Machine Learning
*Giving Computers the Ability to Learn from Data
*Training Simple Machine Learning Algorithms
for Classification
*A Tour of Machine Learning Classifiers
Using scikit-learn
*Building Good Training Sets – Data Preprocessing
*Compressing Data via Dimensionality Reduction
*Learning Best Practices for Model Evaluation and
Hyperparameter Tuning
*Combining Different Models for Ensemble Learning
*Applying Machine Learning to Sentiment Analysis
*Embedding a Machine Learning Model into a
Web Application
*Predicting Continuous Target Variables
with Regression Analysis
*Working with Unlabeled Data – Clustering Analysis
*Implementing a Multilayer Artificial Neural
Network from Scratch
*Parallelizing Neural Network Training
with TensorFlow
*Going Deeper – The Mechanics of TensorFlow
*Classifying Images with Deep Convolutional
Neural Networks
*Modeling Sequential Data Using Recurrent
Neural Networks
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