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Module 01: Introduction to Machine & Deep Learning | |||
What is Machine Learning? | 00:03:00 | ||
Types of Machine Learning | 00:03:00 | ||
Applications of Machine Learning | 00:03:00 | ||
What is Deep Learning? | 00:04:00 | ||
Module 02: Basics of TensorFlow & Installation | |||
What is TensorFlow? | 00:05:00 | ||
Installing and Setting up TensorFlow | 00:03:00 | ||
TensorFlow Architecture | 00:04:00 | ||
A refresher on APIs | 00:08:00 | ||
TensorFlow APls | 00:04:00 | ||
Module 03: Machine Learning Part 1: Supervised Learning | |||
What is Supervised Learning? | 00:03:00 | ||
Linear Regression | 00:10:00 | ||
Logistic Regression | 00:13:00 | ||
Decision Trees | 00:08:00 | ||
Random Forests | 00:08:00 | ||
Support Vector Machines (SVMs) | 00:05:00 | ||
Module 04: Machine Learning Part 2: Unsupervised Learning | |||
What is Unsupervised Learning? | 00:09:00 | ||
K-Means Clustering | 00:06:00 | ||
Hierarchical Clustering | 00:06:00 | ||
Principal Component Analysis (PCA) | 00:04:00 | ||
Module 05: Deep Learning Basics with Tensorflow: Neural Networks | |||
What are Neural Networks? | 00:04:00 | ||
Basic Neural Networks | 00:05:00 | ||
Convolutional Neural Networks (CNNs) | 00:06:00 | ||
Recurrent Neural Networks (RNNs) | 00:04:00 | ||
Building Deep Neural Networks | 00:00:00 | ||
Module 06: Model Evaluation & Optimization | |||
Training and Testing Data | 00:04:00 | ||
Model Evaluation Metrics | 00:05:00 | ||
Overfitting and Underfitting | 00:07:00 | ||
Hyperparameter Tuning | 00:04:00 | ||
Module 07: TensorFlow for Production | |||
Saving and restoring models | 00:04:00 | ||
Deploying TensorFlow models | 00:04:00 | ||
Distributed TensorFlow | 00:04:00 | ||
TensorBoard for visualization and debugging | 00:06:00 | ||
Module 08: Project: Image Classification | |||
ML Project: Image Classification Model | 00:05:00 | ||
Module 09: Conclusion | |||
Conclusion | 00:05:00 | ||
Order Your Certificates & Transcripts | |||
Order your Certificates & Transcripts | 00:00:00 |
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