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.
Dive deep into the world of machine learning without the complexity of neural networks using the Wisconsin Breast Cancer Dataset. This course is designed to enhance your understanding of fundamental ML techniques, including data exploration, model tuning, and evaluation. You'll learn to apply hyperparameter tuning, regularization, and ensemble methods in practical, hands-on exercises, all aimed at improving the accuracy and reliability of your predictive models.
Embark on a journey through the intriguing realm of neural networks with this beginner-oriented course featuring TensorFlow, focusing on the scikit-learn Digits Dataset. Discover the essentials of neural networks and deep learning as you develop, train, and assess fundamental deep learning models using TensorFlow. Delve into diverse neural network structures and excel in refining them, all while understanding the pivotal role 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. This ground-up approach is designed to solidify your understanding of AI, making it an ideal choice for those keen on gaining a holistic understanding of AI.
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.
This course will teach students the skills needed for technical coding interviews at companies like Google. It will focus on understanding how to choose optimal algorithms and data structures for different problems, how to apply them, and how to explain their reasoning. Topics covered will include hash tables, recursion, linked lists, trees, and graphs.