Machine Learning & AI

Machine Learning (ML) and Artificial Intelligence (AI) represent transformative fields at the intersection of computer science and data analysis. Machine Learning involves the development of algorithms and models that enable computer systems to learn and make predictions or decisions based on data patterns without explicit programming.

On the other hand, Artificial Intelligence encompasses broader concepts where machines mimic human cognitive functions, including learning, problem-solving, and decision-making. ML and AI technologies are rapidly advancing and finding applications in various industries, ranging from healthcare and finance to manufacturing and marketing.

The power of ML lies in its ability to uncover insights from vast datasets, while AI extends this capability to intelligent decision-making and problem-solving. These technologies are leveraged in tasks such as image recognition, natural language processing, recommendation systems, and autonomous vehicles, shaping the way we interact with and benefit from technology in our daily lives.

As the fields of ML and AI continue to evolve, they hold immense potential to drive innovation, efficiency, and transformation across industries, ushering in a new era of intelligent computing.

Syllabus:

→ Introduction to Machine Learning and AI
• Overview of ML and AI concepts
• Historical context and evolution
• Applications in various industries
→ Foundations of Statistics and Probability
→ Python Programming for ML and AI
→ Supervised Learning
→ Unsupervised Learning
→ Model Evaluation and Hyperparameter Tuning
→ Natural Language Processing (NLP)
→ Computer Vision
→ Reinforcement Learning
→ Deployment of ML Models
→ Ethical and Responsible AI
→ Case Studies and Real-World Projects
→ AI and ML in Business Strategy
→ Capstone Project