Machine Learning: Foundations, Tools, and Future Trends

The Machine Learning: Foundations, Tools, and Future Trends workshop provides an in-depth introduction to the principles of machine learning, covering key concepts, tools, and applications. Participants will learn about supervised and unsupervised learning techniques, model training, evaluation, and deployment. The workshop will also highlight future trends such as deep learning, AI ethics, and automation in ML development.

• 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.

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

• Students, researchers, and professionals interested in AI/ML
• Data scientists and software developers
• Business analysts and engineers exploring ML applications
• Entrepreneurs and innovators looking to integrate ML into their businesses