This machine learning course provides a complete introduction to core concepts inspired by Andrew Ng’s well-known training program. It is designed for beginners who want to understand how machines learn from data and apply these techniques in real-world applications.
The course starts with an overview of what machine learning is and how it is used in modern artificial intelligence systems. It then explains the two main learning approaches: supervised learning and unsupervised learning. Supervised learning focuses on labeled data where the model learns from input-output pairs, while unsupervised learning discovers hidden patterns in unlabeled datasets.
You will also explore regression techniques, including training and testing models to evaluate performance. Key mathematical concepts such as cost function and gradient descent are explained in a simple and intuitive way to help learners understand how models optimize themselves.
In addition, the course introduces practical tools like Jupyter Notebook, which is widely used for writing and testing machine learning code.
By the end of this course, learners will have a strong foundation in machine learning fundamentals, including how algorithms learn, how errors are minimized, and how models are improved using optimization techniques. This knowledge is essential for anyone planning to enter data science, AI, or machine learning engineering.
