Machine Learning & Deep Learning Essentials with Spark and TensorFlow (TTML5508)

Course Outline

Part 1: Introduction to Machine Learning

Machine Learning (ML) Overview

• Machine Learning landscape
• Machine Learning applications
• Understanding ML algorithms & models

ML in Python and Spark

• Spark ML Overview
• Introduction to Jupyter notebooks
• Lab: Working with Jupyter + Python + Spark
• Lab: Spark ML utilities

Machine Learning Concepts

• Statistics Primer
• Covariance, Correlation, Covariance Matrix
• Errors, Residuals
• Overfitting / Underfitting
• Cross-validation, bootstrapping
• Confusion Matrix
• ROC curve, Area Under Curve (AUC)
• Lab: Basic stats

Feature Engineering (FE)

• Preparing data for ML
• Extracting features, enhancing data
• Data cleanup
• Visualizing Data
• Lab: data cleanup
• Lab: visualizing data

Linear Regression

• Simple Linear Regression
• Multiple Linear Regression
• Running LR
• Evaluating LR model performance
• Lab
• Use case: House price estimates

Logistic Regression

• Understanding Logistic Regression
• Calculating Logistic Regression
• Evaluating model performance
• Lab: Use case: credit card application, college admissions

Classification: SVM (Supervised Vector Machines)

• SVM concepts and theory
• SVM with kernel
• Lab: Use case: Customer churn data

Classification: Decision Trees & Random Forests

• Theory behind trees
• Classification and Regression Trees (CART)
• Random Forest concepts
• Labs: Use case: predicting loan defaults, estimating election contributions

Classification: Naive Bayes

• Theory
• Lab
• Use case: spam filtering

Clustering (K-Means)

• Theory behind K-Means
• Running K-Means algorithm
• Estimating the performance
• Lab: Use case: grouping cars data, grouping shopping data

Principal Component Analysis (PCA)

• Understanding PCA concepts
• PCA applications
• Running a PCA algorithm
• Evaluating results
• Lab: Use case: analyzing retail shopping data

Recommendations (Collaborative filtering)

• Recommender systems overview
• Collaborative Filtering concepts
• Lab: Use case: movie recommendations, music recommendations

Performance 

• Best practices for scaling and optimizing Apache Spark
• Memory caching
• Testing and validation

Part Two: Introduction to Deep Learning with TensorFlow

Introducing TensorFlow

• TensorFlow intro
• TensorFlow Features
• TensorFlow Versions
• GPU and TPU scalability
• Lab: Setting up and Running TensorFlow

The Tensor: The Basic Unit of TensorFlow

• Introducing Tensors
• TensorFlow Execution Model
• Lab: Learning about Tensors

Single Layer Linear Perceptron Classifier with TensorFlow

• Introducing Perceptrons
• Linear Separability and Xor Problem
• Activation Functions
• Softmax output
• Backpropagation, loss functions, and Gradient Descent
• Lab: Single-Layer Perceptron in TensorFlow

Hidden Layers: Intro to Deep Learning

• Hidden Layers as a solution to XOR problem
• Distributed Training with TensorFlow
• Vanishing Gradient Problem and ReLU
• Loss Functions
• Lab: Feedforward Neural Network Classifier in TensorFlow

High level TensorFlow: tf.learn

• Using high level TensorFlow
• Developing a model with tf.learn
• Lab: Developing a tf.learn model

Convolutional Neural Networks in TensorFlow

• Introducing CNNs
• CNNs in TensorFlow
• Lab : CNN apps

Introducing Keras

• What is Keras?
• Using Keras with a TensorFlow Backend
• Lab: Example with a Keras

Recurrent Neural Networks in TensorFlow

• Introducing RNNs
• RNNs in TensorFlow
• Lab: RNN

Long Short-Term Memory (LSTM) in TensorFlow

• Introducing RNNs
• RNNs in TensorFlow
• Lab: RNN

Conclusion

• Summarize features and advantages of TensorFlow
• Summarize Deep Learning and How TensorFlow can help
• Next steps