About This Course
Course Curriculum
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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
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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
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What is Supervised Learning?
00:03:00 -
Linear Regression
00:10:00 -
Logistic Regression
00:13:00 -
Decision Trees
00:01:00 -
Random Forests
00:08:00 -
Support Vector Machines (SVMs)
00:05:00
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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
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What are Neural Networks?
00:04:00 -
Basic Neural Networks
00:05:00 -
Convolutional Neural Networks (CNNs)
00:07:00 -
Recurrent Neural Networks (RNNs)
00:04:00 -
Building Deep Neural Networks
00:05:00
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Training and Testing Data
00:04:00 -
Model Evaluation Metrics
00:05:00 -
Overfitting and Underfitting
00:07:00 -
Hyperparameter Tuning
00:04:00
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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
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ML Project: Image Classification Model
00:05:00
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Conclusion
00:05:00