Duration
4 Months

Start Date
5th Sep, 2021

Format

EMI Options
No Interest EMI

Program Overviews

Key Highlights

Download Syllabus

Best AI Master's Program

Program Syllabus

Course 1: Artificial Intelligence and Deep Learning Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

• Basics of Python
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization
• Fundamentals of Statistics
• Generalized Linear Models
• Regression and Clustering
• What is Machine Learning?
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation
• 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
• Understanding graphical model
• Bayesian Network
• Inference
• Model 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?
• Text Preprocessing and Natural Language Processing
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis
• 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
• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard
• 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
• What is CNN?
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• 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
• Introduction to Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications
• 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
• 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

• Introduction to Python
• Features of Python
• Advantages of using Python
• Companies using Python
• Installation process of Python
• Basic commands of Python
• 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
• 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
• 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
• 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
• Decorators in Python
• Syntax of Decorators and Working with them
• Generators in Python
• Working with Generators
• 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
• SciPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages
• Bayesian Theory
• 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
• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
• 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
• 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
• 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)
• 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

• SAS Vs R Vs Python
• Business objectives
• Key driving factors in the analytics world
• R studio
• Graphical User Interfaces (GUI)
• Installing R studio
• Install essential packages along with different GUIs
• 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
• 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
• 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
• Exploratory Data Analysis (EDA)
• Understanding the spread and data points
• Understanding the sourced data for better analysis
• 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
• 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
• Implementing Decision Tree model in R
• Understanding steps to perform the Classification based on inferences on Decision Tree
• Extensive standard R Packages and Functions
• Assumptions of Linear Regression & Logistic Regression
• Linear & Logistic Regression
• Use of Linear & Logistic Regression Model
• What is Time Series Data?
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• 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
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
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    Batches  Date 
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    5th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    11th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    18th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    26th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST

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