Course Overview
Welcome to the Machine Learning with Python course, where you’ll embark on a journey to master the fundamentals of machine learning and its practical implementation in Python. This course is designed to provide a comprehensive introduction to the world of machine learning, starting with essential concepts and progressing to real-world applications.
We will explore various machine learning techniques, including supervised learning methods like Linear Regression, Support Vector Machines (SVM), and Decision Trees. You’ll learn how to prepare data for analysis, choose suitable models, train them effectively, and evaluate their performance. The course also covers essential topics like parameter tuning to optimize model accuracy and deploying machine learning models in real-world scenarios.
Additionally, we’ll dive into popular machine learning libraries, such as Scikit-learn and TensorFlow. You’ll discover how to leverage these powerful tools through practical examples and hands-on exercises, learning the basics of their APIs and applying them to build and deploy models.
