MAA Academy

Welcome to the Gen AI & ML Program: Bridging Theory and Practical Mastery in the World of Intelligent Solutions!

In an era where technological advancements are unfolding at an unprecedented pace, the realm of industries is undergoing a metamorphosis. In this transformative landscape, our Gen AI & ML Program emerges as the definitive gateway, guiding you to master the intricate art and exacting science of Artificial Intelligence (AI) and Machine Learning (ML)

Course Fees: Rs. 1,39,000 (incl. of GST)

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A Holistic Learning Expedition

Our Gen AI & ML Program is not just an academic pursuit; it’s a journey designed to encompass the breadth and depth of AI and ML domains. This meticulously designed program weaves together the strands of theoretical understanding and practical acumen. It’s not enough to grasp the concepts; you will delve into the practical nuances, transforming theory into functional tools that can drive tangible outcomes.

Module 1: Intro to Information & Python for Data Science
  1. Acquire proficiency in Python programming, including libraries like NumPy, pandas, and BeautifulSoup.
  2. Develop skills in web scraping and API usage for data retrieval.
  3. Perform data analysis and demonstrate the ability to communicate findings.
Module 2: Data Preprocessing & Big Data
  1. Understand the concepts of big data and the 3V’s (Volume, Velocity, Variety).
  2. Learn to handle real-time data streams and perform data cleaning and encoding.
  3. Apply data preprocessing skills to real-world datasets.
Module 3: Statistics & EDA (Exploratory Data Analysis)
  1. Differentiate between descriptive and inferential statistics.
  2. Conduct hypothesis testing and effectively communicate statistical results.
  3. Apply various visualization techniques to explore and communicate data insights.
  4. Analyze real-world data using statistics and EDA.
Module 4: Machine Learning Basics
  1. Gain proficiency in regression and classification models.
  2. Learn model selection techniques and evaluate model performance.
  3. Apply machine learning concepts to real-world prediction tasks.
Module 5: Advanced Regression & SVM (Support Vector Machines)
  1. Explore advanced regression techniques and support vector machines (SVM).
  2. Build and evaluate machine learning models for image classification.
  3. Apply regression and SVM to solve real-world problems.
Module 6: Unsupervised Learning
  1. Master clustering techniques and feature engineering.
  2. Segment a market using unsupervised learning techniques.
  3. Apply unsupervised learning to real-world data.
Module 7: Natural Language Processing
  1. Understand lexical, syntactic, and semantic processing in NLP.
  2. Work with pre-trained language models.
  3. Conduct sentiment analysis on social media data.
  4. Apply NLP techniques to real-world text data.
Module 8: Intro to Deep Learning and Artificial Intelligence (AI)
  1. Explore neural network architectures and Convolutional Neural Networks (CNNs).
  2. Build deep learning models for digit recognition.
  3. Discuss advanced AI concepts in class.
Module 9: Advanced Deep Learning and AI
  1. Dive into advanced deep learning concepts, including RNNs, GANs, and transfer learning.
  2. Apply advanced deep learning techniques to gesture recognition.
  3. Collaboratively present group projects.
Module 10: Classical Reinforcement Learning
  1. Understand classical reinforcement learning techniques like Q-Learning and Markov Decision Processes.
  2. Apply reinforcement learning to game simulation.
  3. Solve reinforcement learning problems through assignments and quizzes.
Module 11: Deep Reinforcement Learning in AI
  1. Explore deep reinforcement learning techniques, including DQN and advanced gradient methods.
  2. Apply deep reinforcement learning to robot navigation.
  3. Assess your understanding through assignments and tests.
Module 12: Generative AI
  1. Introduction to Generative AI
    1. What is Generative AI?
    2. Evolution of Generative AI
    3. Classical ML vs Gen AI
    4. How Gen Al works?
    5. LLMs explained
    6. Ethical considerations
  2. Introduction to Prompt Engineering
    1. Basics of prompts
    2. Elements of prompts
    3. General tips for designing of prompts
    4. Examples of prompts
    5. Prompt techniques
      1. Zero shot prompting
      2. Few shot prompting
      3. Prompt chaining
      4. Contextual prompting
      5. Scenario based prompting
  3. AI Tools
    1. ChatGPT 3.5/40
    2. Claude 2
    3. Gemini
    4. Luma-Dream machine
    5. Scribe
  4. Project
    1. Creating a sophisticated Al personal assistant application that utilizes advanced generative Al techniques to deliver contextually relevant and accurate responses, enhancing user interaction and utility.
Module 13: Advanced AI/ML Topics
  1. Delve into advanced topics like transfer learning and time series forecasting.
  2. Apply advanced AI and ML techniques to real-world problems.
  3. Complete assignments to demonstrate proficiency.
Module 14: Big Data & Cloud Computing
  1. Understand distributed computing and its role in handling big data.
  2. Deploy machine learning models in cloud environments.
  3. Assess your knowledge through assignments and quizzes.
Module 15: Capstone AI/ML
  1. Apply the complete data science workflow to real-world industry problems.
  2. Collaborate on a capstone project related to a specific industry challenge.
  3. Present your project and provide a comprehensive report.

These learning outcomes demonstrate the skills and knowledge you will gain as you progress through each module of the AI & ML program, ensuring a well-rounded understanding of artificial intelligence and machine learning concepts and their practical applications.

The Nexus of Knowledge
and Implementation

At the core of our program lies a dual commitment: equipping you with the theoretical foundations that underpin decision-making in AI and ML, and empowering you with the practical dexterity to architect solutions that have real-world impact. Our goal is for you to emerge not only well-versed in AI and ML concepts but also adept at translating these ideas into actionable solutions that can address real challenges.

Building the Pillars of Understanding

Foundational Mathematics & Statistics

Your journey begins with an exploration of the mathematical bedrock that supports AI and ML. From probability to linear algebra, you’ll gain insights into the mathematical machinery that underlies data-driven insights.


Regardless of your coding background, this module ensures everyone is well-prepared. Dive into algorithms, data structures, and programming paradigms to set a strong foundation.

Python & Linux

Python, the lingua franca of AI and ML, is demystified. You’ll not only learn the language but also navigate the Linux environment, a vital tool for data scientists.

Data Science with
Python (IBM)

Elevate your skills through an IBM-certified Python module. From advanced libraries to collaborative IBM projects, you’ll be prepared to navigate real-world complexities.

Interactive Learning
Beyond Conventions

Embark on Your AI & ML Odyssey

Welcome to an educational journey that melds theoretical underpinnings with practical applications. Immerse yourself in live interactive sessions led by experts, transcending traditional learning boundaries. With IBM’s endorsement, you’re poised to excel in a rapidly evolving landscape. Embark on this transformative expedition and be prepared to shape the future with the power of AI and ML. The AI & ML Program: Where Mastery is the Confluence of Knowledge and Implementation. Your voyage into intelligence begins now. Welcome to MAA Academy.

Key features of the program

Interactive Learning and Practical Application

Live Interactive Sessions with Experts: Engage in real-time sessions with subject matter experts, enabling in-depth discussions and immersive learning. Hands-On Practical Sessions: Gain practical experience through comprehensive hands-on sessions with tools like TensorFlow and PyTorch.

Comprehensive Curriculum with Real-World Relevance

Real-World Case Studies and Projects: Reinforce learning through practical case studies and projects that simulate real-world scenarios. Benefit from IBM’s expertise with modules and industry-relevant projects.

Theoretical Foundations & Practical Application

Immerse in both theoretical understanding and hands-on application to become a well-rounded AI and ML practitioner

In-Depth Exposure to AI Concepts

Essential AI Concepts: Delve into a wide range of AI topics including Generative AI, NLP, computer vision, and deep learning techniques.

Ethical AI Considerations

Ethics and Best Practices: Explore the ethical dimensions of AI model development and deployment, aligning AI advancements with responsible practices.

Industry Preparedness and Networking

Industry-Specific Tools: Acquire skills in industry-relevant tools like Power BI and web scraping, positioning yourself for a competitive edge.

Deployment Strategies and Monitoring

Learn deployment strategies and monitoring techniques to ensure AI models operate effectively.

Transformative Learning Experience

Live Online Classes: Engage in interactive live sessions, offering direct access to experts and facilitating real-time learning.

Practical Skill Application

The program focuses on practical application, ensuring you can confidently implement AI and ML solutions.

Program Outcomes

Solid Foundation in AI & ML Fundamentals

Graduates will have a strong understanding of the foundational principles of Artificial Intelligence and Machine Learning, including mathematical concepts, statistics, programming, and data science essentials.

Proficiency in Python and Programming

Learners will acquire proficiency in Python programming, enabling them to develop, implement, and optimize AI and ML algorithms effectively.

Data Handling and Analysis

Graduates will be adept at handling various types of data, utilizing Python libraries and tools to analyze, visualize, and draw meaningful insights from data.

Advanced Machine Learning Techniques

Learners will master a wide range of machine learning techniques, from linear algebra to deep learning, enabling them to create predictive models and algorithms

Expertise in Deep Learning and Neural Networks

Graduates will have a comprehensive understanding of deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), allowing them to solve complex problems like image recognition and sequence analysis.

Natural Language Processing Competence

Learners will be well-versed in Natural Language Processing (NLP), allowing them to process and analyze textual data, build chatbots, and work with pre-trained language models.

Data Wrangling and Preparation

Graduates will be skilled in data preprocessing, cleaning, and manipulation, ensuring that data is prepared for accurate analysis and modeling.

Applied AI in Real-world Scenarios

Learners will have the ability to apply AI and ML techniques across various industries and departments, leveraging real-time data and APIs to make informed decisions.

Ethical AI Development

Graduates will understand the ethical considerations associated with AI model development and deployment, ensuring that their solutions are developed responsibly and ethically.

Big Data Management and Cloud Integration

Learners will have the expertise to handle big data and deploy machine learning models on cloud platforms, ensuring scalability and efficiency.

Deployment and Monitoring of AI Models

Graduates will be equipped to deploy, monitor, and maintain AI models in production environments, ensuring their continued effectiveness and performance.

Collaboration with Industry Tools

Learners will gain hands-on experience with industry tools such as Power BI and advanced web scraping techniques, enhancing their ability to create industry-relevant solutions.

Certification and Recognition

Completing the program will provide learners with IBM-certified modules, recognized in the industry, showcasing their expertise and enhancing their employability.

Interactive Learning Skills

Graduates will have honed their interactive learning skills through live sessions with subject matter experts, equipping them with the ability to engage in real-time discussions and collaborative problem-solving.

Comprehensive AI Portfolio

Learners will leave the program with a well-rounded portfolio of projects and practical implementations, demonstrating their ability to apply AI and ML techniques to real-world challenges.

Gen AI Project

Creating a sophisticated AI personal assistant application that utilizes advanced generative AI techniques to deliver contextually relevant and accurate responses, enhancing user interaction and utility.

Overall, completing this AI & ML program will empower learners with a comprehensive skill set, enabling them to drive innovation, make informed decisions, and contribute effectively to the field of Artificial Intelligence and Machine Learning across various industries.

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