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Build Machine Learning Models and Neural Networks - Total Mastery Bundle
Intro to Tensorflow
Intro to Tensorflow - Source Files
00. Course Intro (6:10)
01. Intro To Tensorflow (5:33)
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 (12:43)
08. Linear Regression Model - Testing The Model (5:22)
09. Summary And Outro (2:55)
Image Recognition with MNIST
Source Files
00. Course Intro (6:57)
01. Intro to Image Recognition (14:07)
02. Intro to MNIST (4:42)
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 (13:24)
09. Summary And Outro (2:55)
Build Beginner Models in TensorFlow 2.0
Source Files
00. Course Overview (3:30)
01. Build Models On The Web (5:06)
AI Uninformed Search Algorithms
Source Files
01. What Are Search Algorithms (7:21)
02. Depth First Search (9:00)
03. Build A Depth First Search Algorithm (8:26)
04. What Is Breadth First Search (bfs) (5:08)
05. Build A Breadth First Search Algorithm (6:56)
06. Depth Limited Search (3:58)
07. Iterative Deepening Depth First Search (5:32)
08. What Is Uniform Cost Search (6:04)
09. Build A Uniform Cost Search Algorithm (8:07)
10. Bidirectional Search (4:44)
AI Informed Search Algorithms
Source Files
01. What Are Informed Search Algorithms (4:07)
02. What Is Greedy Best-first Search (8:16)
03. Build A Greedy Best First Search Algorithm (10:43)
04. What Is A Search (5:10)
How Machine Learning Works
Source Files
01. How Does A Machine Learning Agent Learn (7:37)
02. What Is Inductive Learning (4:10)
03. Make Decisions With Decision Trees (10:50)
04. Performance Of A Machine Learning Algorithm (4:13)
05. Handle Noise In Data (5:20)
06. Statistical Learning (3:56)
Logistic Regression
Source Files
01. What Is Logistic Regression (4:26)
02. Prepare Data For Logistic Regression (12:19)
03. How To Prepare Data (8:52)
04. Build A Logistic Regression Model (5:29)
05. How To Build A Logistic Regression Model (3:28)
06. What Is Optimization (12:10)
07. Optimize The Logistic Regression Model (12:44)
08. How To Optimize A Logistic Regression Model (12:45)
09. Train The Model (10:09)
10. Test The Model (2:33)
11. Visualize Results (5:38)
Gradient Boosted Classification
Source Files
1. What Is Gradient Boosting (1:54)
2. Prepare Data For Gradient Boosted Classification (7:19)
3. Build Binary Classes (6:12)
4. How To Shape Data For Classification (2:58)
5. Shape Data For Classification (7:06)
6. How To Build A Boosted Trees Classifier (4:03)
7. Build A Boosted Trees Classifier (4:37)
Gradient Boosted Regression
Source Files
1. Build Input Functions (3:55)
2. Build A Boosted Trees Regressor (3:02)
3. Train And Evaluate The Model (4:07)
07. Building A Cnn Part 5 - Train And Test The Model
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