What I will learn?
- Understand the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML).
- Work with Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for data analysis and visualization.
- Master supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction.
- Solve real-world problems by applying AI and ML techniques in projects like recommendation systems and fraud detection.
- Develop predictive models using algorithms like Linear Regression, Decision Trees, Random Forest, and Neural Networks.
- Gain hands-on experience with deep learning frameworks like TensorFlow and PyTorch.
- Work on Natural Language Processing (NLP) and Computer Vision tasks using state-of-the-art tools.
- Build and deploy AI/ML models on cloud platforms like AWS and Google Cloud.
About Course
This course is designed for aspiring data scientists, AI engineers, and ML practitioners. Whether you are new to AI/ML or want to deepen your expertise, this course offers a comprehensive guide to the concepts, tools, and applications of AI and ML. Through a mix of theory, coding exercises, and real-world projects, you’ll gain the skills needed to excel in the growing field of AI and Machine Learning.
Course Curriculum
Requirements
- A basic understanding of computers and the internet.
- Familiarity with programming fundamentals (helpful but not mandatory).
- A laptop/PC with an internet connection.
Material Includes
- Access to live coding sessions and recorded lectures.
- Downloadable resources: code templates, project files, and cheat sheets.
- Hands-on assignments and quizzes for every module.
- Certificate of completion upon successfully finishing the course.
