A Practical Guide to Machine Learning with Python
|
The goal is to introduce the audience to some fundamental machine learning algorithms with implementation.
This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.
User Reviews
Be the first to review “A Practical Guide to Machine Learning with Python”
Related Products
Advanced RAG Techniques: Choosing the Right Approach
The course explains advanced RAG concepts, showcasing their implementation through LangChain for enhanced practical learning and application.
Machine Learning with NumPy, pandas, scikit-learn, and More
This course equips learners with practical data analysis and machine learning with pandas and other frameworks such as NumPy and TensorFlow.
Fundamentals of Retrieval-Augmented Generation with LangChain
This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.
Coderust: Hacking the Coding Interview
Coderust equips you for coding interviews with practical problems and solutions in six programming languages.
Mastering Data Analysis with Python Pandas
Explore advanced data manipulation and analysis techniques using Pandas in Python.
Clean Code in Python
Learn the principles of writing clean, maintainable, and efficient Python code for interviews and beyond.
There are no reviews yet.