This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.
A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!
Gain a thorough grasp of time series analysis and its effects, as well as practical tips on how to apply machine learning methods and build RNNs. Learn to train RNNs efficiently while taking crucial concepts such as overfitting and underfitting into account. The course offers a useful, hands-on manner for learning Python methods and principles.
This course takes you through the concepts of Alteryx from scratch. With the help of interesting case studies, you will learn how to use Alteryx for joining datasets, performing union operations, finding and replacing text, sorting and filtering data, and a lot more.
This course will show you why Hadoop is one of the best tools to work with big data. With the help of some real-world data sets, you will learn how to use Hadoop and its distributed technologies, such as Spark, Flink, Pig, and Flume, to store, analyze, and scale big data.
Scala is doubtless one of the most in-demand skills for data scientists and data engineers. This competitive course will teach you the essential concepts and methodologies of Scala with a lot of practical implementations.
The course is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the key concepts and methodologies of RL and deep RL, along with several practical implementations. This course will help you know the theory and practical aspects of reinforcement and deep reinforcement learning.
This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.
This course first equips you with the fundamentals of Python and then progresses to teach you how to use various libraries such as NumPy, Pandas, Seaborn, Bokeh, and so on. This course contains several mini projects so that, by the end of this course, you will be equipped with the essential tools you need to become a visualization expert.
Take your first step toward Natural Language Processing with this beginner-to-pro course. Gain an in-depth understanding of deep learning models for NLP with the help of examples. Learn the essential concepts from the absolute beginning with complete unraveling along with examples in Python.