This course is designed to provide you with a comprehensive understanding of
the fundamental concepts of Machine Learning and how to effectively
implement them using Python. You will learn essential theories, algorithms,
and techniques that form the foundation of Machine Learning, including
supervised and unsupervised learning, regression, classification,
clustering, and neural networks.
Throughout this course, you will gain hands-on experience by working with
real-world datasets and implementing Machine Learning models using popular
libraries such as Scikit-Learn, TensorFlow, and Pandas. The course covers
both basic and intermediate skills, ensuring that you develop a strong
understanding of data preprocessing, model evaluation, feature engineering,
and optimization techniques.
Basic Knowledge of Programming