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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.
4.5
57 Lessons
2 Projects
72h 30min
Updated 4 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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.).
Learning Roadmap
1.
Introduction to Course
Introduction to Course
Get familiar with coding basic machine learning models using Python and its historical importance.
2.
Introduction to Machine Learning
Introduction to Machine Learning
Look at the essentials of machine learning types, key datasets, and core libraries.
3.
Exploratory Data Analysis
Exploratory Data Analysis
3 Lessons
3 Lessons
Break apart Exploratory Data Analysis techniques for importing datasets, using data frame functions, and practical quizzes.
4.
Data Scrubbing
Data Scrubbing
6 Lessons
6 Lessons
Break down complex ideas in data scrubbing, variable removal, one-hot encoding, and dimension reduction.
5.
Pre-Model Algorithms
Pre-Model Algorithms
5 Lessons
5 Lessons
Solve problems in PCA and K-means clustering for dimensionality reduction and data simplification.
6.
Split Validation
Split Validation
2 Lessons
2 Lessons
Investigate how split validation partitions data, optimizes models, and ensures unbiased assessments.
7.
Model Design
Model Design
4 Lessons
4 Lessons
Master the steps to design, implement, evaluate, and optimize machine learning models effectively.
8.
Linear Regression
Linear Regression
5 Lessons
5 Lessons
Get familiar with implementing linear regression, handling data, and evaluating prediction accuracy.
9.
Logistic Regression
Logistic Regression
5 Lessons
5 Lessons
Get started with logistic regression for classification, handling data, and evaluating predictions.
10.
Support Vector Machines
Support Vector Machines
4 Lessons
4 Lessons
Go hands-on with implementing and optimizing Support Vector Machines for robust classification.
11.
K-Nearest Neighbors
K-Nearest Neighbors
4 Lessons
4 Lessons
Apply your skills to implement and optimize k-NN models using Python for classification tasks.
12.
Tree-Based Methods
Tree-Based Methods
10 Lessons
10 Lessons
Dig into core tree-based methods, including decision trees, random forests, and gradient boosting.
14.
Appendix
Appendix
2 Lessons
2 Lessons
Master Python basics and set up Jupyter Notebook for effective machine learning practice.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Oliver Theobald
Oliver is the author of Machine Learning for Absolute Beginners and a series of other successful titles in the data science field.
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