Beginner
14h
Updated 3 weeks ago
Fundamentals of Machine Learning: A Pythonic Introduction
Explore machine learning fundamentals by building algorithms from scratch and using scikit-learn, while mastering classic models and modern techniques through hands-on projects.
Machine learning is a core skill for modern developers, powering applications such as data analysis, computer vision, recommendation systems, and automation. In this course, you’ll learn essential machine learning concepts, key algorithms, and practical techniques using Python, combining theory with hands-on implementation and comparison to scikit-learn models.
You’ll begin with machine learning fundamentals and real-world use cases, then explore supervised learning and clustering. The course includes a practical bag-of-visual-words project and covers topics such as linear and logistic regression, support vector machines, ensemble methods, and principal component analysis. It concludes with modern representation learning techniques, including autoencoders and variational autoencoders.
By the end, you will be able to apply core machine learning algorithms to real datasets, evaluate model performance, and confidently use machine learning in real-world projects.
Machine learning is a core skill for modern developers, powering applications such as data analysis, computer vision, recommenda...Show More
WHAT YOU'LL LEARN
An understanding of fundamental machine learning algorithms and their use cases
Strong problem-solving skills developed through hands-on machine learning projects
A working knowledge of applying machine learning algorithms to real-world datasets, including classification, regression, clustering, and dimensionality reduction
Hands-on experience implementing machine learning algorithms from scratch and with scikit-learn
The ability to assess, compare, and interpret the performance of machine learning models
An understanding of fundamental machine learning algorithms and their use cases
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Content
1.
Course Overview
3 Lessons
Get familiar with foundational machine learning concepts, hands-on projects, and algorithm implementation.
2.
Supervised Learning
11 Lessons
Get started with supervised learning, focusing on regression, classifiers, validation, and sklearn.
3.
Clustering
10 Lessons
Examine clustering techniques including k-means, DBSCAN, agglomerative clustering, and their practical applications.
4.
Generalized Linear Regression
9 Lessons
Grasp the fundamentals of generalized linear regression, kernel methods, and feature transformations.
5.
Support Vector Machine
9 Lessons
Explore support vector machines for classification, utilizing hyperplanes, kernels, and optimization techniques.
6.
Logistic Regression
8 Lessons
Investigate logistic regression, BCE optimization, kernel methods, multiclass extension, and neural network transition.
7.
Ensemble Learning
9 Lessons
Master the fundamentals of ensemble learning and explore techniques to enhance predictive accuracy.
8.
Decoding Dimensions: PCA and Autoencoders
6 Lessons
Solve problems in dimensionality reduction using PCA, autoencoders, and VAEs.
9.
Appendix
6 Lessons
Get started with CVXPY, mathematical and convex optimization, gradient descent, and Lagrangian duality.
Certificate of Completion
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Complete more lessons to unlock your certificate
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