csv As List (3:00)
03 Create A Csv File (3:35)
Source files
03 Work with json files
01 Read And Write To A Json File (3:15)
Source files
04 Work with Excel files
01 Inspect Excel Sheets (5:07)
02 Merge Excel Files (13:45)
03 Get Value From Cell In Excel Sheet (7:12)
04 Display Entire Excel Sheet Contents (3:48)
05 Create A New Excel File With Values (2:35)
Source files
05 Automate files with OS Module
01 Find A File By Name (3:17)
02 Check If File Or Directory Path Exists (3:03)
03 List All Files And Directories At A Path (2:51)
04 Find All Files Of Given Type (2:56)
05 Delete Old Files With Datetime Module (6:24)
06 Bulk Rename Files (4:54)
Source files
06 Work with exceptions
01 Handle Exceptions (4:21)
02 Use Asserts (3:24)
Source files
07 Search documents for words
01 Search Txt File (4:14)
02 Search Csv File (2:12)
Source files
08 Format data
01 Format Data Into Table (6:31)
02 Visualize Product Sales Per Quarter (9:37)
Source files
LEVEL 2 - Python Regular Expressions - 01 Search through data with regex
01 Search For String In Text (2:34)
02 Find Characters By Type (2:49)
Source files
02 Find expressions that match conditions
01 Find Words Of Specific Length Starting With Specific Letter (8:17)
02 Find Expression Containing Numbers And Symbols In A Specific Format (4:23)
03 Find Expression Of A Specific Format (5:10)
04 Search Ignoring Capitalization (1:19)
05 Find Words At Beginning Or End Of Line (3:45)
06 Find Independent Words (2:34)
Source Files
03 Search for anomalies
01 Find Repeating Characters (3:47)
02 Search For Multiple Expressions At Once (7:59)
03 Make A Dictionary Of Expressions Found (4:05)
Source files
04 Data manipulation with regex
01 Remove Whitespaces (2:57)
02 Split String On Word (3:29)
Source files
LEVEL 3 - Scrape the Web - Python and Beautiful Soup Bootcamp - Overview
00 What Is Web Scraping (5:39)
01 What You'll Need (1:30)
Source Files
Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:24)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
Source Files
03 Web Scraping with Beautiful Soup in Python
02 Build An Html Webpage To Scrape (12:42)
03 Select Data Structures From A Webpage (5:48)
04 Extract Urls And Text (5:24)
05 Work With Tags (8:06)
06 Work With Attributes (5:18)
07 Add Navigation To A String (5:29)
08 Navigate Html Contents (7:16)
09 Find All Filter (4:51)
Source Files
Web Automation with Selenium Python - 00 Getting Started with Selenium
00.00 What You'll Learn (5:42)
00.01 Install Selenium (9:11)
00.02 Download Visual Studio Code (4:10)
00 Source Files
01 Automate Finding Elements
01.01 Find Elements By Name (14:50)
01.02 Find Elements By Id (7:34)
01.03 Find Elements By Xpath (12:29)
01.04 Find Input Field By Xpath (13:44)
01.05 Find Elements By CSS Selector (9:14)
01.06 Find Elements By Link Text (7:47)
01.07 Find Elements By Partial Link Text (8:05)
01.08 Find Elements By Classname (6:22)
01.09 Find Elements By Tagname (7:29)
Source Files
02 Beginner's Automation with Selenium
02.01 Automate A Google Search (19:41)
02.02 Automate Navigating A Dropdown Menu (16:22)
02.03 Automate Changing Tabs (15:41)
02.04 Automate Alert Popups (13:26)
02 Source Files
03 Avoid Errors with Waits
03.01 Explicit Waits (21:03)
03.02 Implicit Waits (8:45)
Source Files
04 Automate Browsers Commands
04.02 Get Title And URL (4:12)
04.01 Automate Window Size (12:04)
04.03 Automate Closing Vs Quitting Windows (4:06)
Source Files
05 Automate Mouse Actions
05.01 Mouse Hover (14:01)
05.03 Right Click (6:26)
05.02 Automate Mouse Click (7:39)
05.04 Automate Double Click (8:36)
05.05 Click, Hold And Release (7:17)
05 Source Files
06 Automate Images
06.01 Web Scrape Images (13:29)
06.02 Automate Downloading Images (27:34)
Source Files
Data Mining with Python! Real-Life Data Science Exercises - 0 Data Mining Introduction
Introduction to Data Mining (9:30)
Project Files
01. Data Wrangling - A Complete Overview
Data Wrangling Demystified (63:56)
Project Filese
02. Data Mining Fundamentals
01. Cluster Analysis (20:08)
02. Classification and Regression (34:30)
03. Association and Correlation (13:10)
04. Dimensionality Reduction (25:38)
Project Files - Data Mining fundamentals with Mammoth Interactive
03. Frameworks Explained - Taming Big Data with Spark
01. Apache Spark - An Overview Of The Framework (26:36)
02. Spark Key Functions (20:26)
03. Spark Machine Learning (7:32)
04. EXAMPLES - Using The Machine Learning Pipeline (6:16)
Project Files
04. EXAMPLES - Mining and Storing Data
01. Text Mining (15:05)
02. Network Mining (10:11)
03. Matrix (7:16)
04. SQL (12:35)
Mining and Storing with Mammoth Interactive
05. NLP (Natural Language Processing)
01 NLP Data Cleaning (6:55)
02. Count Vectorizer (7:58)
03. NLP Example with Spam (9:59)
04. Tweak Model with Spam Data (5:32)
05. Pipeline with Spam Data (4:48)
Project Files - NLP with Mammoth Interactive
06. Conclusion and Summary
06. Conclusion and Challenge (4:40)
07 Conclusion Files - Mammoth Interactive
08 Please rate this course
Bonus Lecture - Mammoth Interactive Deals
Automate Excel Files with Python OpenPyXL - 01 Introduction
01.00 Course Overview (2:20)
01.01 Run Openpyxl on the Web (1:45)
Source Files
02 Use OpenPyXL and Sheets
02.01 Make a Workbook (11:01)
02.02 Save a Workbook (3:51)
02.03 Read a Workbook (8:02)
02.04 Work with Rows and Columns (8:07)
02.05 Use a Formula (8:41)
02.06 Use Dates (7:18)
02.07 Merge and Unmerge Cells (7:11)
02.08 Fold a Range (6:17)
02.09 Make a New Sheet (3:17)
02.10 Copy Data to a Sheet (4:35)
02.11 Remove a Sheet (3:45)
02 Source Files
03.01 Worksheet Tables
03.01 Build a Table (15:50)
03.02 Style a Table (8:55)
03.01 Source Files
03.02 Format Cells
03.01 Import Dataset (4:19)
03.02 Style a Cell (6:47)
03.03 Make a Named Style (6:57)
03.04 Copy a Style (4:59)
Source Files
04 Build 2D Charts
04.01 Make a Chart (11:04)
04.02 Build Line Charts (15:30)
04.03 Build a Pie Chart (14:09)
04.04 Build a Scatter Chart (11:22)
04.05 Build an Area Chart (8:21)
04 Source Files
05 Project_ Employee Timelog
05.01 Project Setup (4:29)
05.02 Expand Columns to Fit Content (6:35)
05.03 Add Dates (7:34)
05.04 Add Days of the Week (7:11)
05 Source Files
06 Write to a Text File
06.01 Read Spreadsheet Data (7:11)
06.02 Store Spreadsheet Data (3:39)
06.03 Write to a Text File (5:21)
06 Source Files
07 Update a Spreadsheet
07.01 Set Up Update Information (3:44)
07.02 Update the Spreadsheet (5:41)
07 Source Files
08 More Chart Types
08.01 Build a Stock Chart (9:13)
08.02 Build a Doughnut Chart (9:22)
08.03 Build a Bubble Chart (8:53)
08 Source Files
09 Web Scraping
09.01 Import Web Driver (8:06)
09.02 Scrape a Web Page (6:06)
09.03 Parse Page Data (9:17)
09.04 Put Data into Excel Sheet (6:22)
09.05 Clean Data (4:38)
09 Source Files
Beginners Excel VBA - 01-02 Introduction
01.00 Course Overview (2:24)
01.01 How To Save Macros (1:34)
01-02 Source Files
03 Send Messages with MsgBox
03.00 Topics Overview (0:53)
03.01 Build A Simple Message (3:34)
03.02 Build An Advanced Message (5:10)
03.03 Empty A Sheet With The Msgbox Function (6:44)
03.04 Prompt User For Input (7:29)
03 Source Files
04 Workbook and Worksheet Object
04.00 Topics Overview (1:30)
04.01 Object Hierarchymp4 (5:10)
04.02 Change Multiple Worksheets (7:05)
04.03 Add And Count Worksheets (6:01)
04.04 Get Path Of A Workbook (5:14)
04.05 Open And Close Workbooks (8:41)
04.06 Loop Through Worksheets And Workbooks (8:19)
04.07 Build A Sales Calculator (11:53)
04.08 Change Charts (10:30)
04 Source Files
05 Work with the Range Object
05.00 Topics Overview (1:22)
05.01 Program A Range Of A Spreadsheet (6:55)
05.02 Use Cells Instead Of A Range (6:04)
05.03 Use A Range Variable (6:04)
05.04 Select A Range (4:52)
05.05 Access A Row (4:35)
05.06 Copy And Paste A Range (8:45)
05.07 Clear A Range (3:59)
05.08 Count A Range (4:21)
05 Source Files
06 Work with Range Properties
06.00 Topics Overview (1:03)
06.01 Find The Current Region Of A Cell (7:23)
06.02 Dynamic Range Program (7:11)
06.03 Resize A Range (2:30)
06.04 Select Entire Rows And Columns (6:33)
06.05 Offset Property (3:32)
06.06 End Property (5:06)
06 Source Files
07 More Range Projects
07.00 Topics Overview (1:21)
07.01 Union And Intersect Of Ranges (4:24)
07.02 Detect Content (5:37)
07.03 Build A Range Program (5:47)
07.04 Change Text Color (4:30)
07.05 Bold A Range (2:39)
07.06 Change Cell Color (5:04)
07.07 Work With Areas (4:55)
07.08 Find Differences In Ranges (8:17)
07 Source Files
08 Variables
08.00 Topics Overview (1:38)
08.01 Integer Data Type (2:58)
08.02 String Data Type (2:20)
08.03 Double Data Type (2:54)
08.04 Boolean Data Type (4:05)
08.05 Retain Variable Value (2:54)
08 Source Files
09 Work with Conditionals
09.00 Topics Overview (1:26)
09.01 If Then Statement (4:38)
09.02 Else Statement (4:55)
09 Source Files
10 Work with AND, OR and NOT
10.00 Topics Overview (1:21)
10.01 Greeting Program - Logical Operator And (4:00)
10.02 Logical Operator Or (5:36)
10.03 Logical Operator Not (3:15)
10 Source Files
11 Build Conditionals Projects
11.00 Topics Overview (1:58)
11.01 Select Case (5:42)
11.02 Build A Commission Calculator Project (6:11)
11.03 Find Remainder With Mod (3:11)
11.04 Check Number Program (6:38)
11.05 K Smallest Value Program (6:54)
11.06 Group By Font Style (6:45)
11.07 Remove Empty Cells (5:26)
11 Source Files
Intermediate Excel VBA (Course 2) - Intro to loops
12.00.00 Course Overview (2:50)
12.00 Topics Overview (0:57)
12.01 Single Loop (5:59)
12.02 Double Loop (5:14)
12.03 Triple Loop (6:24)
12.04 Do While Loop (5:34)
12.05 Build A Commission Table (5:33)
12 Source Files
13 Loop Projects
13.00 Topics Overview (1:40)
13.01 Loop Through Defined Range (3:37)
13.02 Loop Through Entire Column (3:29)
13.03 Do Until Loop (3:22)
13.04 Use Step To Increment (4:22)
13.05 Build A Pattern Project (4:49)
13.06 How To Sort (5:32)
13.07 Sort By Related Data (8:15)
13.08 Delete Duplicate Values (6:10)
Source Files
14 String Manipulation
14.00 Topics Overview (1:28)
14.01 Join Strings (3:13)
14.02 Extract Substrings From Left Or Right (3:07)
14.03 Extract Substring At Middle (4:12)
14.04 Get Length Of A String (2:40)
14.05 Get Substring Position (3:16)
14.06 How To Split Strings (4:56)
14.07 Reverse Characters (4:10)
14.08 Change String Casing (3:28)
14.09 Count Words In A Range (8:17)
Source Files
15 Build Custom Functions
15.02 Pass Arguments To A Function (7:43)
15.01 Make And Use Your Own Function (6:33)
15.00 Topics Overview (1:19)
15.03 Custom Calculator Function (6:22)
Source Files
16 Build Arrays
16.00 Topics Overview (1:18)
16.01 One Dimensional Array (5:46)
16.02 Two Dimensional Array (6:25)
16.03 Change Array Size (4:54)
16.04 Build An Array (5:09)
16.05 Populate Row With Array (3:21)
16.06 Array Length (6:47)
16.07 Split String Into An Array (4:28)
16.08 Join Array Into A String (3:49)
Source Files
17 Work with Dates
17.00 Topics Overview (1:12)
17.01 Delay A Procedure (4:12)
17.02 Schedule A Procedure (4:18)
17.03 Count Years (5:20)
17.04 Count Days Between Dates (2:41)
17.05 Count Weekdays Between Dates (4:51)
17.06 Sort Dates (6:27)
17 Source Files
18 Application Object
18.00 Topics Overview (1:36)
18.01 How To Access Excel Functions (4:28)
18.02 Disable Screen Updating (3:19)
18.03 Disable Alerts (3:38)
18.04 Show Progress Of Macro_1 (6:35)
18.05 Read Data From A File (6:35)
18.06 Write Data To A File (5:10)
18 Source Files
19 VBA Projects
19.00 Topics Overview (0:52)
19.01 Build A Table (4:47)
19.02 Build A Table Of Contents (11:47)
19.03 Build A Table Of Contents 2 (4:42)
19.04 Combine Worksheets (17:42)
19.05 Combine Worksheets By Column (15:48)
19 Source Files
20 Programming Charts
20.00 Topics Overview (1:20)
20.01 Program A Chart (6:12)
20.02 Program An Embedded Chart (4:59)
20.03 Delete Charts Programatically (2:19)
20 Source Files
LEVEL 4 Introduction to Machine Learning and Python Data Science - 1. 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
2. PyPlot
00. Course Intro (5:30)
01. Intro to Pyplot (5:10)
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
3. Pandas
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 (19:20)
06. Getting Properties of Series (13:04)
07. Modifying Series (19:01)
13. DataFrame Operations (20:09)
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)
15. Reading CSVs (12:00)
14 DataFrame Comparisons and Iteration (15:35)
16. Summary and Outro (4:14)
Source Files
4. Machine Learning theory
00. Course Intro.mp4 (6:04)
01. Quick Intro to Machine Learning (9:00)
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
Build Machine Learning Models - 00a Intro to Tensorflow
00. Course Intro (6:10)
01. Intro to Tensorflow.mov (5:32)
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
00b Image Recognition with MNIST
00. Course Intro (6:57)
01. Intro to Image Recognition (6:40)
02. Intro to MNIST (4:42)
03. Building a CNN Part 1 - Obtaining Data (15:40)
04. Building a CNN Part 2 - Building the Model (10:14)
05. Building a CNN Part 3 - Adding Loss and Optimizer Functions (4:57)
06. Building a CNN Part 4 - Train and Test Functions (10:58)
07. Building a CNN Part 5 - Train and Test the Model (9:17)
08. MNIST Image Recognition with Keras Sequential Model (13:23)
09. Summary and Outro (2:55)
Source Files
01 Build Beginner Models in TensorFlow 2.0
01 Course Overview (3:30)
02 Build Models On The Web (5:06)
Source Files
02 AI Uninformed Search Algorithms
01 What Are Search Algorithms (7:20)
02 Depth First Search (9:00)
02b Build A Depth First Search Algorithm (8:26)
03 What Is Breadth First Search (bfs) (5:08)
03b Build A Breadth First Search Algorithm (6:55)
04 Depth Limited Search (3:57)
05 Iterative Deepening Depth First Search (5:32)
06 What Is Uniform Cost Search (6:04)
06b Build A Uniform Cost Search Algorithm (8:07)
07 Bidirectional Search (4:43)
Source Files
03 AI Informed Search Algorithms
01 What Are Informed Search Algorithms (4:07)
02 What Is Greedy Best-first Search (8:16)
02b Build A Greedy Best First Search Algorithm (10:43)
03 What Is A Search (5:10)
Source Files
04 How Machine Learning Works
01 How Does A Machine Learning Agent Learn (7:37)
02 What Is Inductive Learning (4:10)
03 Make Decisions With Decision Trees (10:49)
04 Performance Of A Machine Learning Algorithm (4:13)
05 Handle Noise In Data (5:20)
06 Statistical Learning (3:56)
Source Files
05 Logistic Regression
05.01 What Is Logistic Regression (4:26)
05.02 Prepare Data For Logistic Regression (12:19)
05.03 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)
05 Source Files
06 Gradient Boosted Classification
06.01 What Is Gradient Boosting (1:54)
06.02 Prepare Data For Gradient Boosted Classification (7:19)
06.03 Build Binary Classes (6:12)
06.04a How To Shape Data For Classification (2:57)
06.04b Shape Data For Classification (7:06)
06.05a How To Build A Boosted Trees Classifier (4:03)
06.05b Build A Boosted Trees Classifier (4:37)
06 Source Files
07 Gradient Boosted Regression
07.01 Build Input Functions (3:55)
07.02 Build A Boosted Trees Regressor (3:02)
07.03 Train And Evaluate The Model (4:07)
07 Source Files
08 Supervised Learning Introduction
01 What You'll Learn (8:47)
02 What Is Supervised Learning (14:41)
03 Build Models On The Web (5:06)
Source Files
Data Science with Stocks, Excel and Machine Learning - 00 Overview
00.00 Course Overview (5:43)
00 Source Files
01 Project_ Track Stocks in Excel
01.00 What You'll Learn (2:01)
01.01 Pull In Stock Data (8:21)
01.02 Pull In More Stock Information (5:08)
01.03 Calculate Equity And Returns (11:56)
01.04 Calculate Selling Strategy (9:25)
01.05 Calculate Total Returns (4:28)
01 Source Files
02A Other Techniques of Stock Prediction in Excel
02.01 Pull Historical Stock Data (2:31)
02.02 Predict Stocks With Moving Average (9:34)
02.03 Visualize Accuracy (3:48)
02.04 What Is Exponential Smoothing (4:15)
02.05 Predict Stocks With Exponential Smoothing (7:37)
02 Source Files
02B Linear Regression on Stock Data in Excel
02.00 What You'll Learn-1 (1:46)
02.01 Pull Historical Stock Data-2 (5:49)
02.02 What Is Linear Regression-3 (4:45)
02.03 Linear Regression On Stock Data In Excel-4 (8:04)
02.04 Check Accuracy Of Linear Regression-5 (12:53)
02b Source Files
03A Machine Learning Project Introduction
03.00 What You'll Learn-1 (2:01)
03.01 Build Models On The Web-2 (5:05)
03.02 What Libraries Will We Use-3 (5:56)
Source Files
03B Your First Machine Learning Stock Prediction Project
03.01 Scrape Data Via Api-1 (16:42)
03.02 Define Variables-2 (11:36)
03.03 Split Dataset For Training And Testing-3 (7:33)
03.04 Build A Linear Regression Model-4 (9:16)
03.05 Predict Stock Prices-5 (10:14)
03.06 Calculate Model Accuracy-6 (14:17)
03.07 Predict To Buy Or To Sell-7 (7:23)
03 Source Files
04 Deep Learning Project for Stock Market Prediction
04.00 Recurrent Neural Networks-1 (6:23)
04.01 Import Stock Data-2 (9:19)
04.02 What Is Shaping Data-3 (5:18)
04.03 Shape Training And Testing Data-4 (12:06)
04.04 What Is Scaling Data-5 (6:35)
04.05 Scale Data For Training-6 (11:24)
04.06 What Is Keras-7 (3:24)
04.07 Build A Keras Model-8 (14:03)
04.08 Scale And Shape Data For Testing-9 (5:33)
04.09 Test The Model-10 (5:15)
04 Source Files
Building Neural Networks - Linear Algebra for Deep Learning
01 Scalar-1 (4:40)
02 Vector-2 (7:36)
03 Matrix-3 (8:21)
04 Tensor-4 (6:51)
Source Files
08 Matrix Operations
01 Matrix-matrix Addition (4:53)
02 Matrix-scalar Addition (1:59)
03 Matrix-scalar Multiplication (2:06)
04 Matrix Multiplication (2:34)
Source Files
09 Build a Neural Network from Scratch
09.01 What Is A Neural Network (8:02)
09.02 Prepare Data (8:31)
09.03 Shuffle And Batch Data (3:26)
09.04 Build Weights And Biases (6:25)
09.05 Build A Neural Network From Scratch (5:28)
09.06 Optimize The Model (10:20)
09.07 Train And Evaluate The Model (11:36)
09.08 Test And Visualize The Neural Network (9:57)
09 Source Files
11 Build a Convolutional Neural Network from Scratch
11.01 What Is A Convolutional Neural Network-1 (4:32)
11.02 Prepare Data For A Convolutional Neural Network-2 (4:09)
11.03 Shuffle And Batch Data-3 (2:17)
11.04 Build Weights And Biases-4 (8:48)
11.05 What Are Wrappers-5 (18:09)
11.06 Build A Convolutional Neural Network From Scratch-6 (9:57)
11.07 What Is The Adam Optimizer-7 (13:20)
11.08 Train And Evaluate The Model-8 (10:32)
11.09 Test And Visualize The Convolutional Neural Network-9 (7:49)
11 Source Files
12 Convolutional Neural Networks CIFAR-Image Classification
Source Files
03 Define Classes And Visualize Dataset-3 (8:31)
02 Normalize Image Values-2 (2:21)
12.04 Build A CNN For CIFAR-Image Classification
04a 2D Convolution Layer (10:14)
04b Relu Activation Function (6:40)
04c 2D Max Pooling Layer (9:34)
04d Flatten And Dense Layers (5:33)
04e Build A CNN For CIFAR-image Classification (13:42)
Source Files
12.05 Optimization and Loss
05a How Do You Build An Optimizer For Cifar-image Classification-1 (12:53)
05b How Do You Calculate Loss For Cifar-image Classification-2 (12:05)
05c Build An Optimizer For Cifar-image Clasification-3 (3:01)
Source Files
12.06 Train And Visualize CIFAR-Image Classification
06 Train The CNN For CIFAR-image Classification (8:20)
07 Evaluate And Visualize The CNN (8:07)
Source Files
13 Build a Recurrent Neural Network
13.01 What Is A Recurrent Neural Network-1 (4:58)
13.02 Prepare Data For A Recurrent Neural Network-2 (7:25)
13.03 Shuffle And Batch Data-3 (2:43)
13.04 Build A Recurrent Neural Network-4 (7:42)
13.05 Calculate Accuracy And Loss-5 (4:53)
13.06 Optimize The Neural Network-6 (5:08)
13.07 Train A Recurrent Neural Network-7 (6:09)
13 Source Files
14 Build a Dynamic Recurrent Neural Network
14.01 What Is A Dynamic Neural Network (6:09)
14.02 Generate Sample Data (13:39)
14.03 Shuffle And Batch Data (4:23)
14.04 Build A Dynamic Neural Network (7:34)
14.05 Calculate Accuracy And Loss (5:15)
14.06 Optimize The Neural Network (7:29)
14.07 Train A Dynamic Neural Network (11:55)
14 Source Files
15 Build a Bi-directional Recurrent Neural Network
15.01 What Is A Bi-directional Neural Network (5:46)
15.02 Prepare Data For A Bi-directional Neural Network (8:54)
15.03 Build A Bi-directional Neural Network (8:43)
15.04 Calculate Accuracy And Loss (5:51)
15.05 Optimize The Bi-directional Rnn (5:29)
15.06 Train A Recurrent Neural Network (6:44)
15 Source Files
16 Prepare Data For Image Segmentation
01 Load Data For Image Segmentation (6:01)
02 Normalize Images (2:38)
03 Load Training Images (7:11)
04 Load Testing Images (4:38)
05 Prepare Data For Image Segmentation (6:25)
06 Visualize Images And Masks (5:20)
Source Files
17 Build A Neural Network For Image Segmentation
01 How Do You Build A Neural Network For Image Segmentation (10:04)
02 Set Up A Neural Network (6:36)
03 Build Neural Network Layers (6:32)
04 Compile Optimizer And Loss (2:07)
Source Files
18 Train And Test Image Segmentation
01 Build A Mask (1:34)
02 Visualize Model Progress (5:13)
03 Visualize Model Results (13:36)
04 Plot Model Accuracy (7:50)
05 Test The Neural Network (3:37)
Source Files
19 Word2Vec Sentiment Classification of Words
00 What Is Word2vec (5:19)
Source Files
20 Prepare Data for Word2Vec Sentiment Classification
01 Load Data For Word2vec (7:37)
02 Build Datasets For Word2vec (5:56)
03 Cache And Prefetch Data For Word2vec (1:56)
Source Files
21 Build Layers for Word2Vec
00 How Do You Build An Embedding Layer (2:06)
01 Build A Word2vec Embedding Layer (2:35)
02 Clean Data For Word2vec (4:29)
03 How Do You Vectorize Text (10:20)
04 How Do You Build A Word2vec Sequential Layer (27:14)
Source Files
22 Train And Test Word2Vec
01 Optimizer And Loss For Word2vec (2:24)
02 Train And Test The Word2vec Model (2:50)
03 Visualize Word Embeddings (9:02)
Source Files
Computer Vision and Deep Learning with OpenCV and Python - Overview
01 Course Overview - Opencv (4:51)
02 What You'll Need (2:38)
03 Analyze Images with OpenCV
01 Detect Edges In An Image (8:19)
02 Detect Contours In An Image (11:23)
03 Detect Corners In An Image (9:37)
04 Restore Images with Computational Photography
01 Restore A Damaged Image (18:57)
05 Detect objects in images
01 Detect Objects In An Image With Masking (15:39)
02 Detect Faces In Images (11:31)
03 Extract Foreground In An Image (18:30)
04 Find Object In Image With Template Matching (12:27)
06 Machine learning and neural networks introduction
01 What Is Machine Learning (5:26)
02 What Is Deep Learning (7:42)
03 What Is A Neural Network (8:47)
04 What Is Ml-Agents (5:16)
07 Read text in an image with OCR and Tesseract
01 Extract Text From An Image With Tesseract (13:30)
02 Improve Accuracy With Thresholding (8:10)
03 Change Perspective Of An Image With Foreign Text (15:30)
04 Extract Foreign Language Text From An Image (8:03)
08 Build your first neural network with OpenCV
01 Generate Data (7:08)
02 Build An Artificial Neural Network (9:16)
03 Visualize Model Results (14:30)
09 Object detection with OpenCV Deep Learning
01 Load Yolo Dnn Model (3:18)
02 Build A Neural Network With Opencv (7:44)
03 Print Out Detected Objects (6:44)
04 Outline Objects In The Original Image (21:57)
10 Outline objects in a video
01 Outline Objects In A Video (10:55)
02 Draw Contours On Video (16:30)
03 Save New Frames As A Video (5:04)
11 Detect faces in video
01 Load A Video From Drive (7:39)
02 Detect Faces In Video (10:31)
03 Detect Eyes In Video (6:40)
04 Save New Frames As A Video (7:40)
12 Track a color in videos
02 Save New Frames As A Video (7:14)
01 Track Color In A Video (20:06)
13 Detect lanes for autonomous vehicle computer vision
01 Load A Driving Dash Cam Video (4:04)
02 Process Each Video Frame (14:54)
03 Outline Lanes Detected (12:21)
04 Save New Frames As A Video (13:52)
14 Build a motion alert video monitoring system
01 Load A Video From Drive (5:28)
02 Detect Objects In A Video With Contours (10:05)
03 Detect When Motion Begins And Ends (15:17)
04 Record Each Time Motion Begins (16:35)
15 Detect emotion in a video
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