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