Machine Learning

1. Introduction to Artificial Intelligence : 5 Hours Approx.

Overview of AI concepts and history
AI problem-solving techniques

2. Probability and Statistics for AI : 15 Hours Approx.

Probability theory
Statistical inference
Bayesian reasoning

3. Machine Learning Fundamentals : 20 Hours Approx.

Supervised learning
Unsupervised learning
Evaluation metrics and model selecƟon

4. Data Preprocessing and Feature Engineering : 14 Hours Approx

Data cleaning and preprocessing techniques
Feature selection and extraction
Handling missing data and outliers

5. Deep Learning : 18 Hours Approx.

Neural networks and activation functions
Convolutional neural networks (CNNs)
Recurrent neural networks (RNNs)
Generative adversarial networks (GANs)
Natural Language Processing (NLP)

6. AI Ethics and Responsible AI : 8 Hours Apporx

Ethical considerations in AI development and deployment
Bias and fairness in AI algorithms
Privacy and security concerns
Regulatory and legal aspects of AI

7.Advanced Topics in AI (Choose electives based on student interest) : 12 Hours Approx.

Generative models (e.g., Variational Autoencoders)
Transfer learning and domain adaptation
Explainable AI and interpretability.
Reinforcement learning in robotics

Capstone Project : 32 Hours Approx.

Apply data science concepts learned throughout the curriculum to build a realworld project or solve a specific problem.

Empowering Minds to Shape the Future with ML

At Terra Asset, we are dedicated to unlocking the potential of Machine Learning (ML) and empowering individuals with the knowledge and skills to drive innovation. Join us to gain cutting-edge knowledge, hands-on experience, and industry-leading training that will prepare you to shape a future powered by ML technology.