This Course Focusses on Predictive and Prescriptive Analytics in the Analytics Evolution Road Map Given Below

Descriptive

Explains what happened

Diagnostic

Explains why it happened.

Predictive

Forecasts what might happen

Prescriptive

Recommends an action based on the forecast.

Why Data science & Machine Learning for Predictive Analytics ?

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

Program – Week by Week Plan

5 Week Plan

Week 1

Python Essentials

Python Essentials for DS/ML Needs

Statistics

Week 2
Regression Techniques and Modelling.
Week 3

ML - Basics

Basics of Machine learning. Supervised and Unsupervised Learning

Deep dive in to Algorithms

Week 4
Decision Trees, KNN, NAIVE BAYES
Week 5

Projects / Case Studies

Real Time Case Studies

Detailed Course Structure

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

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