Artificial Intelligence (AI) and Machine Learning (ML)Program


Program Description:

The comprehensive AI & Machine Learning program at Indepth Research Institute (IRES) has been designed to equip participants with the fundamental knowledge and practical skills necessary to thrive in the rapidly evolving field of artificial intelligence and machine learning. Spanning across three modules, this program covers a wide array of topics ranging from foundational concepts to advanced techniques, ensuring participants gain proficiency in key areas of AI and ML.

Through an immersive curriculum, participants will delve into topics such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. They will learn to preprocess data, build and evaluate machine learning models, and optimize algorithms for improved performance. Hands-on experience with popular libraries and tools like TensorFlow, Scikit-Learn, and PyTorch will empower participants to tackle real-world AI challenges with confidence.

Duration: 3 Months

Mode of Delivery: Virtual

Who Should Attend?

  • Aspiring Data Scientists
  • Software Engineers and Developers
  • Data Analysts and Business Intelligence Professionals
  • Researchers and Academics
  • Entrepreneurs and Business Leaders
  • Anyone Interested in AI and ML

Program Objectives:

  • Equip participants with a solid understanding of the fundamental principles and theories underlying artificial intelligence and machine learning.
  • Develop hands-on skills in implementing machine learning algorithms and models using Python and relevant libraries.
  • Train participants to effectively preprocess, clean, and manipulate data for machine learning tasks.
  • Enable participants to construct, train, evaluate, and interpret various machine learning models, including supervised, unsupervised, and reinforcement learning models.
  • Foster the ability to formulate and solve real-world problems using appropriate machine learning techniques and tools.
  • Enhance participants' ability to critically analyze and interpret the results of machine learning models and make informed decisions.
  • Ensure participants are proficient in using essential tools and libraries for machine learning, including Jupyter Notebooks, Scikit-Learn, TensorFlow, Keras, and PyTorch.

Learning Outcomes:

By the end of the program, participants will be able to:

  • Gain a comprehensive understanding of the theoretical foundations of AI and machine learning.
  • Develop practical skills in implementing machine learning algorithms using Python and relevant libraries.
  • Acquire the ability to preprocess, analyze, and visualize data effectively.
  • Learn to evaluate and improve machine learning models using appropriate metrics and techniques.
  • Understand the applications and limitations of different machine learning methods and techniques.

Program Pre-requisites

Participants are expected to have:

  • Proficiency in Python, including understanding of basic syntax, control structures (loops, conditionals), and functions.
  • Familiarity with Python data structures (lists, dictionaries, sets, tuples).
  • Understanding of basic linear algebra (vectors, matrices, operations).
  • Basic calculus (derivatives, integrals) to understand optimization and gradient descent.
  • Fundamental concepts in probability and statistics, including distributions, mean, variance, standard deviation, and hypothesis testing.
  • Basic understanding of machine learning concepts, including supervised, unsupervised, and reinforcement learning.
  • Familiarity with the concept of training, validation, and test datasets.
  • Experience with data manipulation libraries such as Pandas and NumPy.
  • Ability to read and write data in different formats (CSV, JSON).
  • Familiarity with data visualization libraries such as Matplotlib and Seaborn.
  • Understanding of how to create basic plots (line plots, scatter plots, histograms).
  • Basic knowledge of using Jupyter Notebooks for interactive coding and data exploration.
  • Familiarity with version control systems, especially Git, for managing code.

Tools

  • Python
  • Microsoft Azure
  • OpenAI Gym

Module 1: Supervised Learning

The Supervised Learning module focuses on mastering the fundamental principles and techniques of machine learning. Participants will learn to make predictions or decisions from labeled data through activities such as data preprocessing, model selection, training, and evaluation. Key goals include developing predictive models that generalize well to unseen data and understanding underlying algorithms. Challenges include overfitting, underfitting, and dealing with imbalanced datasets. Hands-on experience with popular algorithms and Python libraries will equip participants to build and deploy supervised learning models effectively.

Module 2: Unsupervised Learning

The Unsupervised Learning module is designed to explore the principles and methodologies of learning from unlabeled data. Participants will delve into techniques for uncovering hidden patterns, structures, and relationships within datasets without explicit guidance. Activities in this module include clustering, dimensionality reduction, and anomaly detection, aimed at gaining insights from unstructured or unlabelled data. Challenges in unsupervised learning encompass determining the optimal number of clusters, handling high-dimensional data, and interpreting results accurately. By the module's conclusion, participants will possess the skills to apply unsupervised learning algorithms effectively in various domains, enhancing their ability to extract valuable insights from complex datasets.

Module 3: Reinforcement Learning

The Reinforcement Learning module focuses on understanding and implementing algorithms that enable agents to learn through interaction with an environment. Participants will explore the principles of reinforcement learning, where agents aim to maximize cumulative rewards by taking actions based on past experiences. Key activities include understanding the concepts of states, actions, rewards, and policies, and implementing algorithms such as Q-learning and deep Q-networks. Challenges in reinforcement learning include balancing exploration and exploitation, handling continuous action spaces, and dealing with sparse rewards. By the end of the module, participants will gain practical experience in developing and deploying reinforcement learning agents to solve complex decision-making problems effectively

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Program Experience

Program Delivery

Delivered via video lectures.

Real-World Examples

Delivered through a combination of video and live online lectures.

Applications to Data Sets

Learn through individual assignments and feedback.

Debrief of Learnings

Delivered through a combination of recorded and live video lectures.

Certificate

Upon successful completion of the program, you will earn a certificate of completion from Indepth Research Institute.

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