-Day 1-
A. Intro to Python libraries for Machine Learning
○ Numpy
○ Pandas
○ Scikit-learn
B. Intro to Machine Learning
○ Supervised Learning
■ Linear Regression
■ Logistic Regression
■ Support Vector Machines
■ Decision Trees
■ Naive Bayes
○ Unsupervised Learning
■ K-means clustering
■ Hierarchical clustering
■ Principal Component Analysis
○ Semi-supervised Learning Algorithm
-Day 2-
C. Machine Learning Model Optimization
D. Intro to Neural Networks
○ Build a Neural Network from scratch
○ Tensorflow 2.0
E. Deep Neural Networks
○ Transfer Learning and Fine Tuning
○ Convolutional Neural Networks
○ Recurrent Neural Networks
○ Generative Adversarial Networks (Optional)
-Day 3-
F. Machine Learning with Azure
○ Intro to Azure
○ Intro to ML Studio
○ Training Machine Learning models in Azure
○ Model Validation
○ Auto ML
○ Registering models
○ Deploying models
■ Building REST APIs and testing with Swagger UI