Train, deploy, and scale models with Machine Learning courses!

Fundamentals of Machine Learning for Software Engineers

Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

Become a Machine Learning Engineer

This Skill Path covers Python fundamentals, machine learning techniques and algorithms through practical exercises, preparing you as a machine learning engineer for real-world applications.

Mastering Machine Learning Theory and Practice

Gain insights into machine learning algorithms like regression, deep learning, and SVMs. Learn about Python, probability theory, and optimization through practical coding exercises. Delve into ML intricacies.

Machine Learning with NumPy, pandas, scikit-learn, and More

Delve into practical machine learning with NumPy, pandas, scikit-learn, and more. Gain insights into data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

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.

Feature Engineering for Machine Learning

Bayesian Machine Learning for Optimization in Python

Gain insights into Bayesian optimization and statistical modeling to efficiently tackle high-dimensional problems. Learn about hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

Mastering Self-Supervised Algorithms for Learning without Labels

Gain insights into self-supervised learning. Delve into pseudo label generation, similarity maximization, redundancy reduction, and masked image modeling to apply and modify these algorithms on unlabelled datasets.

Basic Pytorch Tensor Manipulation for Machine Learning

This course teaches you how to create, manipulate, and understand tensors for machine learning using PyTorch.

Simplifying Machine Learning with PyCaret in Python

Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

Alexa Skills 101: Building voice apps for Alexa

Gain insights into developing voice apps with Alexa Skills Kit. Delve into real projects, learn fundamentals, and discover how to launch apps on the Alexa skill store.

Hands-on Machine Learning with Scikit-Learn

Gain insights into Scikit-Learn's datasets, feature engineering, linear/logistic regression, and unsupervised learning. Delve into k-means clustering and neural networks to enhance your machine learning expertise.

Reliable Machine Learning

Explore how to ensure reliability in ML models. Gain insights into software testing, ML-specific techniques, runtime checks, and monitoring tools to build robust ML systems effectively.

Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

Make Your First GAN Using PyTorch

Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

Introduction to JAX and Deep Learning

Discover the power of JAX in deep learning. Gain insights into its ecosystem and learn about linear algebra, pseudo-random number generation, and optimization algorithms for cleaner, structured coding.