The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.
Who is this book for?
This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.
What you will learn
Analyze datasets for insights Scrub data for model readiness Understand key ML algorithms Design and validate models Apply Linear and Logistic Regression Utilize K-Nearest Neighbors and SVMs