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Learn the core concepts of AI & Machine Learning, from basics to real-world applications, step by step
“This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content”
Are you ready to explore the world of Artificial Intelligence (AI) and Machine Learning (ML)? This beginner-friendly course will give you the foundational knowledge and practical skills to understand, apply, and evaluate AI systems with confidence.
In this course, you’ll start by learning what AI is, its history and evolution, and how it is transforming industries such as healthcare, finance, education, and transportation. You’ll gain a solid understanding of core concepts like supervised learning, unsupervised learning, and reinforcement learning, along with the mathematics that make AI work—linear algebra, probability, and optimization.
Next, you’ll dive into machine learning models and learn how to build and evaluate them using Python libraries such as NumPy, Pandas, and Scikit-learn. You’ll also explore the basics of deep learning, including neural networks, CNNs, and RNNs, and discover how they power applications like image recognition and natural language processing.
Beyond the technical side, this course emphasizes the importance of ethical AI. You’ll learn about bias, fairness, accountability, privacy, and security, ensuring that you can think critically about the impact of AI in society.
By the end of this course, you’ll have the confidence to understand and explain AI concepts, build simple ML models, and take the next step toward becoming a data scientist, ML engineer, or AI professional.
Take your first step into the exciting world of Machine Learning and Artificial Intelligence today!
Course Content
Introduction to Machine Learning and AI
- 00:00
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History and Evolution of AI
00:00 -
Applications of AI in Real Life
04:34 -
AI vs Machine Learning vs Deep Learning
03:54 -
Your First ML Experiment – Hands on Lab
Section 2: Foundations of Machine Learning
Section 3: Linear Regression & Polynomial Regression
Section 4: Unsupervised Learning
Section 5: Neural Networks & Deep Learning
Section 6: Reinforcement Learning
Section 7: Natural Language Processing (NLP)
Section 8: Computer Vision
Section 9: Ethics and Future of Al
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