Machine learning skills are in high demand, since algorithms now drive much of the trading on Wall Street and the product recommendations behind major companies like Amazon, Spotify, and Netflix. This course opens with foundational machine learning concepts such as linear and logistic regression, both essential tools for solving machine learning problems. From there, you'll explore more advanced algorithms, including k-nearest neighbors, decision trees, and random forests. Along the way, you'll be introduced to important statistical concepts like bias, variance, and overfitting. You'll also learn how to evaluate the performance of your models and gain insight into choosing the most effective features and algorithms for your work.
The course centers on practical skills, equipping you with the tools you need to solve real-world problems using machine learning. While the mathematical foundations of each algorithm are explained visually, no formal math component is included. You should be comfortable with Python programming and libraries such as NumPy and Pandas. If you're new to Python, it's a good idea to build those fundamentals before enrolling.
What You Will Learn
- How to clean and balance your data using the pandas library
- Applying machine learning algorithms such as logistic regression and random forests with the scikit-learn library
- Choosing strong features to use as input for your algorithms
- Properly splitting data into training, testing, and cross-validation sets
- Important theoretical concepts like overfitting, variance, and bias
- Evaluating how well your machine learning models perform
This course is available for 29 days. You can choose when to start your access period. Once you activate, you will have 29 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
