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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