AI/ML Fundamentals

Course Objectives:

•  Provide comprehensive trainings on AI/ML concepts, techniques and applications.

•  Equip students and professionals with practical skills to build and deploy AI/ML models in real-world scenarios.

•  Enable students and professionals to understand the end-to-end lifecycle of AI/ML projects from data preprocessing to model evaluation and deployment.

•  Offer hands-on experience in implementing machine learning algorithms and neural networks.

•  Prepare students for roles such as Data Scientist, Machine Learning Engineer or AI/ML Specialist in the industry.

Topics Covered:

1. Introduction to AI and Machine Learning

• AI/ML Fundamentals

• Data Analysis and Visualizations

2. Data Preprocessing and Feature Engineering

• Data Preparation for Machine Learning

• Feature Engineering

3. Supervised Learning - Regression and Classification

• Regression Techniques

• Classification Algorithms

4. Unsupervised Learning - Clustering and Dimensionality Reduction

• Clustering Techniques

• Dimensionality Reduction

5. Introduction to Neural Networks and Deep Learning

• Neural Networks Fundamentals

• Deep Learning in Industry

6. Advanced Deep Learning and Model Optimization

• Convolutional Neural Networks (CNN)

• Model Optimization and Hyperparameter Tuning

7. Natural Language Processing (NLP)

• Introduction to NLP

• Deep Learning for NLP

8. AI Model Deployment and Real-World Use Cases

• Model Deployment Strategies

• AI in Industry - Case Studies and Challenges

9. Capstone Project & Industry Applications

• Capstone Project: Students and professional's will work on a real-world AI/ML project, where they:

  • •  Collect, preprocess, and analyze data.

    •  Build and evaluate ML models.

    •  Deploy the model using a cloud-based platform.

    •  Present their project in a professional setting, simulating an industry environment.