Program Objectives:

• To introduce participants to the fundamentals of machine learning.
• To provide hands-on experience with ML tools and libraries.
• To train participants in data preprocessing, model building, and evaluation.
• To explore the deployment and optimization of ML models.
• To discuss emerging trends and ethical considerations in ML.

What you will learn?

Day 1: Understanding Machine Learning & Its Ecosystem

• Introduction to Machine Learning (ML) and its relationship with AI
• How ML works: Datasets, Algorithms, and Functions
• Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning
• Difference between Machine Learning & Deep Learning
• Python & ML: Why Python is the preferred language?
• Industry Applications: AI-ML in healthcare, finance, security, and automation
• Q&A Session

Day 2: ML Models & Algorithms – Building the Foundation

• Understanding ML Models: Regression, Classification, Clustering
• Supervised Learning: Linear & Logistic Regression, Decision Trees
• Unsupervised Learning: K-Means Clustering, PCA
• Deep Learning Overview (Introduction to Neural Networks)
• Model Optimization: Overfitting, Underfitting, Bias-Variance Tradeoff
• Case Study: Real-world ML Model Implementation
• Q&A Session

Day 3 : Hands-on with Python & ML Tools

• Setting up Python for ML (Jupyter Notebook, Anaconda)
• Important Python Libraries

Day 4 : Hands-on with Python & ML Tools

• Step-by-Step Building ML Models in Python
• Implementing Machine Learning Algorithms using python
• Q&A Session

Day 5: Real-World Applications & Career Pathways in ML

• Career Paths in Machine Learning: ML Engineer, Data Scientist, AI Researcher
• How to Master ML: Certifications, Courses, and Learning Platforms
• Building Your ML Portfolio: Open-source projects & Kaggle competitions
• Industry Collaboration: ML use-cases in academia & corporate settings
• Live Q&A & Closing Remarks