Master’s in Artificial Intelligence

Module 01 – Introduction to Data Science with R
Module 02 – Data Exploration
Module 03 – Data Manipulation
Module 04 – Data Visualization
Module 05 – Introduction to Statistics
Module 06 – Machine Learning
Module 07 – Logistic Regression
Module 08 – Decision Trees and Random Forest
Module 09 – Unsupervised Learning
Module 10 – Association Rule Mining and Recommendation Engines

Self-paced Course Content

Module 11 – Introduction to Artificial Intelligence
Module 12 – Time Series Analysis
Module 13 – Support Vector Machine (SVM)
Module 14 – Naïve Bayes
Module 15 – Text Mining

Tools covered

Python

42 Hours 12 Module

Module 01 – Python Environment Setup and Essentials
Module 02 – Python language Basic Constructs
Module 03 – OOP concepts in Python
Module 04 – Database connection
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing
Module 07 – Matplotlib for data visualization
Module 08 – Pandas for data analysis and machine learning
Module 09 – Exception Handling
Module 10 – Multi Threading & Race Condition
Module 11 – Packages and Functions
Module 12 – Web scraping with Python

Tools covered

Machine Learning

32 Hours 9 Module

Module 01 – Introduction to Machine Learning
Module 02 – Supervised Learning and Linear Regression
Module 03 – Classification and Logistic Regression
Module 04 – Decision Tree and Random Forest
Module 05 – Naïve Bayes and Support Vector Machine (self-paced)
Module 06 – Unsupervised Learning
Module 07 – Natural Language Processing and Text Mining (self-paced)
Module 08 – Introduction to Deep Learning
Module 09 – Time Series Analysis (self-paced)

Tools covered

Artificial Intelligence & Deep Learning with Tensorflow

32 Hours 13 Module

Module 01 – Introduction to Deep Learning and Neural Networks
Module 02 – Multi-layered Neural Networks
Module 03 – Artificial Neural Networks and Various Methods
Module 04 – Deep Learning Libraries
Module 05 – Keras API
Module 06 – TFLearn API for TensorFlow
Module 07 – Dnns (deep neural networks)
Module 08 – Cnns (convolutional neural networks)
Module 09 – Rnns (recurrent neural networks)
Module 10 – Gpu in deep learning
Module 11 – Autoencoders and restricted boltzmann machine (rbm)
Module 12 – Deep learning applications
Module 13 – Chatbots

Tools covered

Natural Language Processing

20 Hours 6 Module

Module 01 – Overview of Natural Language Processing and Text Mining
Module 02 – Text Mining, Cleaning, and Pre-processing
Module 03 – Text Classification
Module 04 – Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling
Module 05 – Introduction to Semantics and Vector Space Models
Module 06 – Dialog Systems

Advanced Deep Learning and Computer Vision

6 Module

Module 01 – RBM and DBNs & Variational AutoEncoder
Module 02 – Object Detection using Convolutional Neural Net
Module 03 – Generating images with Neural Style and Working with Deep Generative Models
Module 04 – Distributed & Parallel Computing for Deep Learning Models
Module 05 – Reinforcement Learning
Module 06 – Deploying Deep Learning Models and Beyond

Excel

24 Hours 23 Module

Module 01 – Entering Data
Module 02 – Referencing in Formulas
Module 03 – Name Range
Module 04 – Understanding Logical Functions
Module 05 – Getting started with Conditional Formatting
Module 06 – Advanced-level Validation
Module 07 – Important Formulas in Excel
Module 08 – Working with Dynamic table
Module 09 – Data Sorting
Module 10 – Data Filtering
Module 11 – Chart Creation
Module 12 – Various Techniques of Charting
Module 13 – Pivot Tables in Excel
Module 14 – Ensuring Data and File Security
Module 15 – Getting started with VBA Macros
Module 16 – Ranges and Worksheet in VBA
Module 17 – IF condition
Module 18 – Loops in VBA
Module 19 – Debugging in VBA
Module 20 – Dashboard Visualization
Module 21 – Principles of Charting
Module 22 – Getting started with Pivot Tables
Module 23 – Statistics with Excel

Tools covered

Tableau Desktop 10

30 Hours 13 Module

Module 01 – Introduction to Data Visualization and The Power of Tableau
Module 02 – Architecture of Tableau
Module 03 – Charts and Graphs
Module 04 – Working with Metadata and Data Blending
Module 05 – Advanced Data Manipulations
Module 06 – Working with Filters
Module 07 – Organizing Data and Visual Analytics
Module 08 – Working with Mapping
Module 09 – Working with Calculations and Expressions
Module 10 – Working with Parameters
Module 11 – Dashboards and Stories
Module 12 – Tableau Prep
Module 13 – Integration of Tableau with R

Tools covered

Free Career Counselling

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Project Work

Projects will be a part of your Artificial Intelligence Master’s program to consolidate your learning. It will ensure that you have real-world experience in AI.

Practice 100+ Essential Tools

Get Real-world Experience

Deal with Financial Data

The project is included to help learners with hands-on experience in financial data analysis. Analyze global sales numbers and profit data by developing an interactive map, and use map styles and layers for enhanced visualization.

Calculation of Employee Bonuses

The comprehensive project lets the learners use the ‘if’ function to calculate the bonuses of all the employees at 10% of their salary. Also, rate the sales employees as per the respective sales figures and rating scale.

Analyze Movie Review Data with NLP

As an important requirement of this project, pre-process the data using tokenization and lemmatization. Also, perform a sentimental analysis task on the data by classifying whether movie reviews are positive or negative.

Integrate Big Data with AWS

Get practical know-how to implement the Machine Learning methods and the regression models, along with making use of Glacier, and adapt Redshift to integrate the company’s Big Data with AWS, making it accessible to the users.

Work with the Customer Data of the UK Bank

The project involves using banking datasets to analyze, clean, process, and visualize the data. Once the data analysis is completed, the project also lets you learn to implement Naive Bayes and Principal Component Analysis.

Market Basket Analysis

The project requires learners to identify trends in the company’s inventory dataset to increase sales numbers. Moreover, they also need to implement data extraction, data manipulation, etc. for Market Basket Analysis.

Credit Card Fraud Detection

The project lets you perform data analysis on a banking dataset for several parameters. Also, use V4 predictor analysis, v7 predictor analysis, and data visualization techniques to calculate the probability of fraud activities.

Prediction of Loan Approval

Netflix Movie-recommendation System

Learn to analyze movie datasets and movie recommendations based on ratings. Get hands-on experience in working with a combined dataset of movies and ratings. Also get to perform data analysis on several data labels, etc.

Web Scraping

The project gives a practical exposure to the applications of Python in Web Scraping. Also get a chance to work on various Web Scraping libraries, Beautiful Soup, Navigable String, parser, searching tree deployment, and more.

Performing Analysis on Customer Churn Dataset

As an important part of the project, learners will be required to analyze employment reliability in the Telecom industry, and also work on real-time analysis of data with multiple labels, data visualization for reliability factor.

Impact of Pre-paid Plans on the Preferences of Investors

This project allows the learners to effectively find and identify the most impacting factors in the preferences of the pre-paid model along with identifying the variables that are highly correlated with impacting factors.

Analyze COVID-19 Trends using Python

The project allows learners to analyze data using Python. Use Pandas to collect data from various files, Plotly to build interactive visualizations, and Prophet library from Facebook to successfully develop time-series models.

Face Detection Project

Use Python 3.5(64-bit) with OpenCV for face detection. As an important requirement, learners need to ensure that the system will have to detect multiple faces in a single image. Working with essential libraries like cv2 and glob.

CNN Assignment

Build a Convolutional Neural Network using MNIST dataset after successfully downloading the MNIST dataset through Keras. The project focuses on know-how to fit the dataset to a model and evaluate the loss and accuracy of the model.

Career Transition

Gaurav Saboo

Senior Technical Associate | India

Salary Hike

I got promoted to Sr. Technical Associate with a 60% hike after completing this course from Intellipaat. The training taught me new skills and how to implement the same in my current role.

Technical Analyst

Senior Technical Associate

D Rajesh Sahoo

Machine Learning Engineer | India

Civil Engg Graduate to Machine Learning Engg.

Intellipaat made my transition from civil engineering graduate to IT industry professional smoothly with its round-the-clock support, excellent curriculum, industry-standard projects, and efficient trainers.

B. Tech — Civil Engineering

Machine Learning Engineer

Melwin Rodrigues

Data Scientist | India

Non-Tech to Data Scientist

Becoming a Data Scientist from a Customer Service Agent was possible only due to expert guidance by Intellipaat trainers. Even after working for 10 years in customer care, I am a Data scientist today.

Customer Service Agent

Data Scientist

Shehzin Mulla

Marketing Data Analyst | India

Salary Hike

Thanks to Intellipaat, as I was able to shift from a Data Analyst to a Marketing Data Analyst with a 35% salary hike and gained deep understanding in Analytics.

Data Analyst

Marketing Data Analyst

Ankit Kumar

Data Scientist | Delhi