Machine Learning

6 courses

Implementing AI Algorithms from Scratch

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.

See courses

This learning path includes:

6 courses with bite-sized lessons and practices

26 engaging lessons in text and video formats

106 hands-on practices

One-on-one guidance from Cosmo, our AI tutor

Verified skills you'll gain

ADVANCED

Coding and Data Algorithms

ADVANCED

Machine Learning Model Development

INTERMEDIATE

Deep Learning and Neural Networks

Tools you'll use

Numpy

Python

Scikit-learn

Trusted by learners working at top companies

4 lessons

17 practices

Regression and Gradient Descent

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.

See details

6 lessons

27 practices

Classification Algorithms and Metrics

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.

See details

5 lessons

21 practices

Gradient Descent: Building Optimization Algorithms from Scratch

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.

See details

4 lessons

16 practices

Ensemble Methods from Scratch

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.

See details

4 lessons

15 practices

Unsupervised Learning and Clustering

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.

See details

3 lessons

10 practices

Neural Networks Basics from Scratch

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.

See details

Meet Cosmo: The smartest AI guide in the universe

Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.

Whether you’re leveling up or just starting out, find your next step here.