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.
This learning path includes:
4 courses with bite-sized lessons and practices
17 engaging lessons in text and video formats
65 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.
Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.
Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.
Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.
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.
This comprehensive learning path will guide the learner in employing Python to perform efficient dimensionality reduction—a crucial skill in the data science and machine learning realm. By the end of this path, learners will be competent in the essential techniques used to extract essential features from high-dimensional data, thereby improving model computational efficiency.