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 PyTorch. 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 PyTorch.
This learning path includes:
5 courses with bite-sized lessons and practices
32 engaging lessons in text and video formats
119 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Explore essential machine learning preparation using the Titanic Dataset. Gain skills in cleaning and preprocessing historical data with Python and Pandas, readying it for ML models and accurate analytics.
Learn fundamental machine learning models with Sklearn, centered on the Iris Dataset. This course covers key algorithms like linear and logistic regression, and decision trees. Master implementation, evaluation, and optimization to pave the way for advanced machine learning concepts.
Explore feature engineering using UCI's Abalone Dataset in this course. Enhance your skills in feature extraction, selection, and transformation to boost machine learning model performance. Learn to craft valuable features, apply different selection strategies, and use feature combinations to uncover data patterns.
Delve into machine learning fundamentals using the Wisconsin Breast Cancer Dataset. This course focuses on key ML techniques like data exploration, model tuning, and evaluation. Master hyperparameter tuning, regularization, and ensemble methods through practical exercises to boost your predictive models' accuracy and reliability.
Start your exploration of neural networks with a beginner's course on TensorFlow, using the scikit-learn Digits Dataset. Learn neural network basics and deep learning by developing, training, and evaluating models with TensorFlow. Understand different neural network architectures and improve them, emphasizing the importance of data preparation in deep learning.
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.
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.
Welcome to this extensive learning path designed to transition you from a curious enthusiast to a proficient data science professional. This pathway encompasses a collection of courses tailored to equip learners with the foundational knowledge, tools, and techniques required to unearth actionable insights from raw data. By utilizing Python—one of the most versatile and powerful languages in the data science community—you will be positioned at the forefront of the ever-evolving landscape of data-driven decision-making.