Artificial Intelligence Master’s Certification Training

Enroll Now

Syllabus

Course 1: Artificial Intelligence and Deep Learning Certification Training

Course 2: Python for Data Science Certification Training

Course 3: Data Science Certification Training

Duration
4 Months

Start Date
15th May, 2022

Format

EMI Options
No Interest EMI

Program Overviews

Key Highlights

Download Syllabus

Best AI Master's Program

Download syllabus

Program Syllabus

Course 1: Artificial Intelligence and Deep Learning Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

Module 1: Python, libraries and its application

• Basics of Python
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization

Module 2:Understanding predictive analytics

• Fundamentals of Statistics
• Generalized Linear Models
• Regression and Clustering

Module 3: Getting started with Machine Learning

• What is Machine Learning?
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation

Module 4: Understanding Time Series Analysis

• What is a Time Series?
• Time Series Analysis techniques and applications
• Components of Time Series
• Moving average
• Smoothing techniques
• Exponential smoothing
• Univariate time series models
• Multivariate time series analysis
• Arima model
• Time Series in Python

Module 5: Graphical Models

• Understanding graphical model
• Bayesian Network
• Inference
• Model learning

Module 6: Introduction to Reinforcement Learning

• Module 6: Introduction to Reinforcement Learning
• Getting started with Reinforcement Learning
• Bandit Algorithms and Markov Decision Process
• Dynamic Programming and Temporal Difference Learning methods
• What is Deep Q Learning?

Module 7: Learning Natural Language Processing

• Text Preprocessing and Natural Language Processing
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis

Module 8: Introduction to Deep Learning

• What is Deep Learning?
• Why Deep Learning?
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning

Module 9: Understanding Neural Networks With Tensorflow

• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model

Module 10: Advanced Neural Networks With Tensorflow

• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard

Module 11: Deep Networks

• What is Deep Network?
• Why Deep Networks?
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent
• Types of Deep Networks

Module 12: Convolutional Neural Networks (CNN)

• What is CNN?
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN

Module 13: Recurrent Neural Networks (RNN)

• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model

Module 14: Restricted Boltzmann Machine (RBM) and Autoencoders

• Introduction to Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications

Module 15: Understanding Keras Api

• Here you will learn how to implement Keras API and how to use Keras with TensorBoard.
• Getting started with Keras
• Compose Models in Keras
• What is sequential composition?
• What is functional composition?
• Predefined Neural Network Layers
• What is Batch Normalization?
• Save and Load a model with Keras
• Customize the model training process

Module 16: Tflearn Api

• What is TFLearn?
• Compose Models in TFLearn
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• Batch Normalization
• Save and Load a model with TFLearn
• Customize the Training Process

Course 2: Python for Data Science Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

Module 1: Introduction and basics of the Python

• Introduction to Python
• Features of Python
• Advantages of using Python
• Companies using Python
• Installation process of Python
• Basic commands of Python

Module 2: Python Objects and Data Types

• Python Data Types
• Numbers
• Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
• Strings
• Indexing, slicing, and formatting
• Lists
• Tuples
• Sets
• Boolean
• Dictionaries

Module 3: Python Statements, Control flow and Functions

• Python statements
• If Elif and Else Statements
• For loop
• While loop
• Range vs xrange in Python
• List Comprehensions in Python
• Chaining comparison in python
• Else with for and Switch Case in Python
• Using iteration in python
• Iterators in Python
• Iterators function
• Python functions and its types
• Defining a Function in Python
• Rules for naming Python function (identifier)
• Python Function Parameters
• Python Return Statement and calling a function
• Function arguments
• Python function argument and its types
• Default argument in Python
• Python keyword arguments
• Python arbitrary arguments
• Python built-In functions with syntax and examples
• Lambda expressions, map, and filter Functions

Module 4: Working with Object-Oriented Programming concepts in Python

• OOP concept
• Attributes
• Class Keywords
• Class Object Attributes
• Methods in Python
• Data Hiding and Object Printing
• Constructors and Destructors in Python
• Class and static variable in python
• Class method and static method in python
• Inheritance, Encapsulation, Polymorphism & Abstraction
• Special Methods - Magic Method

Module 5: Modules, Packages, Errors & Exception Handling in Python

• Modules
• Installing external packages and modules
• Working oPn yPi using pip Install
• Numeric, Logarithmic, Power, Trigonometric and Angular functions
• Python Errors and Exceptions
• Syntax Errors in Python
• Handling Exceptions in Python
• Raising Exceptions
• User-defined Exceptions
• Unit Testing in Python

Module 6: Decorators and Generators in Python

• Decorators in Python
• Syntax of Decorators and Working with them
• Generators in Python
• Working with Generators

Module 7: Mathematical Computation using NumPy

• NumPy
• Creating Arrays in NumPy
• Using Arrays and Scalars
• Indexing NumPy Arrays
• NumPy Array Manipulation
• Array Transportation
• Universal Array Function
• Array Processing
• Array Input and Output

Module 8: Scientific computing using SciPy

• SciPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages
• Bayesian Theory

Module 9: Data Manipulation using Pandas

• Data manipulation
• Pandas libraries
• Dependency of NumPy libraries
• Pandas Series objects
• Pandas data frames
• Load and process data with Pandas
• Combining data objects
• Merging, and various types of data object attachments
• Record & clean notes, edit notes, visualize notes

Module 10: Data Visualization using Matplotlib and Seaborn

• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting

Module 11: Web scraping with Python

• Web scraping in Python
• Web scraping libraries
• Beautifulsoup and Scrapy
• Installation of beautifulsoup
• Installation of Python parser lxml
• Creating soup object with input HTML
• Searching of tree
• Full or partial parsing
• Output print
• Searching the tree

Module 12: Machine Learning with Python

• Introduction to Machine Learning
• Understanding SciKit Learn
• Need of Machine Learning
• Types in Machine Learning
• Machine Learning Workflow
• Understanding SciKit Learn!
• Machine Learning Use-Cases
• Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning

Module 13: More into Machine Learning- Supervised Learning

• Supervised learning
• Classification and Regression Algorithms
• Linear regression and how to do calculations in Linear Regression?
• Understanding Linear regression in Python
• Understanding Logistics regression
• Working with Supports vector machine
• xgboost (standalone step)

Module 14: More into Machine Learning- Unsupervised Learning

• Unsupervised Learning
• Use Cases of Unsupervised Learning Understanding Clustering,
• Types of Clustering - Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
• Understanding K-Means Clustering and its algorithm
• Stepwise calculation of k-means algorithm
• Running k-means with SciKit Library
• Understanding association mining rule
• Market basket analysis
• Association rule mining and Apriori Algorithm

Course 3: Data Science Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

Module 1: Welcome to the Analytics World

• SAS Vs R Vs Python
• Business objectives
• Key driving factors in the analytics world

Module 2: Introduction to R- environment

• R studio
• Graphical User Interfaces (GUI)
• Installing R studio
• Install essential packages along with different GUIs

Module 3: Importing and reading the data in R

• Data Structure
• Data Types
• Vectors
• Matrices
• Factors
• Data frames
• Lists
• Importing Data
• Connecting to database
• Exporting Data
• Viewing partial data and full data
• Variable & Value Labels: Date Values

Module 4: Data Manipulation

• Variables to perform calculations & binning
• Operators and using multiple operators
• Built-in Functions & User-Defined Functions
• Control Structures
• Conditional statements
• Loops
• Functions
• Sorting, Merging and Appending Data
• Aggregating/summarizing Data
• Reshaping & Subsetting Data
• Data Type Conversions
• Sampling
• File preparation
• Aggregation
• Merging
• Appending
• Type conversion
• Renaming and formatting data
• Handling Duplicates/Missing values

Module 5: Visualization and Reporting in R

• Creating Interactive Graphs on R using packages like
• GGPLOT, GGPLOT2, and PLOTLY
• Working with Histograms & Density Plot
• Dot Plots
• Bar Plots
• Line Charts
• Pie Charts
• Boxplots
• Scatterplots

Module 6: Exploratory analysis in R

• Exploratory Data Analysis (EDA)
• Understanding the spread and data points
• Understanding the sourced data for better analysis

Module 7: More into Analytics World

• Understanding more about Analytics World
• Data Science Vs Data Analytics Vs ML Vs AI Vs Business Analysis
• Analytics keywords and their definitions
• Business Objectives
• Key driving factors in the Analytics world

Module 8: Introduction to Predictive Modelling

• Types of Business problems
• Mapping of Techniques
• Different Phases of Predictive Modeling
• EDA - Exploratory Data Analysis and Need of Data preparation
• Data Preparation
• Performing Data Preparation steps
• Consolidation/aggregation
• Outlier treatment
• Flatliners
• Missing values
• Dummy Creation
• Variable Reduction
• Data Alignment and fine-tuning
• Cluster and Segmentation in R
• Working with various Behavioral Segmentation Techniques
• K-Means Cluster Analysis in R
• Heuristic Segmentation Techniques
• Value-Based, RFM Segmentation
• Life Stage Segmentation

Module 9: Understanding Decision Trees: Applications in R

• Implementing Decision Tree model in R
• Understanding steps to perform the Classification based on inferences on Decision Tree
• Extensive standard R Packages and Functions

Module 10: Linear and Logistic Regression in R

• Assumptions of Linear Regression & Logistic Regression
• Linear & Logistic Regression
• Use of Linear & Logistic Regression Model

Module 11: Understanding the Forecasting: Applications in R

• What is Time Series Data?
• Different components of Time Series data
• Visualize the data to identify Time Series Components

Module 12: Machine Learning in R

• Understanding differences in Statistical learning vs. Machine learning
• Understanding essential classes of Machine Learning Algorithms: Supervised vs Unsupervised Learning
• Text Mining and Sentiment Analysis in R

Download Brouchure

150+

Hours of Content

50+

Live Sessions

14+

Tools & Softwares

30+

Case Studies & Projects

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

Engage in collaborative projects with student-mentor interaction

Benefit by learning in-person with expert mentors

Personalised subjective feedback on your submissions to facilitate improvement

Download Placement Report

Course Fees

Life time Free Upgrade, 24x7 Life time Support & Access, Attend as many batches for Life time,
1:1 Doubt Resolution Sessions

Batches Date
TIME TABLE

Date	Days	Time
15th May	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
21st May	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
28th May	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
5th Jun	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST

Course Fees
Details & Offers

Was $ 700

Now $ 499.00
29% OFF

What Our Learners Are Saying ?

Anurag Srivastava

AGM, Vodafone Idea Limited

I had enrolled in Azure Master's course. I must say that the support team was very helpful right from counseling time to the finish of the course. Course was very well structured and easy to understand. Lots of practical-oriented concepts with Indust

Abhas Kant

Technical Consultant at Moueclick Technologies

Change is the need of our Technical Profession so it becomes very important to learn the new technologies like Azure Data Factory and i did the same to enhance my skills. Eduranz helped me a lot to learn all the concepts mainly focusing on Hands-On.

Kebiraj S

DevOps Manager at Eurolandcom India

Eduranz is a revolution in Cloud Trainings. I have taken 4 courses Cloud & Devops Master's Program from Eduranz. They have perfect material prepared professionally by highly qualified professionals and their Learning management system is very resourc

Asim Nouman

Senior Software Engineer at TECH MAHINDRA

The AWS Solutions Architect online Instructor-Led Training, which i took from Eduranz was interactive and also helped me to improve my knowledge on Cloud. Further helped me in changing the job as well. Thanks for providing a well-experianced trainer

Archit Jha

Solutions Architect at Capgemini

Cleared the Solutions Architect Certificate after doing training of AWS from Eduranz and got the Job in Capgemini as a Test Solutions Architect, Thanks Eduranz Team.

Kapil Nara

Project Manager at Wipro

Eduranz is providing the best software training I have seen in my 15 years of IT career. I have been an Eduranz student for over one year now. Having completed courses such as AWS Solutions Architect Certification Training, DevOps Certification Train

Vijaya Mohan

Business Analyst at EZOPS, United States

Eduranz has great instructors. I joined AWS Solutions Architect Certification class and learnt concepts like EC2, Auto Scaling, EBS, S3, Glacier, VPC, Cloudwatch etc. Would highly recommend this class for any AWS or Cloud enthusiast.

Kebiraj S

DevOps Manager at Eurolandcom India

Abhas Kant

Technical Consultant at Moueclick Technologies

Kishore Kaveti

Database Administration Advisore at NTT DATA

Awesome learning methodology. I have taken Azure Data Factory online training from Eduranz and it was worth of every penny i spent. Cleared the Azure Data Engineer Certification as well.

Suvarchala Koppella

Senior Consultant at Capgemini

"Wanted to Switch from BI to Azure Data Factory but being a senior professional i was skeptical about moving to Cloud and then Eduranz was referred by one of my colleagues. Cleared the Azure Data Engineer Certification and Switched to a new job role

Dileep M

Assistance Manager, Vodafone Idea Limited

This course on Azure will leave one owning all the information that is required to shape up and clear the certification. From Instructor to the support team has always made sure that every individual gets the entire volume of the subject. The structu

Saurabh Dawle

System Administrator at Fiserve

The Azure course at Eduranz was better than any real-time classes I ever attended. The faculty was wonderful and always available. The classes were practical oriented and it has been a great learning experience. Thank you Eduranz Team!

Shivapriya Goud

Application Programmer at Black Knight

I have been part of AZ 104, AZ 303-304 Trainings. The courses were brilliantly designed to make the leraners understand, gain confidence and Clear the Official Certifications on what they are learning for. I got more than what I am looking for and ga

Anurag Srivastava

AGM, Vodafone Idea Limited

Abbas Ali

Advisory Consultant at IBM India Private Limited

Let me start of by congratulating the entire Eduranz team for putting together such amazing content for their courses. I am enrolled in their Azure live batches courses and my learning experience has been phenomenal. I tried learning Azure on my own

Preksha Jain

Application development senior Analyst at Accenture

I had opted for the Azure course and to tell you the faculties were well experienced and the learning curve was really high. I have Cleared the Azure Solutions Arch Certification by the help of Eduranz Team including My Trainer.If you are having seco

Charan SK

Associate Application Designer at DXC Technology

DevOps Training at eduranz was extensive. It was well planned and well-executed. The trainer was well versed with the technology. The assignments were of a good mixture. Overall a great experience.

Varghese George

Manager at Future Supply Chains

The DevOps course curriculum is really good and the training was awesome. Interactive learning classes, knowledgeable instructors and great support staff is the added advantage for people. I will recommend Eduranz to all my working Professional frien

Shivaji Mengar

Senior Software Engineer at AWC Software Pvt Ltd

I have been using Eduranz for learning different topics related to DevOps, Azure. I am very happy with the training and the help they are providing and I feel better than another online training where I registered for AWS. One of the great things is

Asim Nouman

Senior Software Engineer at TECH MAHINDRA

Vijaya Mohan

Business Analyst at EZOPS, United States

Kapil Nara

Project Manager at Wipro

Archit Jha

Solutions Architect at Capgemini

Cleared the Solutions Architect Certificate after doing training of AWS from Eduranz and got the Job in Capgemini as a Test Solutions Architect, Thanks Eduranz Team.