Aim

To equip AI professionals with core machine learning and AI skills, so they can innovate and advance in their careers.

Program Structure

Introduction to Machine Learning and AI

• A broad overview of machine learning and AI.
• Historical development and how it has evolved.
• Important terms and key concepts.

Supervised Learning

• Linear regression and classification techniques.
• Decision trees and random forests.
• Support vector machines (SVM).
• Model evaluation and performance metrics.

Unsupervised Learning

• Clustering methods (K-means, Hierarchical).
• Techniques for dimensionality reduction (PCA, LDA).
• Anomaly detection approaches.

Neural Networks and Deep Learning

• Introduction to neural networks.
• Key principles of deep learning.
• Convolutional neural networks (CNN).
• Recurrent neural networks (RNN).
• Transfer learning concepts.

Natural Language Processing (NLP)

• Text preprocessing and tokenization methods.
• Sentiment analysis techniques.
• Topic modeling.
• Sequence models and LSTM.
• Transformer models like BERT.

Practical Machine Learning

• Using Python and Jupyter Notebooks for coding.
• Building models with TensorFlow and Keras.
• Implementing advanced deep learning using PyTorch.
• Applying scikit-learn for machine learning algorithms.

Participant’s Eligibility

• Final-year undergraduates and postgraduates in Computer Science or related fields.
• Professionals in IT, data science, and software development aiming to boost their AI knowledge.

Program Outcomes

• Develop a strong foundation in machine learning and AI.
• Gain expertise in supervised and unsupervised learning methods.
• Get hands-on experience with neural networks and deep learning.
• Learn to apply natural language processing in real-world applications.
• Master popular machine learning frameworks like TensorFlow, Keras, PyTorch, and scikit-learn.
• Complete practical projects to demonstrate your knowledge of machine learning.
• Earn a certificate of completion, endorsed by top industry professionals, to enhance your career.

Program Deliverables

• Access to an e-LMS platform.
• Real-time dissertation projects.
• Project mentorship and guidance.
• Opportunity to publish research papers.
• Self-assessment tools.
• Final exam.
• e-Certificate.
• e-Marksheet.

Future Career Prospects

• Machine Learning Engineer: Build and refine machine learning models and algorithms.
• Data Scientist: Analyze and interpret complex data to support business decisions.
• AI Researcher: Push the boundaries of artificial intelligence through research.
• AI Developer: Design and implement AI solutions.
• NLP Specialist: Work on language-based AI tasks like text and speech processing.
• Business Intelligence Analyst: Leverage AI to guide business strategy and improve operations.