Machine Learning through Python & R

This 2-day training program develops a comprehensive foundation for Machine Learning using Python and R through its associated libraries. It is not only a hands-on course but also helps in developing the understanding of underpinning statistical methods involved. It covers all aspects of machine learning’s building blocks that involves data extraction, choosing appropriate model, model fine tuning, and result validation with variety of cutting-edge libraries available today. Specifically, the course will cover Python/R basics, fundamentals of machine learning, data handling and preparation and ML technique application.

After completing this 4-day machine learning program, participants should be able to:

• Navigate the interactive data science environment JuPyteR for Python.
• Import and Export data from variety of sources in Python and R ecosystem.
• Apply appropriate model using Python/R libraries.
• Architect data pipeline for machine learning products.
• Conceptualize and kick start machine learning projects.

Participants are expected to have the latest version of Anaconda, Python, GitHub and JuPyteR labs installed on their laptop. Prior knowledge of statistical concepts will be useful. A lot of enthusiasm and wonderment about what can be achieved through active participation will significantly enhance the learning from the program.

Introduction to Python

• Anaconda and Jupiter notebook basics
• Basic commands in Python
• Data Types and Operations
• Python packages
• Introduction to libraries like NumPy, SciPy, Matplotlib, Pyspark and Pandas

Data Handling in Python

• Data importing
• Working with datasets
• Manipulating the datasets
• Creating new variables
• Exporting the datasets into external files
• Data Merging

Basic Descriptive Statistics

• Measures of Central tendency
• Measures of dispersion
• Probability
• Binomial Distribution
• Normal Distribution
• Hypothesis Testing
• Correlation
• Data exploration / Cleaning / Preparation
• Variable Identification
• Missing value treatment
• Outlier treatment
• Feature Engineering

Data Preparation for Analysis

• Exploratory Data Analysis
• Data Validation rules
• Data cleaning techniques
• Data Preparation for analysis

Regression Analysis in Python

• Correlation
• Simple Regression models
• R-Square
• Multiple regressions
• Multicollinearity
• Individual Variable Impact (VIF)

Logistic Regression

• Need of logistic Regression
• Logistic regression models
• Validation of logistic regression models
• Multicollinearity in logistic regression
• Individual Impact of variables
• Confusion Matrix

Model Selection and Cross validation

• How to validate a model?
• What is a best model?
• Types of data
• Types of errors
• The problem of over fitting
• The problem of under fitting
• Bias Variance Trade-off
• Regularization
• Cross validation
• Boot strapping

Distance Concepts

• Decision trees Classification
• KNN
• LDA/SVM

Unsupervised Learning

• Clustering
• k means
• PCA - Dimensionality Reduction

Introduction to R

• Basic commands in R
• Data Types and Operations
• R packages
• Introduction to R libraries
• Chart Plottng in R

Data Handling in R

• Data importing
• Working with datasets
• Manipulating the datasets
• Creating new variables
• Exporting the datasets into external files
• Data Merging

Data Preparation for Analysis

• Exploratory Data Analysis
• Data Validation rules
• Data cleaning techniques
• Data Preparation for analysis

Regression Analysis in R

• Correlation
• Simple Regression models
• R-Square
• Multiple regressions
• Multicollinearity
• Individual Variable Impact (VIF)

Logistic Regression

• Need of logistic Regression
• Logistic regression models
• Validation of logistic regression models
• Multicollinearity in logistic regression
• Individual Impact of variables
• Confusion Matrix

Showcase of Rattle Program in R

• Solving a pre-existing problem like MTCars.csv or Weather.csv
• Executing Supervised and Unsupervised methodologies on the dataset.

Business Case Studies

• Discussion on the Scope and possibilities within R based Data Science
• A 30 Minute Case Study & Analysis - BFSI/ Telecom
• A 30 Minute Case Study & Analysis - CPG/ Retail

Live Case Application in Data Science

• Discussion on Solutioning possibilities for a Business Problem
• Evolving a simple Data Science solution through a Practical Exercise

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