Join our Social media channels to get the latest discounts
Complete Machine learning course for anyone who wants to make a career in data science.
Here is why you should take this course:
Unlock the secrets of understanding Machine Learning for Data Science!
In this introductory course, we will guide you through the wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.
This course is your complete guide to both supervised & unsupervised learning. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.
In this age of big data, companies across the globe use Machine Learning to sift through the avalanche of information at their disposal.
Cluster analysis is a staple of unsupervised machine learning and data science.
It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning.
In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns.
In this course we will talk about clustering. This is where instead of training on labels, we try to create our own labels! We’ll do this by grouping together data that looks alike.
There are 2 methods of clustering we’ll talk about: k-means clustering and hierarchical clustering.
Next, because in machine learning we like to talk about probability distributions, we’ll go into Gaussian mixture models and kernel density estimation, where we talk about how to "learn" the probability distribution of a set of data.
By becoming proficient in unsupervised & supervised learning, you can give your company a competitive edge and boost your career to the next level.
Regression analysis is one of the central aspects of both statistical and machine learning based analysis.
This course will teach you regression analysis for both statistical data analysis and machine learning.
It explores the relevant concepts from basic to expert level.
This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions.