Dive deep into the intricate universe of Artificial Intelligence. This path is designed to give you the perfect understanding of classic ML algorithms by implementing them from scratch without using any libraries like SK-learn.
Dig deep into regression and learn about the gradient descent algorithm. This course does not rely on high-level libraries like scikit-learn, but focuses on building these algorithms from scratch for a thorough understanding. Master the implementation of simple linear regression, multiple linear regression, and logistic regression powered by gradient descent.
Gradient Descent: Building Optimization Algorithms from Scratch
5 lessons
Course 4
Ensemble Methods from Scratch
4 lessons
Course 5
Unsupervised Learning and Clustering
4 lessons
Course 6
Neural Networks Basics from Scratch
3 lessons
Turn screen time into skills time
Practice anytime, anywhere with our mobile app.
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal
From our community
Hear what our customers have to say about CodeSignal Learn
I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!
Francisco Aguilar Meléndez
Data Scientist
+11
I love that it's personalized. When I'm stuck, I don't have to hope my Google searches come out successful. The AI mentor Cosmo knows exactly what I need.
Faith Yim
Software Engineer
+14
It's an amazing product and exceeded my expectations, helping me prepare for my job interviews. Hands-on learning requires you to actually know what you are doing.
Alex Bush
Full Stack Engineer
+9
I'm really impressed by the AI tutor Cosmo's feedback about my code. It's honestly kind of insane to me that it's so targeted and specific.
Abbey Helterbran
Tech consultant
+8
I tried Leetcode but it was too disorganized. CodeSignal covers all the topics I'm interested in and is way more structured.
Jonathan Miller
Senior Machine Learning Engineer
+12
I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!
Francisco Aguilar Meléndez
Data Scientist
+11
27 practices
Go beneath the surface of classification algorithms and metrics, implementing them from scratch for deeper understanding. Bypass commonly-used libraries such as scikit-learn to construct Logistic Regression, k-Nearest Neighbors, Naive Bayes Classifier, and Decision Trees from ground up. This course includes creating the AUCROC metric for Logistic Regression, among others.
Delve into the intricacies of optimization techniques with this immersive course that focuses on the implementation of various algorithms from scratch. Bypass high-level libraries to explore Stochastic Gradient Descent, Mini-Batch Gradient Descent, and advanced optimization methods such as Momentum, RMSProp, and Adam.
Learn about Ensemble Methods and their implementation from scratch. This course covers the understanding and implementation of multiple ensemble methods such as Bagging, Random Forest, AdaBoost, and Gradient Boosting Machines like XGBoost without relying on high-level libraries.
Navigate through the intricacies of Unsupervised Learning and Clustering in this hands-on course. Skip the high-level libraries and build core aspects of unsupervised learning methods from scratch, including k-Means, mini-batch k-Means, Principal Component Analysis, and DBSCAN. Learn to assess cluster quality with crucial clustering metrics like homogeneity, completeness, and v-metric.
Dive deep into the theory and implementation of Neural Networks. This course will have you implementing tools at the heart of modern AI such as Perceptrons, activation functions, and the crucial components of multi-layer Neural Networks. All of this without the help of high-level libraries leaves you with a profound understanding of the underpinning mechanisms.
