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Python & TensorFlow: Deep Dive into Machine Learning
Introduction to Machine & Deep Learning
1.1 Types of Machine Learning (3:11)
1.0 What is Machine Learning (2:41)
1.2 Machine Learning Applications (2:56)
1.3 What is Deep Learning (4:09)
Basics of TensorFlow & Installation
2.0 What is TensorFlow (4:30)
2.1 Installing TensorFlow on a Working Environment (3:14)
2.2 TensorFlow Architecture (4:23)
2.3 A Refresher on APIs (8:01)
2.4 TensorFlow APIs (3:37)
Machine Learning Part 1: Supervised Learning
3.1 What is supervised Learning (2:55)
3.2 Linear regression (9:52)
3.3 Logistic Regression (13:05)
3.4 Decision Tree (7:41)
3.5 Random Forests (8:05)
3.6 Support Vector Machine (SVM) (5:16)
Machine Learning Part 2: Unsupervised Learning
4.0 What is unsupervised learning (9:07)
4.1 K-Means Clustering (5:56)
4.2 Hierarchical Clustering (6:29)
4.3 Principal Component Analysis (3:48)
Deep Learning Basics with Tensorflow: Neural Networks
5.2.1 Basic Neural Network(BNN) (5:07)
5.1 What is neural Network (3:39)
5.2.2 Convolutional Neural Networks (CNNs) (5:38)
5.3 Recurrent Neural Networls (RNNs) (4:00)
5.4 Building A Deep Neural Network (4:37)
Model Evaluation & Optimization
6.1 Model Evaluation Metrics (5:02)
6.0 Training and Testing Data (3:55)
6.2 Overfitting and Underfitting (7:11)
6.3 Hyperparameter Tuning (4:20)
TensorFlow for Production
7.2 Deploying TensorFlow models (4:06)
7.1 Saving and restoring models (4:09)
7.3 Distributed TensorFlow (4:30)
7.4 TensorBoard for Visualization and Debugging (5:58)
Project: Image Classification
8.1 Project - Image Classification (5:26)
Conclusion
9.1 Conclusion (4:49)
7.2 Deploying TensorFlow models
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