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A Practical Guide to Machine Learning with Python - AI-Powered Course
4.4
Beginner
72h 30min
A Practical Guide to Machine Learning with Python
Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.
Join 2.8M developers at
Overview
Content
Reviews
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.
This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge ...Show More
WHAT YOU'LL LEARN
Learn fundamental principles and techniques of machine learning.
Understand the benefits and drawbacks of a variety of common machine learning methods.
The key premise of the course is to teach you how to code basic machine learning models.
Develop skills with using machine learning tools to solve real-world issues.
Learn the fundamentals of different learning paradigms (supervised, unsupervised, etc.).
Learn fundamental principles and techniques of machine learning.
Show more
Content
57 Lessons12 Quizzes
1.
Introduction to Course
2 Lessons
Get familiar with coding basic machine learning models using Python and its historical importance.
2.
Introduction to Machine Learning
4 Lessons
Look at the essentials of machine learning types, key datasets, and core libraries.
3.
Exploratory Data Analysis
3 Lessons
Break apart Exploratory Data Analysis techniques for importing datasets, using data frame functions, and practical quizzes.
4.
Data Scrubbing
6 Lessons
Break down complex ideas in data scrubbing, variable removal, one-hot encoding, and dimension reduction.
5.
Pre-Model Algorithms
5 Lessons
Solve problems in PCA and K-means clustering for dimensionality reduction and data simplification.
6.
Split Validation
2 Lessons
Investigate how split validation partitions data, optimizes models, and ensures unbiased assessments.
7.
Model Design
4 Lessons
Master the steps to design, implement, evaluate, and optimize machine learning models effectively.
8.
Linear Regression
5 Lessons
Get familiar with implementing linear regression, handling data, and evaluating prediction accuracy.
9.
Logistic Regression
5 Lessons
Get started with logistic regression for classification, handling data, and evaluating predictions.
10.
Support Vector Machines
4 Lessons
Go hands-on with implementing and optimizing Support Vector Machines for robust classification.
11.
K-Nearest Neighbors
4 Lessons
Apply your skills to implement and optimize k-NN models using Python for classification tasks.
12.
Tree-Based Methods
10 Lessons
Dig into core tree-based methods, including decision trees, random forests, and gradient boosting.
13.
Conclusion
1 Lessons
Investigate future growth opportunities in machine learning and stay motivated for continuous learning.
14.
Appendix
2 Lessons
Master Python basics and set up Jupyter Notebook for effective machine learning practice.
Certificate of Completion
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Course Author:
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