**Descriptive**

Explains what happened

**Diagnostic **

Explains why it happened.

**Predictive**

Forecasts what might happen

**Prescriptive**

Recommends an action based on the forecast.

Machine Learning (ML), globally recognized as a key driver of digital transformation,will be responsible for cumulative investments of $58 billion by the end of 2021

The global ML industry, growing at a CAGR of 42 percent, will be worth almost $9 billion in the latter part of 2022.

The neural networks market will be worth over $23 billion in 2024

The Deep Learning (DL) applications market in the US alone has been predicted to shoot from $100 million in 2018 to $935 million in 2025.

# Program Overview

**Machine Learning Ninja Program is an Eye-opener program and** will provide a guide-map to traverse the complex world of Data Science , Artificial Intelligence ,Neural Networks & Deep Learning . In 10 days (5 weekends ), Students will be able to understand the vast potential ,they are entering in to and would be able to device a strategy to convert their learning to shape up their careers.

### Top Skills You Will Learn

NLP, Deep Learning, Reinforcement Learning and Graphical Models along with a solid foundation in Predictive Analytics and Statistics

### Job Opportunities

Business Analyst, Product Analyst, Machine

Learning Engineer, Data Scientist

### Who Is This Program For?

Engineers, Software and IT Professionals,

Data Professionals

### Minimum Eligibility

Bachelor’s Degree with minimum 1 year of

work experience or a degree in Mathematics or Statistics

## 5 Week Plan

## Python Essentials

Python Essentials for DS/ML Needs

## Statistics

Regression Techniques and Modelling.

## ML - Basics

Basics of Machine learning. Supervised and Unsupervised Learning

## Deep dive in to Algorithms

Decision Trees, KNN, NAIVE BAYES

## Projects / Case Studies

Real Time Case Studies

## Introduction to Data Science and Python Basics

- What is analytics & Data Science?
- Common Terms in Analytic
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics?
- Critical success drivers
- Overview of analytics tools & their popularity
- Why Python for data science?
- Overview of Python- Starting with Python
- Introduction to installation of Python
- Understand Jupyter notebook & Customize Settings
- Input/Output basics
- Data Types and Assignment Operator
- Mathematical Expressions Exercise
- IF and Else, Strings Manipulation and Slicing
- For Loops
- Functions
- Lists, Tuples and Dictionaries

## Data Analysis and Intro to Machine Learning

- Data Analysis Use Case 2.
- Supervised vs Unsupervised Learning
- Machine Learning vs Deep Learning
- Introduction – Applications
- Assumptions of Linear Regression
- Getting started with Univariate Analysis using Sci-Kit Learn

## Logistic Regression

- Introduction – Applications
- Linear Regression Vs. Logistic Regression
- Building Logistic Regression Model (Binary Logistic Model)
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Accuracy, Precision, Recall
- Confusion Metrics
- ROC-AUC Curve
- Business Validation – Implementation on new data

## Ensemble Learning

- What is Ensemble Learning
- Bagging and Boosting
- Random Forest
- OOB Score
- Adaboost
- Gradient Boosted Trees
- Implementation of Random Forest using Gradient Boosted Trees and Random Forest
- Intorduction to Deep Learning
- Working and understanding of a Neural Network

## Python Basics Continued

- Recursion
- Regular Expression
- Date Time manipulation
- Arrays and Numpy
- Series
- Classes
- Inheritance
- Mini Python Project – Web Scraping

## Introduction To Statistics & Data Visualization

- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampe vs Population – Concept of Hypothesis Testing
- Covariance, Pearson Collinear, Standardization and Normalization
- Why standardization is important
- Statistical Methods – Z/t-tests( One sample, independent, paired),Correlations and Chi-square
- Line plot, bar plot, Histogram, Scatter Plot
- Five Point Summary, IQR
- Introduction to Pandas
- Data Analysis on a used Case 1

## Linear Regression

- Building Linear Regression Model
- Assess the overall effectiveness of the model
- Understanding the goodness of the fit (Variable significance, R-square/Adjusted R-squareetc)
- Evaluating the Model (Absolute Mean Error, Mean Square Error, Root Mean Square Error)
- Multivariate Analysis
- Bias-Variance Tradeoff
- Overfitting vs Underfitting

## K-Means Clustering, KNN, Decision Trees

- Sampling Bias
- Cross Validation
- K-Nearest Neighbors
- Same Use Case for KNN
- Hyper-Parameter tuning using KNN
- K-Means Clustering
- Principal Component Analysis
- Introduction to Decision Trees
- Working of a Decision Trees