Program Duration
Duration – 3 months
Training & Practice hours 75+
Program Pedagogy
Version 1  Online, Selfpaced
Version 2  Online, Live training
Batch Starts
Version 1  Selfpaced
Version 2  Batch starts Jan 15, 2022
Program Highlights

Industryoriented curriculum

Industry recognized Certifications

Python /R for Machine Learning

Machine Learning Algorithms

AI Technology

Predictive Modelling

Interview Preparation ML & AI roles
Program Variants
VERSION 1
SELFPACED ONLINE
When can you take
Take anytime, Selfpaced
Certification included
Microsoft Certified: Data Scientist Associate (Training included)
apply nowVERSION 2
LIVE TRAINING ONLINE
When can you take
Batch starts Jan 7, 2022
Certification included
 a.
Certificate from Christ University
 b.
Data Scientist Associate certification from Microsoft Certified (Training & Examination costs included)
SKILLS YOU WILL LEARN
Practical applied training in platforms commonly used for ML and AI
Learn practical concepts and frameworks and apply your knowledge on tools, libraries and platforms for experiential learning

Predictive Modelling

Machine Learning

Big Data

Neural Network

Statistical Modelling

Marketing Analytics

Financial Analytics

Social Media Analytics

Data Mining

NLP

Artificial Intelligence

Sentiment Analysis

Data Visualization

Deep Learning

Consumer Analytics

Supply Chain Analytics
PROGRAM OBJECTIVES

75+
TEACHING & PRACTICE HOURS

100+
APPLIED EXAMPLES PROJECTS & CODES FOR MACHINE LEARNING & ARTIFICIAL INTELLIGENCE

25+
AI / ML ALGORITHMS

15+
LEARN FROM INDUSTRY LEADERS, DOMAIN PROFESSIONALS & ACADEMIC EXPERTS

150+
INTERVIEW QUESTIONS ASKED FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

24x7
ACCESS TACCESS TO LEARNING MANAGEMENT SYSTEM WITH FULLY RECORDED VIDEOS O LEARNING MANAGEMENT SYSTEM WITH FULLY RECORDED VIDEOS
YOU WILL LEARN 15+ PLATFORMS AND TOOLS USED IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE
Practical applied training in platforms commonly used for ML and AI
Learn practical concepts and frameworks and apply your knowledge on tools, libraries and platforms for experiential learning
Machine Learning and Artificial Intelligence PLATFORMS/TOOLS YOU WILL LEARN
Our Alumni at Work
WHAT JOB ROLES WILL I BE PREPARED FOR
 Machine Learning Engineer
 NLP Scientist
 Artificial Intelligence Engineer
 Cloud architect for ML
 ML AI Scientist
 HumanCentered Machine Learning Designer
 Data Scientist
 Statistician
 Big Data Engineer
 AI Data Analyst
 AI Engineer
SOME OF THE TOP COMPANIES GLOBALLY THAT RECRUIT FOR MACHINE LEARNING & AI ROLE
JUNIOR ML/AI SCIENTIST
Avg Salary
5 10 LPA
Work Experience
03 years
Skills
Python, Pandas, AWS, Maths and Statistics
ASSOCIATE ML/AI SCIENTIST
Avg Salary
10 25 LPA
Work Experience
35 years
Skills
Python, Pandas, AWS, R, Data Science Modeling
SENIOR ML/AI SCIENTIST
Avg Salary
25 ? 50 LPA
Work Experience
58 years
Skills
Python, Pandas, AWS, Tableau, Project Experience
LEAD DATA SCIENTIST
Avg Salary
35 ? 150 LPA
Work Experience
1218 years
Skills
Python, Pandas, AWS, Tableau, Google Cloud , Project Experience, Management Skills
DIRECTOR / CTO
Avg Salary
150 LPA+
Work Experience
18+ years
Skills
Python, Pandas, AWS, R , Tableau, Google Cloud , Management Skills, Data Visualization
Program Pathway & outline
1
INTRODUCTION TO PREDICTIVE MODELLING
Learn the concepts, processes, and applications of Predictive Modeling, and their implementation in Python
2
SUPERVISED LEARNING ALGORITHMS
Learn and implement Supervised Machine Learning models including Regression, Classification, SVM algorithms
3
UNSUPERVISED LEARNING ALGORITHMS
Learn and implement Unsupervised Machine Learning models including Clustering and Neural Networking algorithms
4
AI & NEURAL NETWORKS
Learn different kinds of Artificial Intelligence algorithms including NLP, Deep Learning and Reinforcement Learning
Overview

Duration
"<p>15+ Teaching & Practice Hours</p>" "<p>10+ Applications to Predictive Modelling </p>"

Includes
"Comprehensive notes Practical examples Detailed codes Elements of Python Standard Library
"
Platform you will learn
Overview

Duration
"<p>30+ Teaching & Practice Hours</p>" "<p>50+ Examples and codes of Supervised Learning Algorithms</p>"

Includes
"Comprehensive notes Practical examples Detailed codes Use Cases Python Implementation
"
Platform you will learn
Supervised Learning ? Regression
 Linear Regression
 Logistic Regression
 Multivariate Regression
 Ridge RegressionLasso Regression
 Problems with Regression
Unsupervised Learning Algorithms
 Kmeans clustering.
 Hierarchal clustering.
 Anomaly detection.
 Neural Networks.
 Principle Component Analysis.
 Independent Component Analysis.
 Apriori algorithm
Supervised Learning  Classification Algorithms
 KNearest Neighbor
 Na?ve Bayes Classifier
 Decision Trees
 Ensemble Learning ? Bagging
 Random Forest
 Adaboost, Gradient Boost, XGBoost
 Support Vector Classifier
Linear Regression Models
 Least Square Method
 Linear Regression Using One Explanatory Variable
 Regression Table
 Regression Table with Average_Pulse as Explanatory Variable
 The Information Part in Regression Table
 The Coefficients Part in Regression Table
 Data Science  Regression Table: PValue
 Data Science  Regression Table: RSquared
 Data Science  Linear Regression Case Study
Polynomial Regression Models
 ML Polynomial Regression
 Need for Polynomial Regression:
 Equation of the Polynomial Regression Model:
 Implementation of Polynomial Regression using Python
 Visualizing the result for Linear regression:
 Applying Polynomial Regression to the dataset (CASE STUDY)
Logistic Regression Models
 Introduction to Logistic Regression
 What are the types of logistic regression
 What is the Sigmoid Function?
 Hypothesis Representation
 Decision Boundary
 Cost Function
 Gradient Descent
KNearest Neighbor
 KNearest Neighbor(KNN) Algorithm for Machine Learning
 Why do we need a KNN Algorithm?
 How does KNN work?
 How to select the value of K in the KNN Algorithm?
 Advantages of KNN Algorithm:
 Python implementation of the KNN algorithm
Decision Trees,
 Decision Tree Algorithm
 Types of Decision Trees
 Important Terminology related to Decision Trees
 Assumptions while creating Decision Tree
 How do Decision Trees work?
 How to avoid/counter Overfitting in Decision Trees?
 Decision Tree Classifier Building in ScikitlearnDecision
Support Vector Machines
 What is Support Vector Machine?
 How does it work?
 How to implement SVM in Python and R?
 How to tune Parameters of SVM?
 Python Implementation of Support Vector Machine
 Types of SVM
 Ridge Regression
Overview

Duration
"<p>20+ Teaching & Practice Hours</p>" "<p>30+ Applied examples and codes with Unsupervised Learning algorithms</p>"

Includes
"Comprehensive notes Practical examples Detailed codes Use Cases Python Implementation
"
Platform you will learn
Neural Networks
 Multi Layer Perceptrons
 Convolutional Neural Networks
 Recurrent Neural Network
 Auto Encoder
 Generative Adversarial Network
 Graph Neural Networks
 Applications
Multi Layer Perceptron's
 Introduction
 Working on Dataset MNIST
 Building the model
 Regularization
 Activation
 Model visualization
 Evaluation
Convolutional Neural Networks
 Introduction
 Background of CNNs
 What exactly is a CNN?
 How does it work?
 What?s a pooling layer?
 Limitations
Recurrent Neural Network
 Introduction
 Why sequentially matters?
 Why having another model while we have Markov Models?
 FFNN ( Feed Forward Neural Networks ) and Backpropagation:
 Recurrent Neural Networks
 Training Recurrent Neural Networks
Generative Adversarial Network
 Introduction GAN
 GAN Libraries
 TFGAN and TorchGAN
 Mimicry
 Microsoft / IBM GAN toolkit
Graph Neural Networks
 Starting With Recurrent Neural Networks (RNNs)
 Reimagining Recurrent Neural Network (RNN) as a Graph Neural Neural Network (GNN)
 NonDirected Nature of GNNs
 Applying Graph Neural Networks to Useful Inference
 A Tour of Graph Neural Network Applications
 What?s Next for Graph Neural Networks (GNNs)?
 Quantum Graph Neural Networks (QGNNs)
 Bringing GNNs Into the Future
Overview

Duration
"<p>20+ Teaching & Practice Hours</p>" "<p>20+ Application from various domain in Artificial Intelligence AI strategy</p>"

Includes
"Comprehensive notes Practical examples Detailed codes Use Cases Python Implementation
"
Platform you will learn
Importance of Predictive Modelling in Data Science and Analytics
 Predictive Modeling introduction
 Common tool s and platforms for Predictive Modelling
 Types of Predictive Modelling algorithms
 Can you Predict the Stock Market?
 R vs Python for Predictive Modelling
 Model Validation ? Training and Testing
 Lifecycle of Model Building
 Applications of Predictive Modelling
Introduction To Artificial Intelligence
 What is Artificial Intelligence AI
 Emergence of AI
 AI in Practice
 Relationship between Artificial Intelligence, Machine Learning, and Data Science
Practical considerations for Predictive Modelling
 Understand Business Objective
 Analyze and Transform Variables. Random Sampling
 Model Selection and Develop Models (Training)
 Validate Models (Testing), Optimize and Profitability
 Document Methodology and Models
 Implement Models and UAT
 Monitoring and Performance Tracking
 How good is your model
 Choosing between models
Natural Language Processing
 Perform sentiment analysis of tweets
 Classify positive or negative sentiment in tweets using NLP
 Relationships between words using AI
 Translation algorithms and associations
 Implement Autocorrect using distance algorithms
 Implement Autocomplete using Ngram algorithms
 Train Neural networks to perform Named Entity recognition using LTSM algorithms
 Applied examples of Lexical Processing
 Applied examples of Syntactic Processing
Data Exploration for Predictive Modelling
 Data Exploration Tools
 Why is Data Exploration Important?
 What is Exploratory Data Analysis?
 Data Exploration in GIS
 Data Exploration in Machine Learning
 Interactive Data Exploration
 What is the Best Language for Data Exploration?
 Data Exploration in Python
 Data Exploration in R
Deep Learning
 Syntactic Processing
 Neural Networks and Deep Learning
 Convolutional Neural Networks Industry Applications
 Recurrent Neural Networks and applications
 Classical Reinforcement Learning
 Deep Reinforcement
Investment for the Program
VERSION 1 SELFPACED ONLINE

Financing Options
Microsoft Certified: Data Scientist Associate (Training included)

When can you take
Take anytime, Selfpaced
VERSION 2 LIVE TRAINING ONLINE

Financing Options
 a.
Certificate from Christ University
 b.
Data Scientist Associate certification from Microsoft Certified (Training & Examination costs included)
 a.

When can you take
Batch starts Jan 7, 2022
Frequently asked question's about the course
A Machine Learning Engineer is an engineer (duh!) that runs various machine learning experiments using programming languages such as Python, Java, Scala, etc. with the appropriate machine learning libraries. Some of the major skills required for this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc.
Data Scientist analyzes data in order to produce actionable insights. These are then used to make business decisions by the company executives. On the other hand, a Machine Learning Engineer also analyzes data to create various machine learning algorithms that run autonomously with minimal human supervision. In simpler words, a Data Scientist creates the required outputs for humans while a Machine Learning Engineer creates them for machines (Hopefully very smart ones!!!).
NLP stands for Natural language processing and it involves giving machines the ability to understand human language. This means that machines can eventually talk with humans in our own language(Need a friend to talk to Talk with your machine!).
So, an NLP Scientist basically helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. This means that the NLP Scientist should be fluent in the syntax, spelling, and grammar of at least one language in addition to machine learning so that a machine can acquire the same skills.
There are several important Job Roles in Data Science most important one are:
It is not required to have a Programming background, although desirable. However, those without Programming background will undergo Prerequisite training on Programming to accelerate the learning when the Program begins.
Skills Required:
VERSION 1 SELFPACED ONLINE
When can you take
Take anytime, Selfpaced
Certification included
Microsoft Certified: Data Scientist Associate(Training included)
Apply now
VERSION 2 LIVE TRAINING ONLINE
When can you take
Batch starts Jan 7, 2022
Certification included
A A Certificate from Christ University
B Data Scientist Associate certification from Microsoft Certified (Training & Examination costs included)
Apply now
Peer networking
In addition to the training program, you will have ample opportunity to network with the peers in your cohort. Prior to coming to the program, you will have access to your cohort where you can meet and engage with your peers before, during, and after the program.
Industry networking
Throughout the program, you will be taught and mentored by Machine Learning & Data Scientists working/having worked with leading companies.
Alumni networking
Besides, after the program, you will be invited to join the Ml and AI Alumni group.
Placement Leads
We have networks with leading industry participants including banks, financial institutions, consulting firms and analytics firms; you can leverage that network for connecting with the industry and finding suitable opportunities in ML and AI . Once you are trained and ready, we will help you with placement leads that can help you land your dream role in ML and AI.
Having said that, its entirely your performance and skillsets that will define your journey in landing your dream job. We cannot guarantee that you will get a job after completing the program. Obtaining a job is strictly based off one s own skill sets. Upon completion of the program, we will provide you with a certificate that you can print and/or add to your LinkedIn profile.
Networking opportunities
You will have great networking opportunities at the program. Our industry faculty work in the industry in leading positions and that will provide a great opportunity for you to network. Besides, EduEdgePro, through its vast industry network, will help you reach out to the leading financial firms for placements.
Yes, this is an online program.
There are 2 versions