Mastering Machine Learning Algorithms with Python provides a comprehensive understanding of key machine learning techniques and how to apply them using Python. The course covers essential concepts like data preprocessing, model training, evaluation, and optimization, equipping you with the skills to build and fine-tune machine learning models.
The course begins with an introduction to machine learning, covering its history, terminology, and types of algorithms. You'll explore how data influences model outcomes and gain insights into common challenges in the field. Additionally, statistical techniques such as hypothesis testing and probability theory will be introduced to strengthen your model development.
Next, you'll dive into Python programming, mastering data structures such as Pandas DataFrames and NumPy arrays. You’ll implement algorithms like linear regression and logistic regression, alongside practical projects like predicting car prices and classifying telecom churn.
This course is ideal for learners with basic programming knowledge and an interest in machine learning. It’s recommended to have familiarity with Python and statistics. No prior machine learning experience is required.
Applied Learning Project
Throughout this course, you will engage in projects that involve building machine learning models using algorithms like linear regression, logistic regression, and K-means clustering. These hands-on projects will allow you to apply your knowledge to real-world datasets and solve authentic problems.
