Learn TensorFlow and practice your ML skills with our comprehensive learning path. Ideal for beginner and intermediate ML Engineers, it covers the essentials and advanced techniques for building and optimizing Deep Learning models.
This learning path includes:
4 courses with bite-sized lessons and practices
19 engaging lessons in text and video formats
95 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Dive into TensorFlow, one of the top libraries for numerical computation and AI. This beginner-friendly course focuses on TensorFlow's core: tensors and their use in neural networks. Learn about tensors, tensor operations, and basic TensorFlow components to begin creating simple neural network models.
Dive into TensorFlow to learn and build basic neural networks. This course covers key elements like layers, neurons, activation functions, and model training. Progress through hands-on code examples, ending with building and assessing a neural network for binary classification.
Explore the famous Iris dataset in our advanced TensorFlow course. Learn to preprocess data, build, and train a multi-class classifier. Evaluate performance with metrics and visualizations. Conclude with model optimization techniques to boost efficiency and accuracy, and cover saving/loading for deployment.
This course delves into advanced TensorFlow techniques to boost model performance and reliability. Learn about using regularization and dropout to prevent overfitting, and explore real-time training improvements with callbacks. Each module is concise and impactful, equipping you with practical skills to enhance your machine learning models.
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.
Embark on a comprehensive journey into the world of machine learning with this carefully curated learning path. Designed with python programmers and data scientists in mind who don't know much Machine Learning yet, this path starts with the fundamentals of Machine Learning using Sklearn and then progresses to advanced concepts in deep learning through Tensorflow. By the end of this course, you'll have a solid foundation in machine learning, along with the skills needed to build and optimize neural networks using Tensorflow.
Dive deep into the intricate universe of Artificial Intelligence with this in-depth learning path. This path is perfectly suited for those who wish to not only understand the theoretical aspects of Machine Learning algorithms but also wish to learn how to code these algorithms from scratch, without relying on common libraries such as Sklearn. You'll start with grasping the essence of Machine Learning, dissect the underlying principles, and then move on to implementing some of the most fundamental and crucial algorithms in ML all by yourself.
Dive into the world of unsupervised learning with this specialized path focusing on Clustering, an essential Machine Learning technique. Understand everything about Clustering from scratch, starting with data preprocessing, moving on to different clustering algorithms like K-means, DBSCAN, Agglomerative Hierarchical Clustering, and finally, mastering validation techniques to evaluate the performance of your models.