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Executive Post Graduate Certification in AI and Machine Learning

2,165 Ratings

Learn from IIT Faculty & Industry Experts with Guaranteed Job Interviews. Campus Immersion at IIT Roorkee.

Gain expertise in Artificial intelligence and Machine Learning through an Executive Post Graduate Certification program in AI and ML offered by iHUB DivyaSampark, a Technology Innovation Hub of IIT Roorkee, in collaboration with Intellipaat This AI and ML program is in collaboration with tech giants IBM and Microsoft. Get classes and guidance directly from IIT Faculty and industry experts, with personalized 1:1 mentorship. Become IIT certified AI and ML expert with this online bootcamp.

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Ranked #1 Artificial Intelligence Program by India TV

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Learning Format

Online Bootcamp

Live Classes

11 Months

iHub - IIT Roorkee

Certification

Campus Immersion

IIT Roorkee

500+

Hiring Partners

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About Program

This online artificial intelligence and machine learning Executive Post Graduate Certification course conducted by the IIT Faculty aims to help you master all the basic and advanced level skills that are crucial in the field of AI and ML. With an updated syllabus, keeping the trending generative AI models in focus, this course is designed to shape your development skills with the demanding trends in the industry.

Key Highlights

620 Hrs of Applied Learning
90+ Live Sessions Across 11 months
218 Hrs of Self-Paced Learning
Learn from IIT Faculty & Industry Practitioners
50+ Industry Projects & Case Studies
One-on-One with Industry Mentors
Placement Assistance
Resume Preparation and LinkedIn Profile Review
24*7 Support
Designed for Working Professionals & Freshers
1:1 Mock Interview
No Cost EMI Option
2 Days campus immersion at IIT Roorkee
Certification from IBM and Microsoft
AZ-900: Microsoft Azure Fundamentals worth $99
Top 2 performers per batch will receive Rs 80000 in fellowship*
Up to Rs. 50 Lakhs startup Incubation Support*
3 Guaranteed Job Interviews upon movement to Placement Pool
Weekday/Weekend Batches

About iHUB DivyaSampark, IIT Roorkee

iHUB DivyaSampark aims to enable innovative ecosystem in new age technologies like AI, ML, Drones, Robots, data analytics (often called CPS technologies) and becoming the source for the next generation of digital technologies, products and services by promoting, enhancing core competencies, capacity building,Read More..

Key Achievements of IIT Roorkee:

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Program in Collaboration with IBM and Microsoft

When it comes to new-age technologies and tools, IBM and Microsoft are two of the biggest names and leading innovators. The course is led by experts who will walk you through all of the necessary skills in the domain and get you started on projects relevant to various organizations.

Benefits for students from this IBM and Microsoft collaboration:

  • Free Voucher for Exam AZ-900: Microsoft Azure Fundamentals worth $99
  • Industry-recognized certification from IBM and Microsoft
  • eLearning course from IBM with exercises
  • Real-time projects and exercises
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Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Who Can Apply for the Course?

  • Individuals with a bachelor’s degree and a keen interest in learning AI and ML
  • IT professionals looking for a career transition as Machin learning experts or AI engineers.
  • Professionals aiming to move ahead in their IT career
  • Artificial Intelligence and Business Intelligence professionals
  • AI and ML engineers who want to upskill themselves as per industry trends.
  • Developers and Project Managers
  • Freshers who aspire to build their career in the field of Artificial Intelligence and Machine Learning
Who can aaply

What roles can a person trained in Artificial Intelligence and Machine Learning Work on?

AI Expert

Build strategies on frameworks and technologies to develop AI solutions and help the organization prosper.

Machine Learning Expert

With the help of several machine learning tools and technologies, build statistical models with huge chunks of business data.

Prompt Engineer

With the advancement in technologies of generative AI, the amount of coding work is reduced, giving up a new job role to develop AI as Prompt Engineer.

AI Scientist

Build NLP/LLM models and fine-tune the models to improve accuracy, and come up with new techniques for model valuation and validation.

ML and AI Solution Architects

Implement strategies to explore unstructured data to visualize meaningful insights as per business requirements. Design and develop APIs using deep learning frameworks.

Generative AI Engineers

Use AI models such as BERT, Huggingface, GPT, or LLama-2 to build new AI models. Train and fine-tune models to improve performance.

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Skills to Master

Python

Generative AI

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Machine Learning

Predictive Analytics

NLP

Data visualization

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Tools to Master

pyspark python jupyter Scipy numpy pandas matplotlib TensorFlow SQL Power-BI excel git SparkSQL
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Meet Your Mentors

Curriculum

Live Course Industry Expert Academic Faculty

1.1 Introduction to Linux

  • Linux Fundamentals: Understanding the basics of the Linux operating system.
  • Getting Started: Setting up and navigating Linux.

1.2 Linux Basics

  • File Handling in Linux: Learning essential file operations in Linux.
  • Data Extraction: Extracting and manipulating data using Linux commands.

1.3 Introduction to Python and IDEs

  • Python Overview: Introduction to the Python programming language.
  • IDEs for Python: Exploring various Integrated Development Environments for Python such as Jupyter.

1.4 Python Basics

  • Variables and Data Types: Understanding Python variables and different data types.
  • Control Flow: Learning about loops and conditional statements in Python.
  • Functions and Lambda Functions: Exploring functions,, and lambda functions in Python.
  • File Handling and Exception Handling: Managing files and handling exceptions in Python.

1.5 Object-Oriented Programming (OOP) in Python

  • OOP Concepts: Introduction to Object-Oriented Programming concepts like classes, objects, inheritance, abstraction, polymorphism, and encapsulation.

1.6 Hands-on Sessions and Assignments

  • Practical Application: Applying the knowledge gained in both Linux and Python through hands-on sessions and assignments.
  • Real-World Problem Solving: Solving real-world problem statements to reinforce learning in both Linux and Python.
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2.1 Managing Data with NumPy

  • Operations with NumPy Arrays, including Create, Read, Update, Delete (CRUD) operations.
  • Fundamentals of Linear Algebra: Operations like Matrix Multiplication, Matrix Inversion, Transposition, Determining Matrix Rank and Determinant, working with Scalars, Vectors, and Matrices.

2.2 Utilizing Pandas for Data Handling

  • Techniques for Data Importation, Utilizing DataFrames and Series, Executing CRUD Operations, Segmenting Data.

2.3 Preprocessing of Data

  • Conducting Exploratory Data Analysis, Implementing Feature Engineering Techniques, Scaling Features, Normalizing and Standardizing Data.
  • Approaches for Null Value Treatment, Outlier Identification and Resolution, Variance Inflation Factor (VIF) Analysis, Balancing Bias-Variance, Employing Cross-Validation Techniques, and Splitting Data into Training and Testing Sets.

2.4 Visualizing Data

  • Creating Visual Representations like Bar Charts, Scatter Plots, Count Plots, Line Plots, Pie Charts, and Donut Charts using Python’s Matplotlib Library.
  • Developing Regression and Categorical Plots, Area Plots, and more with Python’s Seaborn Library.
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3.1 Introduction to SQL

  • Essential Concepts in Structured Query Language
  • Understanding SQL Tables, Joins, and Variables

3.2 Proficiency in Advanced SQL

  • Exploration of SQL Functions, Subqueries, Rules, and Views
  • Techniques in Nested Queries, String Functions, and Pattern Matching
  • Utilization of Mathematical and Date-Time Functions

3.3 Mastering User-Defined Functions in SQL

  • Varieties of User-Defined Functions (UDFs), including Inline Table-Valued and Multi-Statement Table Functions
  • Implementation of Stored Procedures, Rank Function, and SQL ROLLUP

3.4 SQL for Optimized Performance

  • Strategies for Effective Record Grouping, Searching, and Sorting
  • Utilization of Clustered Indexes and Common Table Expressions for Enhanced Performance

3.5 Practical SQL Application: Hands-on Exercise

  • Crafting Comparative SQL Queries to Analyze Data Trends Over Time
  • Focusing on Significant Data by Filtering Out Redundancies
  • Predictive Analysis: Identifying Future Demands using Advanced SQL Techniques including Complex Subqueries, Functions, and Pattern Matching Concepts
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4.1 Essentials of Descriptive Statistics

  • Understanding Measures of Central Tendency and Measures of Spread
  • Comprehensive Overview of the Five Point Summary and Related Concepts

4.2 Foundations of Probability

  • In-depth Study of Probability Distributions
  • Exploration of Bayes’ Theorem and the Central Limit Theorem
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5.1 Utilizing MS Excel for Statistics and Descriptive Analytics

  • Comprehensive Analysis of Central Tendency and Spread Measures
  • Detailed Exploration of the Five Point Summary
  • In-depth Study of Probability Distributions, including Applications in Business Analytics
  • Examination of Specific Probability Distributions: Binomial, Poisson
  • Understanding Bayes Theorem and the Central Limit Theorem

5.2 Applying Python for Comprehensive Statistical Analysis

  • Detailed Examination of Correlation and Covariance
  • Mastery in Setting and Evaluating Confidence Intervals
  • Advanced Techniques in Hypothesis Testing, including F-test, Z-test, T-test
  • Proficiency in Analytical Methods such as ANOVA and the Chi-Square Test
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6.1 Foundations of Machine Learning

  • An Overview of Machine Learning Types: Supervised and Unsupervised Learning
  • Introduction to Machine Learning Tools: Scikit-Learn, Keras, and More

6.2 Regression Analysis

  • Exploring the Basics of Regression: Understanding Classification vs. Regression
  • Identifying Regression Problems: Recognizing Dependent and Independent Variables
  • Training Techniques: How to Effectively Train Regression Models
  • Evaluation Strategies: Methods for Assessing Regression Model Performance
  • Optimization Approaches: Enhancing the Efficiency of Regression Models

6.3 Diving into Classification Models

  • Introduction to the Concept of Classification Problems
  • Identifying Key Elements of Classification: Dependent and Independent Variables
  • Training Methodologies: Best Practices for Training Classification Models
  • Evaluative Measures: Techniques for Assessing Classification Model Accuracy
  • Model Efficiency: Strategies for Boosting Classification Model Performance

6.4 Mastering Clustering Techniques

  • Unveiling Clustering Concepts: Understanding Clustering Problems
  • Identifying Clustering Scenarios: Understanding Variables in Clustering
  • Training Approaches: How to Effectively Train Clustering Models
  • Evaluative Techniques: Assessing the Performance of Clustering Models
  • Optimization Strategies: Enhancing the Effectiveness of Clustering Models
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7.1 Linear Regression Models

  • Building and refining linear regression models using statistical analysis, data preparation, normalization, and standardization.

7.2 Logistic Regression Applications

  • Developing logistic regression models for binary classification tasks, such as predicting medical conditions or weather events.

7.3 Decision Tree Implementation

  • Constructing decision tree models for classification issues, structuring data in a tree format to derive optimal solutions.

7.4 Random Forest Strategy

  • Crafting random forest models to tackle classification challenges within a supervised learning framework.

7.5 Utilization of Support Vector Machines (SVM)

  • Employing SVMs for both regression and classification tasks, addressing various types of data.

7.6 Gradient Descent Methodology

  • Applying the gradient descent algorithm as a repetitive optimization technique to locate the function’s local minima and maxima.

7.7 K-Nearest Neighbors Approach

  • Implementing the K-Nearest Neighbors algorithm, a straightforward method for resolving classification problems.

7.8 Time Series Forecasting Techniques

  • Leveraging time series data for insightful forecasting, utilizing specialized methods for time series analysis.
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8.1 K-means Clustering

  • Implementation of the K-means algorithm for sorting data into clusters in an unsupervised learning context.

8.2 Dimensionality Reduction Techniques

  • Techniques for managing and simplifying multi-dimensional data, including standardization for more straightforward computational analysis.

8.3 Linear Discriminant Analysis (LDA)

  • Utilizing LDA for the reduction and optimization of dimensions in multi-dimensional datasets.

8.4 Principal Component Analysis (PCA)

  • Applying PCA to efficiently handle and reduce dimensions in multi-dimensional data.

8.5 Assessing Model Performance

  • Generating Classification Reports: Evaluating models based on metrics such as recall, precision, and F1-score.
  • Constructing Confusion Matrices: Analysis of true positives, true negatives, false positives, and false negatives.
  • Calculating Performance Metrics: Including R-squared, adjusted R-squared, mean squared error, and others for comprehensive model evaluation.
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9.1 Bagging and Boosting Techniques

  • XGBoost: Implementation of Extreme Gradient Boosting, an ensemble method that enhances predictions by combining multiple weaker models.
  • ADAboost: Utilization of an ensemble algorithm that transforms weak learning models into stronger ones, improving overall performance.
  • Gradient Tree Boosting: A sophisticated boosting technique focused on minimizing bias in model predictions.
  • Bootstrap Aggregation: Application of a bagging strategy, employing decision trees on bootstrap samples from the training data.

9.2 Diverse Machine Learning Methodologies

  • Ordinary Least Square (OLS): A fundamental regression technique for estimating linear regression equation coefficients.
  • Markov Chain: A probability-based algorithm used to model sequential events or states.
  • Naive Bayes: Implementation of a predictive model inspired by Bayes’ theorem, widely used in machine learning.
  • Stochastic Gradient Descent: An optimization method that refines machine learning algorithms by iterating towards the minimum or maximum of a function.
  • Gaussian Mixture Model: A probabilistic model assuming data generation from various Gaussian distributions with unknown parameters.
  • Singular Value Decomposition: A matrix factorization technique, often employed in creating recommendation systems.

9.3 Predictive Analytics in Machine Learning

  • Exploring Regression and Multivariate Analysis in predictive modeling.
  • Tackling Classification problems with innovative machine learning approaches.
  • Managing Data Multidimensionality and employing Linear Algebra techniques.
  • Advanced Feature Engineering and Selection methodologies.
  • Hyperparameter Tuning and other critical optimization strategies.

9.4 Cognitive Science Applications in Analytics

  • Understanding the application of Natural Language Processing in various domains like search engines and social media.
  • Delving into Web Analytics with a focus on tools like Google Analytics.
  • Exploring Machine Learning applications in areas like chatbots.
  • Advanced Social Media Analytics including sentiment analysis, topic modeling, and text summarization.
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10.1 Getting Started with Deep Learning

  • Easy-to-Understand Introduction to Deep Learning and AI Concepts
  • Exploration of Deep Learning Applications in Various Industries
  • Clarifying the Differences Among Data Science, Machine Learning, Deep Learning, and Artificial Intelligence
  • Life Cycle of Deep Learning Projects

10.2 Basics of Neural Networks

  • Understanding the Evolution of Deep Learning from the Human Nervous System
  • Introduction to Perceptron (Single Cell)
  • Structure and Function of Multi-Cell Perceptron Topologies
  • In-depth Look at Weights and Biases in Neural Networks
  • Building a Neural Network from the Ground Up Using Numpy
  • The Role and Impact of Learning Rate in Neural Networks
  • Creating Neural Networks with Learning Rate Adjustments
  • Implementation and Significance of Activation Functions

10.3 Neural Network Frameworks Overview

  • Comparison and Contrast of Various Neural Network Frameworks
  • Comprehensive Introduction to TensorFlow
  • Detailed Introduction to Keras, the Official API for TensorFlow
  • The Concept and Practice of Sequential Modeling

10.4 Introduction to Fully Connected Neural Networks

  • Basics of Image Processing with cv2
  • Creation and Understanding of Fully Connected Neural Networks (FCNN)
  • Developing a Single Hidden Layer Neural Network Using Keras (MNIST Dataset)
  • Insights into Neural Network Topology and Parameter Configurations

10.5 Dive into Convolutional Neural Networks (CNN)

  • Introduction to Kaggle and Its Role in Machine Learning
  • Understanding the Challenges of Data Flattening
  • Detailed Introduction to Convolutional Neural Networks
  • The Mathematics Behind Filters/Kernels in CNNs
  • The Concept and Importance of Pooling, Batch Normalization, and Dropout in CNNs

10.6 Post-Modeling Activities in Neural Networks

  • The Process of Augmentation in Deep Learning
  • Testing and Evaluating Neural Network Models
  • Techniques for Saving and Registering Models
  • Strategies for Making Predictions Using Trained Models
  • Training Large Image Models in Batches
  • Utilizing TensorBoard for Performance Monitoring of Large Models

10.7 Learning from Existing Models

  • Fundamental Principles of Transfer Learning
  • Exploring the Architecture of VGG Models
  • Practical Application of VGG16 for Transfer Learning
  • Comparing Transfer Learning with Fine-Tuning Techniques

10.8 Exploring Recurrent Neural Networks

  • Addressing the Limitations of CNNs
  • Understanding the Concept of “Context” in Neural Networks
  • Detailed Study of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Networks

10.9 Foundations of Natural Language Processing (NLP)

  • Introduction to Text Processing in Deep Learning
  • Overview of NLTK and Spacy for Text Analysis
  • Methods for Text Pre-Processing
  • Developing a Sentiment Analysis Detector Using NLP and LSTM Techniques
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11.1 Comprehensive Exploration of Text Mining and Pre-processing

  • Techniques and Tools for Text Mining: Utilizing Various Tokenizers, Tokenization Methods, and Frequency Distribution Analysis
  • Delving into Stemming, Part-Of-Speech (POS) Tagging, and Lemmatization
  • Advanced NLP Concepts: Exploring Bigrams, Trigrams, N-grams, and Entity Recognition

11.2 Advanced Text Classification and Sentiment Analysis

  • A Holistic View of Machine Learning in NLP: Exploring Words, Term Frequency, and Document Frequency
  • Implementing Countvectorizer and Inverse Document Frequency for Text Conversion
  • Analyzing Text Classification: Using Confusion Matrix and Naive Bayes Classifier for Sentiment Analysis

11.3 Sentence Structure and Language Modeling

  • Detailed Study of Language Modeling and Sequence Tagging
  • Techniques for Sequence Tasks: Predicting Sequence of Tags and Understanding Syntax Trees
  • Exploring Context-Free Grammars, Chunking, and Techniques for Automatic Paraphrasing of Texts

11.4 Development of AI Chatbots and Recommendation Systems

  • Practical Application of NLP Concepts: Building an AI-Powered Chatbot and a Recommendation Engine
  • Integrating NLP Techniques for Enhanced User Interaction and Personalized Recommendations
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12.1 Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBNs), and Variational AutoEncoders

  • Introductory Concepts: Understanding RBM and AutoEncoders
  • Application of RBM in Deep Neural Networks and Collaborative Filtering
  • Exploring the Features and Applications of AutoEncoders

12.2 Object Detection with Convolutional Neural Networks (CNN)

  • Building Convolutional Neural Networks Using TensorFlow
  • Components of CNN: Convolutional Layers, Dense Layers, and Pooling Layers
  • Image Filtering Techniques: Utilizing CNNs for User-Specific Image Queries

12.3 Neural Style Transfer and Deep Generative Models

  • Integrating Automated Conversation Bots with Generative Models
  • Understanding and Implementing Sequence to Sequence Models (LSTM) for Image Generation

12.4 Distributed and Parallel Computing in Deep Learning

  • Exploring the Differences Between Parallel and Distributed Computing
  • Utilizing Distributed Computing in TensorFlow with tf.distribute
  • Implementing Distributed Training Across Multiple CPUs and GPUs
  • Introduction to Federated Learning and Parallel Computing in TensorFlow

12.5 Reinforcement Learning in Deep Neural Networks

  • Analyzing the Human Mind’s Correlation with Deep Neural Networks
  • Core Components of Artificial Neural Networks and Their Architecture
  • In-Depth Study of Reinforcement Learning Concepts, Parameters, Layers, and Optimization Algorithms in Deep Neural Networks
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13.1 Long Short-Term Memory (LSTM) Networks

  • Introduction to LSTM: Definition and Working Mechanism
  • Exploring Various Applications of LSTM in Deep Learning and Beyond

13.2 Transformers in Deep Learning

  • Understanding Transformers: Their Function and Role in Deep Learning
  • Detailed Examination of Transformer Architectures: Encoder-Decoder Models, Self-Attention Mechanisms
  • Diverse Applications and Types of Transformers in AI

13.3 Bidirectional Encoder Representations from Transformers (BERT)

  • Overview of BERT as a Language Model
  • Operational Insights: How BERT Functions and Its Distinction from LSTM
  • Exploring the Wide Range of BERT Applications in Natural Language Processing

13.4 Generative Pre-trained Transformer (GPT) Models

  • Insight into GPT: The Essence of Generative Pre-Trained Models
  • Understanding the Functionality of GPT Models
  • Real-World Examples and Applications of GPT in Various Domains

13.5 Large Language Models (LLM)

  • Introduction to Large Language Models in NLP
  • Detailed Analysis of How LLMs Operate
  • Exploring the Broad Spectrum of Applications for LLMs in AI and NLP
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14.1 Non-Relational Data Stores and Azure Data Lake

  • Exploration of Various Non-Relational Data Stores: Document, Columnar, Key/Value, Graph, Time Series, Object Stores, and External Indexes
  • Understanding the Role and Advantages of NoSQL Databases
  • Comprehensive Overview of Azure Data Lake Storage: Key Components, Data Storage Methods, and Data Lake Architecture

14.2 Data Lakes and Azure Cosmos DB

  • Fundamental Concepts of Data Lakes
  • In-depth Analysis of Azure Cosmos DB and its Benefits
  • Overview of Azure Blob Storage and Its Importance
  • Data Partitioning Strategies: Horizontal, Vertical, and Functional Partitioning
  • Understanding Consistency Levels in Azure Cosmos DB

14.3 Relational Data Storage Systems

  • Introduction to Relational Data Storage Solutions
  • Detailed Study of Azure SQL Database: Deployment Models and Service Tiers
  • Significance of SQL Database Elastic Pool

14.4 Azure SQL Database

  • Exploring Azure SQL Security Features
  • High-Availability Models in Azure SQL Database: Standard and Premium Models
  • Azure Database Solutions for MySQL, PostgreSQL, and MariaDB
  • Understanding PolyBase and its Applications
  • Azure Synapse Analytics: SQL Analytics, SQL Pool, and Architecture Components

14.5 Azure Batch Processing

  • Introduction to Azure Batch and its Functions
  • Analysis of Workloads Suitable for Azure Batch
  • Additional Features and Working Mechanism of Azure Batch

14.6 Azure Data Factory

  • Workflow Process in Azure Data Factory
  • Reasons for Using Azure Data Factory
  • Integration Runtime and Data Mapping in Azure Data Factory

14.7 Azure Databricks

  • Introduction and Benefits of Azure Databricks
  • Insights into the Azure Spark-Based Analytics Platform
  • Integration of Apache Spark in Azure Databricks

14.8 Azure Stream Analytics

  • Functionality and Key Features of Azure Stream Analytics
  • Windowing Functions in Stream Analytics: Tumbling, Hopping, Sliding, and Session Windows
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15.1 Fundamentals of MLOps

  • Comprehensive Introduction to MLOps: Understanding the Lifecycle and Pipeline
  • In-depth Exploration of MLOps Components and Processes

15.2 Implementation of Machine Learning Models

  • Getting Started with Azure Machine Learning: An Introductory Overview
  • Strategies and Techniques for Deploying Machine Learning Models Using Azure Services
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16.1 American Sign Language Recognition

  • This case study focuses on generating and utilizing data for image recognition to identify American Sign Language. It includes training custom object detection models with pre-trained models.

16.2 Gesture Recognition

  • Learn how to employ pre-trained model weights to develop a real-time gesture recognition classifier using deep learning and computer vision techniques.

16.3 Shark Species Classification

  • A basic classification case study designed to provide insights into image classification using TensorFlow. It involves the use of deep learning for shark species identification.

16.4 Stock Market Forecasting

  • This study addresses the use of historical data coupled with deep learning techniques, like LSTM, to forecast stock market trends and prices.

16.5 Cyber Threat Detection Using NLP

  • Explore the application of Natural Language Processing and deep learning in detecting cybersecurity threats within text data.

16.6 Text Summarization Using NLP and Deep Learning

  • Dive into the process of summarizing text using advanced deep learning and generative models, emphasizing the role of NLP in extracting key information.

16.7 Sentiment Analysis

  • Understand the implementation of various NLP and deep learning techniques for sentiment analysis, classifying emotions or opinions expressed in text data.

16.8 Development of a Recommendation Engine

  • This case study guides through the creation of a recommendation engine using deep learning, suitable for applications like movie, restaurant, or book recommendations.
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17.1 Excel Fundamentals

  • Explore the basics of Excel, including reading data, formula referencing, named ranges, logical functions, conditional formatting, validation, dynamic tables, sorting, filtering, and chart creation. The module also covers pivot tables, dashboards, data security, and VBA macros, including IF conditions, loops, and debugging.

17.2 Excel for Data Analytics

  • Delve into data analytics using Excel, covering text data handling, text data manipulation (splitting, combining), date manipulation, data conversion, handling missing values, data cleaning, and working with tables.

17.3 Data Visualization with Excel

  • Learn about data visualization techniques in Excel, including charts, pie charts, scatter and bubble charts, bar charts, column charts, line charts, maps, multiples (sets of charts with the same axes), matrices, cards, and tiles.

17.4 Excel Power Tools

  • Explore advanced Excel tools like Power Pivot, Power Query, and Power View, which enable data transformation, modeling, and visualization.

17.5 Classification Problems using Excel

  • Understand how to tackle classification problems in Excel, including binary classification, confusion matrices, AUC and ROC curve analysis, and handling multiple classification problems.

17.6 Information Measure in Excel

  • Explore concepts related to information measures in Excel, covering probability, entropy, dependence, and mutual information.

17.7 Regression Problems Using Excel

  • Learn about regression analysis in Excel, including standardization, normalization, probability distributions, inferential statistics, hypothesis testing, ANOVA, covariance, correlation, linear regression, logistic regression, and information gain using regression.

17.8 Hands-on Exercise

  • Engage in a practical exercise involving classification problems using Excel on sales data. Perform statistical tests on various samples from the population to gain insights.
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18.1 Getting Started with KNIME

  • Introduction to KNIME: Understanding its capabilities in data analytics and workflow creation.

18.2 Data Management in KNIME

  • Learning the essentials of managing data in KNIME: Creating and executing workflows, and effective data loading techniques.

18.3 Loop Structures in KNIME

  • Exploring loop mechanisms in KNIME for enhanced efficiency in data transformation processes.

18.4 Web Scraping Techniques in KNIME

  • Mastering web scraping in KNIME: Techniques to extract data directly from web sources.

18.5 Advanced Features in KNIME

  • Delving into feature selection and hyperparameter optimization in KNIME for refining machine learning models.

18.6 Practical Application: KNIME Case Study

  • Hands-on case study: Building comprehensive machine learning models in KNIME, utilizing algorithms such as linear regression, logistic regression, decision trees, and random forests.
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19.1 Fundamentals of Version Control

  • Introduction to Version Control: Understanding its purpose and various types
  • Insight into Subversion (SVN): Exploring one of the popular version control systems

19.2 Comprehensive GIT Training

  • Overview of GIT Lifecycle: Understanding the stages and processes in GIT
  • Mastery of Common GIT Commands: Learning essential commands for effective version control
  • Branch Management in GIT: Techniques for working with and managing branches

19.3 Collaboration and Workflow in Github

  • Collaborative Techniques: Learning about pull requests and team collaboration on GitHub
  • Authentication Methods in Github: Utilizing SSH and HTTP for secure interactions
  • Advanced GIT Practices: Strategies for merging branches, resolving merge conflicts, and understanding the GIT workflow
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The capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data. The capstone project will help you master the following verticals:

  • Extracting, loading and transforming data into usable format to gather insights.
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements.
  • End-to-end deep learning project lifecycle to build various applications using deep learning frameworks.
  • Assessment and monitoring of the project using advanced MLOps techniques to implement feedback loops.
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Disclaimer
Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

90+ Live Sessions Across 11 months
218 Hrs of Self-Paced Learning
50+ Industry Projects & Case Studies
24*7 Support

Projects

Projects will be a part of your Executive Post Graduate Certification in AI and ML to solidify your learning. They ensure you have real-world experience in Artificial Intelligence and Machine learning.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

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Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Works At

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Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!

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Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application

Admission

Admission

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 2,632

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Upcoming Application Deadline 24th Feb 2024

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 24th Feb 2024 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 24th Feb 2024 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

How will I receive my certificate?

Upon completion of the Artificial Intelligence and Machine Learning training course and execution of the various projects in this program, you will receive a joint Executive Post Graduate Certification in Artificial Intelligence and Machine Learning from Intellipaat and iHUB DivyaSampark, IIT Roorkee.

Intellipaat provides career services that include placement assistance for all the learners enrolled in this course. iHUB DivyaSampark, IIT Roorkee is not responsible for career services.

The Executive Post Graduate Certification in AI and ML course is conducted by leading experts from IIT Roorkee and Intellipaat who will make you proficient in these fields through online video lectures and projects. They will help you gain in-depth knowledge in Artificial Intelligence and Machine Learning, apart from providing hands-on experience in these domains through real-time projects.

After completing the course and successfully executing the assignments and projects, you will gain an Executive Post Graduate Certification in Artificial Intelligence and Machine Learning from Intellipaat and iHub, IIT Roorkee which will be recognized by top organizations around the world. Also, our job assistance team will prepare you for your job interview by conducting several mock interviews, preparing your resume, and more.

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post them to our community.

There will be a two-day campus immersion module at IITR iHub during which learners will visit the campus. You will learn from the faculty as well as interact with your peers. The cost of travel and accommodation will be borne by the learners. However, the campus immersion module is optional.

To be eligible for getting into the placement pool, the learner has to complete the course along with the submission of all projects and assignments. After this, he/she has to clear the Placement Readiness Test (PRT) to get into the placement pool and get access to our job portal as well as the career mentoring sessions.

You will undergo below sessions:

  • Job Search Strategy Sessions
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

Please note that the course fees is non-refundable and we will be at every step with you for your upskilling and professional growth needs.

Due to any reason you want to defer the batch or restart the classes in a new batch then you need to send the batch defer request on [email protected] and only 1 time batch defer request is allowed without any additional cost.

Learner can request for batch deferral to any of the cohorts starting in the next 3-6 months from the start date of the initial batch in which the student was originally enrolled for. Batch deferral requests are accepted only once but you should not have completed more than 20% of the program. If you want to defer the batch 2nd time then you need to pay batch defer fees which is equal to 10% of the total course fees paid for the program + Taxes.

The highest salary in AI domain is currently offered by startup Open AI as L5 AI engineer with a package of a whooping US$ 915k. This amounts to INR 7.8 crore per annum.

The future of AI and ML is vast, with a projected CAGR of 23.7% through 2027, according to Global Market Insights. This exponential growth promises to revolutionize how we live and work, pushing the boundaries of automation and data analysis into deep cognitive tasks. Across industries, we can expect:

  • Personalized experiences: From healthcare diagnoses to educational paths and targeted marketing campaigns, AI and ML will tailor experiences to individual needs and preferences.
  • Enhanced decision-making: By analyzing vast amounts of data, AI and ML models can optimize operations, predict market trends, and drive significant efficiency gains.
  • Advanced automation: Autonomous vehicles, robots performing complex tasks, and self-learning systems will reshape labor landscapes and revolutionize industries.
  • Scientific breakthroughs: From accelerating drug discovery to unlocking materials science breakthroughs and finding solutions to climate change, AI and ML will provide powerful tools for scientific progress.

This AI-powered future will undoubtedly reshuffle industries, creating new job opportunities in areas like development, ethical considerations, and human-machine collaboration.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work