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TensorFlow Mastery: Constructing Dynamic Machine Learning Models
Mammoth Interactive Courses Introduction
Preface
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
Intro to Tensorflow 2
00. Course Intro (6:10)
01. Intro to Tensorflow (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
Image Recognition with MNIST
00. Course Intro (6:57)
01. Intro To Image Recognition (14:07)
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 (3:40)
Source Files
Machine Learning: Beginners - Build Beginner Models in TensorFlow 2.0
00. Course Overview (3:30)
01. Build Models On The Web (5:06)
Source Files
Machine Learning: Beginners - AI Uninformed Search Algorithms
00. What Are Search Algorithms (7:20)
01. Depth First Search (9:00)
02. Build A Depth First Search Algorithm (8:26)
03. What Is Breadth First Search (bfs) (5:08)
04. Build A Breadth First Search Algorithm (6:55)
05. Depth Limited Search (3:57)
06. Iterative Deepening Depth First Search (5:32)
07. What Is Uniform Cost Search (6:04)
08. Build A Uniform Cost Search Algorithm (8:07)
10. Bidirectional Search (4:43)
Source Files
Machine Learning: Beginners - AI Informed Search Algorithms
00. What Are Informed Search Algorithms (4:07)
01. What Is Greedy Best-first Search (8:16)
02. Build A Greedy Best First Search Algorithm (10:43)
03. What Is A Search (5:10)
Source Files
Machine Learning: Beginners - How Machine Learning Works
00. How Does A Machine Learning Agent Learn (7:37)
01. What Is Inductive Learning (4:10)
02. Make Decisions With Decision Trees (10:49)
03. Performance Of A Machine Learning Algorithm (4:13)
04. Handle Noise In Data (5:20)
05. Statistical Learning (3:56)
Source Files
Machine Learning: Beginners - Logistic Regression
00. What Is Logistic Regression (4:26)
01. How To Prepare Data (8:52)
02. Prepare Data For Logistic Regression (12:19)
03. Build A Logistic Regression Model (5:29)
04. How To Build A Logistic Regression Model (3:28)
05. What Is Optimization (12:10)
06. How To Optimize A Logistic Regression Model (12:45)
07. Optimize The Logistic Regression Model (12:44)
08. Train The Model (10:09)
09. Test The Model (2:33)
10. Visualize Results (5:38)
Source Files
Machine Learning: Beginners - Gradient Boosted Classification
00. What Is Gradient Boosting (1:54)
01. Prepare Data For Gradient Boosted Classification (7:19)
02. Build Binary Classes (6:12)
03. How To Shape Data For Classification (2:57)
04. Shape Data For Classification (7:06)
05. How To Build A Boosted Trees Classifier (4:03)
06. Build A Boosted Trees Classifier (4:37)
Source Files
Machine Learning: Beginners - Gradient Boosted Regression
00. Build Input Functions (3:55)
01. Build A Boosted Trees Regressor (3:02)
02. Train And Evaluate The Model (4:07)
Source Files
Machine Learning Intermediate - Supervised Learning Introduction
00. What You'll Learn (8:47)
01. What Is Supervised Learning (14:41)
02. Build Models On The Web (5:06)
Source Files
01. Build A Boosted Trees Regressor
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