Corse Intraduction
This program will expose the participants to various techniques in predictive modelling and machine learning using Python. The program will start with the application of statistics using Excel and then proceed into different aspects of Machine Learning. Upon completion, the participants will be comfortable with topics in Supervised, Unsupervised and Reinforcement Learning.

Duration : 5 Days

Objectives

• Understand the role of descriptive and inferential statistics
• Understand how to apply feature engineering techniques
• Implement regression and classification techniques using Python
• Implement unsupervised learning techniques
• Understand how to measure model performance
• Understand how to build ANN and SVM Models
• Understand how to implement Deep Learning algorithms
• Implement Text Mining using Python

Prerequisites

• Anaconda for Python 3 must be installed
• Basic Python programming is mandatory
• Proficient in Numpy and Pandas
• Basics of Statistics

Day 1 : Applied Statistcs

• Introduction
• Objectives
• Mean, Median and Mode
• Variance and Standard Deviation
• Covariance and Correlation
• Sampling
• Hypothesis Testing
• T-test, F-test and Chi-Square test
• P-value
• Type 1 and Type 2 errors

Day 2 : Regression Models

• Feature Engineering
• Simple Linear Regression
• Multiple Regression
• Polynomial Regression
• R-Square
• Adjusted R-Square
• ANOVA Table

Day 3 : Classification Models

• KNN
• Naïve Bayes
• Gini, Entropy and Information Gain
• Decision Trees
• Logistic Regression
• Bagging
• Boosting
• Random Forests

Day 4 : Unsupervised Learning and Text Mining

• Clustering
• PCA
• Text Processing
• Text Mining models
• Artificial Neural Networks
• Support Vector Machine

Day 5 : Deep Learning

• Introduction to Deep Learning
• CNN
• RNN
• SOM
• Auto Encoders
• LSTM
• GAN