Post Graduate Program in AI and Machine Learning 


Hands On Training

Advisory Board members from Industry & Academia

Live interactive session

Access Lecture Recordings After Course Completion

Learn From Faculty And Domain Experts From Industry

Projects spanning throughout the course duration

Industry-Endorsed Curriculum

Placement Assistance

Separate batches for working professional

Who Will Benefit from AI and Machine Learning Program 

Our AI and machine learning course can benefit a wide range of individuals and professionals who are interested in understanding and applying these technologies. Here are some examples of who can benefit from this course:

  1. Students and Researchers: Students pursuing degrees or conducting research in fields such as computer science, data science, mathematics, engineering, or related disciplines can greatly benefit from an AI and machine learning course. It provides them with a solid foundation and practical skills to work with AI and machine learning techniques in their academic or research projects.

  2. Data Scientists and Analysts: Professionals working with data analysis, data mining, and predictive modeling can enhance their skills and stay updated with the latest techniques by taking an AI and machine learning course. It equips them with the knowledge and tools to extract insights from complex datasets, build accurate models, and make data-driven decisions.

  3. Software Engineers and Developers: AI and machine learning are becoming integral parts of many software applications. Software engineers and developers can benefit from understanding the fundamentals of AI and machine learning to incorporate intelligent features into their applications, such as recommendation systems, natural language processing, and computer vision.

  4. Business Professionals and Managers: AI and machine learning have significant implications for businesses across various industries. Executives, managers, and decision-makers can benefit from an AI and machine learning course to gain insights into the potential applications, limitations, and strategic considerations of these technologies. This knowledge can help them make informed decisions and effectively lead AI initiatives within their organizations.

  5. Entrepreneurs and Innovators: Individuals interested in starting their own AI-driven startups or leveraging AI and machine learning to create innovative solutions can benefit from an AI and machine learning course. It provides them with the necessary knowledge to identify opportunities, design intelligent systems, and navigate the technical challenges associated with implementing AI technologies.

  6. Anyone Interested in AI and Machine Learning: Even if you don't fall into any of the categories mentioned above, if you have a keen interest in AI and machine learning and want to understand how these technologies work, their potential, and their impact on society, an AI and machine learning course can be highly beneficial. It can help you develop a deeper understanding and enable you to engage in meaningful discussions on AI-related topics.

Overall, an AI and machine learning course can benefit both technical and non-technical individuals who want to gain a solid understanding of AI and machine learning concepts and applications, regardless of their background or profession.

AI and Machine Learning Course Contents

Introduction to Artificial Intelligence:

  • Definition and history of AI
  • Types of AI (narrow AI vs. general AI)
  • Applications of AI in various fields

Introduction to Machine Learning:

  • What is machine learning?
  • Supervised, unsupervised, and reinforcement learning
  • Training, testing, and validation of models
  • Evaluation metrics for machine learning models


Data Preprocessing

  • Data cleaning and handling missing values
  • Feature selection and feature engineering
  • Data normalization and scaling
  • Dealing with imbalanced datasets

Regression Models:

  • Linear regression
  • Logistic Regression 
  • Polynomial regression
  • Support Vector Regression
  • Decision Tree Regression 
  • Random Forest Regression  
  • Regularization techniques (L1, L2 regularization)
  • Evaluation of regression models

Classification Models:

  • Logistic regression
  • Decision trees and random forests
  • Support vector machines (SVM)
  • Naive Bayes classifiers
  • Evaluation of classification models

Unsupervised Learning

  • Clustering algorithms (K-means, hierarchical clustering)
  • Dimensionality reduction techniques (Principal Component Analysis - PCA, t-SNE)
  • Anomaly detection

Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • Activation functions
  • Backpropagation algorithm
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Transfer learning
  • Deep learning frameworks (TensorFlow, Keras, PyTorch)

Natural Language Processing (NLP)

  • Text preprocessing and tokenization
  • Word embeddings (Word2Vec, GloVe)
  • Sentiment analysis
  • Named Entity Recognition (NER)
  • Language generation models (such as GPT-3)

Reinforcement Learning

  • Markov Decision Processes (MDP)
  • Q-learning
  • Policy gradients
  • Deep Q-Networks (DQN)

Deployment and Ethical Considerations

  • Model deployment and serving
  • Ethical considerations in AI and machine learning
  • Bias and fairness in machine learning
  • Privacy and security concerns

Case Studies and Practical Projects:

  • Real-world applications of AI and machine learning
  • Hands-on projects to apply learned concepts

Job Roles

Participant undergoing the training program can avail following position.

Data Analyst

Data Engineer

Data Scientist

Machine Learning Engineer

Business Analyst

FAQ on AI and Machine Learning

Are there any prerequisites for understanding AI and machine learning concepts?

While a background in mathematics and programming is helpful, many AI and machine learning courses are designed to be accessible to learners with varying levels of expertise. Introductory courses often provide necessary mathematical and programming foundations along the way.

What programming languages are commonly used in AI and machine learning courses?

Python is the most commonly used programming language in the field of AI and machine learning due to its simplicity, extensive libraries (e.g., NumPy, Pandas, TensorFlow, PyTorch), and large community support. Some courses may also cover languages like R or MATLAB for specific applications.

Can I get a certificate upon completing an AI and machine learning course?

Yes, Post Graduate Program in AI and Machine Learning

How long does it take to complete an AI and machine learning course?

The duration of a course can vary widely. Some introductory courses may be completed within a few weeks, while more comprehensive programs can span several months. It depends on the depth of the content, the time commitment required, and the pace at which the learner progresses.

What prerequisites are required to take an AI and machine learning course?

The prerequisites can vary depending on the course level, but generally a solid understanding of programming (often Python), basic mathematics (linear algebra, calculus, probability), and statistics is beneficial. Some advanced courses may require additional knowledge of algorithms and data structures.