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26th June 2024


per month

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Online- blended learning


Project Based

Program Overview

Key Features

Industry Oriented Curriculum
Masterclasses by Industry Experts
In-demand Skills
Hands-on Projects
Live Weekend Sessions
Capstone Project

Step into the World of ML & AI

Experience best-in-class content developed by leading faculty & industry leaders.

Earn a chance to become a machine learning expert

Completing our Machine Learning and Generative Artificial Intelligence certificate course qualifies students for the TCS iON National Proficiency Test (NPT). The NPT evaluates practical knowledge, certifying individuals in their domains. Integrating the NPT for our students aims to bridge skill gaps, produce job-ready candidates, and enhance overall industry employability.

Key Highlights

Discover our online Machine Learning and Generative
AI Certificate program and begin an exciting educational journey.

According to a report by Gartner, the year

2025 will see 2 million

New jobs generated by AI and ML.

India is among the top three talent markets, producing


of the world’s AI talent pool.

Curriculum (Machine
Learning & Generative AI)

By the end of this module, learners will understand the fundamental concepts of machine learning, including supervised and unsupervised learning techniques. They will be able to interpret machine learning algorithms and their applications, enabling them to make informed decisions when implementing ML solutions.

Upon completion of this module, participants will possess the skills to efficiently collect, clean, and pre-process large datasets using popular tools such as Apache Spark and TensorFlow Data Validation. They will apply various data engineering techniques, including feature engineering and dimensionality reduction, to ensure data quality and optimize model performance. Additionally, students will work on real-life projects, addressing specific industry problems and utilizing machine learning and deep learning techniques to extract valuable insights from the data.

After completing this module, students will be capable of building and evaluating machine learning models using a variety of algorithms, such as decision trees, random forests, and gradient boosting. They will gain hands-on experience with libraries like scikit-learn and XGBoost to implement model training and evaluation procedures. Furthermore, learners will apply their knowledge to real-life projects, tackling industry-specific challenges and employing advanced machine learning techniques to derive meaningful predictions and recommendations.

At the end of this module, learners will have a comprehensive understanding of deep learning architectures and techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. They will gain practical experience with popular frameworks such as TensorFlow and PyTorch, implementing and fine-tuning deep neural network for tasks like image classification, natural language processing, and sequence generation. Students will also engage in real-life projects, using deep learning techniques to address complex real-world problems and deliver innovative solutions.

Upon completion of this module, participants will be adept at using Amazon Web Services (AWS) for deploying and managing machine learning applications. They will gain hands-on experience with AWS services like Amazon SageMaker and Amazon Recognition, mastering the end-to-end process of building scalable machine learning pipelines on the cloud. Learners will work on real-life projects, leveraging AWS infrastructure to tackle industry-specific challenges and develop production-ready machine learning systems.

By the end of this module, students will master the development and deployment of generative AI models, including GANs, VAEs, and transformers, using popular frameworks like TensorFlow and PyTorch. They will gain handson experience with cutting-edge tools such as ChatGPT and Dall-E. Working on real-life projects, students will apply generative AI techniques to create realistic images, generate coherent text, and compose unique music pieces at the mid journey of their learning.

A regional language translator is a software application or service that allows users to translate text or speech from one regional language to another, typically between languages that are not commonly used outside of a specific region or country. Regional language translators are designed to address the language barriers that exist between different communities and languages within a region.
Regional language translators use a variety of techniques to translate text, including statistical machine translation, rule-based translation, and neural machine translation. These techniques involve analyzing the structure and meaning of the source language and using that information to generate a translation in the target language Capstone Project Business Domain: NLP & Translation

When you complete a course with us, you become the most desirable candidate in the field of Machine Learning & Generative AI

Note : Sample Certificate

Eligibility Criteria

Aspiring Data Scientists
Business Professionals & Entrepreneurs
Software Engineers & Developers
College Students
Technology Enthusiasts
Researchers & Academicians

Potential Job Profiles

Machine Learning Engineer
Data Scientist
Deep Learning Engineer
AI Researcher
Computer Vision Engineer
AI Solution Architect

Programming Languages
and Tools covered

Fee Structure

EMI starting at


Per month

Full Course Fee



Inclusive of all taxes

Learning Outcomes

  • Apply supervised and unsupervised learning techniques to solve real-world problems.
  • Implement deep learning models, including CNNs and RNNs, for computer vision and sequential data analysis.
  • Use generative AI models, such as VAEs and GANs, for image and text generation.
  • Apply computer vision techniques using the OpenCV library for image classification and object detection.
  • Build end-to-end machine learning pipelines for data preprocessing, model training, and deployment.
  • Use AWS services, including Amazon SageMaker, for machine learning on the cloud.
  • Understand ethical implications and challenges related to bias, privacy, and transparency in machine learning and generative AI.

Career Outcomes


Expert-designed, industry-aligned test. Benchmark test for job skills. Access corporate job listings on the TCS ion portal Improved employability quotient Higher salary package Aligned with industry standards.

Showcase your Talent

Certified candidates get more attention from recruiters which increases your placement quotient.

21st Century In-demand Skills

Prepare students on 21st century skills to keep up to date on the Industry demand.

Augment Student Employability

Augment student employability with Industry relevant certification over and above the academic curriculum.

Job Listing Platform

Avail the assistance of TCS ion Job Listing Platform where over 2300 corporate partners are hiring from.

Project Showcase

Project Topic: Integrated Agriculture Analysis
Business Domain: Agriculture and Prediction System
Integrated agriculture analysis is the process of combining various data sources and analytical techniques to provide insights into the performance and sustainability of agricultural systems. This approach integrates data from a range of sources, including weather patterns, soil characteristics, crop yields, and market trends, to provide a comprehensive understanding of agricultural systems.

Project Topic: Smart Retail Analytics: Supermarket Chain
Business Domain: Retail and FMCG
The project should support analytical activities related to sales of products along with customer requirements, availability, and procurement of products. This should include features related to KPI identification, EDA, association analysis of items, smart visualizations using interactive reports and dashboards.

Project Topic: Cyber Threat Analysis on Android Apps
Business Domain: Cyber Security
Cyber threat analysis on Android apps involves using machine learning algorithms to identify and analyse potential security threats in Android apps. With millions of apps available on the Google Play Store, it is important to ensure that these apps do not contain malicious code or vulnerabilities that can be exploited by attackers. To perform cyber threat analysis on Android apps using machine learning, the first step is to collect a dataset of known threats and non-threats. This dataset can be used to train a machine learning model to identify patterns and anomalies that may indicate the presence of a threat.

Project Topic: Interactive Behavior Analysis in discussions
Business Domain: Human Behavior Analytics
This project would focus to analyze interactive human behavior under different situations and discussions like presentations, interviews etc. to understand the emotional stability under different conditions. The system should comprise of deep learning models that can analyze patterns and behaviors from bot based textual interactions as well as from facial expressions and other behavioral patterns captured through system camera while interacting. The analyzer module should be capable of comparing emotions from both text and images and deduce appropriate results.

Project Topic: People’s behavior analysis in Chat Message
Business Domain: Social Media Analysis
People's behavior analysis in chat messages involves using machine learning algorithms to analyze the language used in chat messages to gain insights into the behavior and personality of individuals. For example, sentiment analysis can be used to identify whether a person's messages are generally positive, negative, or neutral in tone. Word choice analysis can be used to identify the types of words and phrases that a person uses frequently, which can provide insights into their interests and personality.

Project Topic: Intelligent Property Analyzer using MLOPs
Business Domain: Real Estate Market
To develop a ML-powered automated system using Machine Learning operations on Cloud to accomplish tasks to predict the approximate price of a housing property based on multiple factors related to the property. This should comprise of MLOps and pileline-based Data Wrangler, feature store, detect imbalance and bias as data would be transferred dynamically and the model shouldbe capable of considering latest data inputs for prediction.

Meet Our Instructors

Learn from the best at Amity Online with world-class
education and leading international faculty.

Are you ready to take the next step in your career ?

Frequently Asked Questions

Classified as one of the upcoming technologies, Aritificial Intelligence or AI is the ability of a robot or a machine, to perform complex tasks, without human input. AI can further be divided into four categories, namely, Reactive Machines, Limited Memory, Theory of Mind and Self-Awareness. A few areas where AI can be efficiently implemented include Automation, Machine Learning, Machine Vision, Robotics, Self-Driving Cars and more.

AI Engineers tend to play a vital role in building an infrastructure around AI. Apart from defining problems to be solved and chalking out strategies, AI Engineers are also required to use programming algorithms to deploy, test and build AI models. In order to be a successful AI Engineer, an individual must be well-versed in several programing languages like Scala, TypeScript, C++, R, Python Java. To add to the tally, the engineers Should also display expertise in Linear Algebra, Calculus, Statistics and Probability. This further helps the AI Engineers to have a better understanding of AI models including Gaussian Mixture Models, Hidden Markov, Linear Discriminant Analysis and Naïve Bayes.

A course in Artificial Intelligence course equips individuals to become an AI specialist by providing them with an overview of AI technology and processes. Educational qualification from recognised institutes like Amity University and Future Academy also tend to go a long way, in helping aspirants bag jobs in reputed organizations across the globe as an AI specialist! The AI specialists also need to shoulder several responsibilities including carrying out statistical analysis on the systems created, monitoring pre-existing systems, developing the infrastructure for data transformation and data ingestion and converting Machine Learning Models to APIs.

Professionals with thorough knowledge of AI models and the evolving market trends can make a flourishing career in the field of AI. A few job opportunities that an AI Engineer or a specialist can apply for include Machine Learning Engineer, Computer Vision Engineer, Research ML/AI, Business Intelligence Developer, Big Data, Architect, NLP Engineer and more.

Aspiring candidates should have completed class 12th from any recognized Education Board.