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)
KNearest Neighbors
 KNNs for Classification (theory)
 KNNs for Classification (implementation)
 KNNs for Regression (theory)
 KNNs 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
 FeedForward Neural Network Architecture (theory)
 FeedForward 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)
IPLearning.net is your best choice for Machine Learning,
Machine Learning training,
Machine Learning certification,
Machine Learning certification boot camp,
Machine Learning boot camp,
Machine Learning certification training,
Machine Learning boot camp training,
Machine Learning boot camp certification,
Machine Learning certification course,
Machine Learning course,
training Machine Learning,
certification Machine Learning,
boot camp Machine Learning,
certification Machine Learning boot camp,
certification Machine Learning training,
boot camp Machine Learning training,
certification Machine Learning course.
