Applied Data Science & Machine Learning

Day 1

What is Data Science? How is it applied in business?

Mathematics Primer for Machine Learning

• Linear Algebra
• Vector & Tensor Calculus
• Probability
• Bayesean Statistics

Day 2

Introduction to the Data Science Tech Stack (Python)

Introduction to the World of Machine Learning

• Applications
• Requirements
• Supervised Learning
• Unsupervised Learning

Making the Leap from Bayesean Statistics to Machine Learning

• Naive Bayes Classifiers (theory)

Day 3

Naive Bayes Classifiers (continued)

• Naive Bayes Classifiers (implementation)

K-Nearest Neighbors

• K-NNs for Classification (theory)
• K-NNs for Classification (implementation)
• K-NNs for Regression (theory)
• K-NNs for Regression (implementation)

Day 4

Linear Regression

• Simple Linear Regression (theory)
• Simple Linear Regression (implementation)
• Multiple Linear Regression (theory)
• Multiple Linear Regression (implementation)
• Generalization of Linearity (theory)
• Generalization of Linearity (implementation)

Day 5

Linear Regression (continued)

• Model Optimization (Estimator Bias & Variance)
• Regularization (theory)
• Regularization (implementation)
• Cross Validation & Model Selection
• Vectorized (Multiple) Linear Regression

Day 6

Logistic Regression

• A Mathematical Model of the Biological Neuron
• The Probabilistic Perspective of Logistic Regression
• Binary Logistic Regression (theory)
• Binary Logistic Regression (implementation)
• Generalization of Linearity (theory)
• Generalization of Linearity (implementation)

Day 7

Logistic Regression (continued)

• One vs. All - Generalizing Beyond Binary Classification (theory)
• One vs. All (implementation)
• Regularization (theory)
• Regularization (implementation)

Day 8

Artificial Neural Networks

• Neurons to Neural Networks
• Feed-Forward Neural Network Architecture (theory)
• Feed-Forward Neural Network Architecture (implementation)
• The Back Propagation Algorithm (theory)
• The Back Propagation Algorithm (implementation)

Day 9

Deep Learning

• Deep Neural Networks (theory)
• Deep Neural Networks (implementation)
• Regularization for Neural Networks (theory)
• Regularization for Neural Networks (implementation)
• Adaptations for Big Data

Day 10

Modern Optimization in Neural Networks

• Momentum in Back Propagation (theory)
• Momentum in Back Propagation (implementation)
• Nesterov Momentum (theory)
• Nesterov Momentum (implementation)
• Adaptive Momentum - AdaM (theory)
• Adaptive Momentum - AdaM (implementation)
• Dropout & Reverse Dropout (theory)
• Dropout & Reverse Dropout (implementation)

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