Dive into Predictive Modeling with Python, focusing on regression using the California Housing Dataset. Through hands-on coding, this path teaches you how to build and refine models. Master regression techniques and predictive modeling to make informed predictions.
This learning path includes:
5 courses with bite-sized lessons and practices
22 engaging lessons in text and video formats
104 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Initiate your understanding of predictive modeling by exploring the fundamental workings and purposes of these models. Gain insights into how predictive models can guide decision-making across industries and sectors.
Unveil how preprocessing refines data to make predictive models more effective. Learn to handle missing values, outliers and categorical variables, ensuring data consistency and integrity.
Grasp the basics of using different regression models for predictive modeling. Learn how to establish polynomial, lasso and ridge regression models within Python.
As you become more proficient with regression models, this course will introduce you to more advanced models available in the Scikit-Learn library. Explore popular machine learning algorithms, including Support Vector Machines, decision trees, random forest and neural networks.
Any predictive regression model is only as good as its performance, this course delves into advanced techniques for evaluating and optimizing regression models. Explore sophisticated strategies to enhance predictive accuracy and model robustness.
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.