What you’ll learn
What is Machine Learning? And if a computer can think – can it learn?
The impacts Machine Learning and Data Science is having on society.
To really understand computer technology has changed the world, with an appreciation of scale.
To know what problems Machine Learning can solve, and how the Machine Learning Process works.
How to avoid problems with Machine Learning, to successfully implement it without losing your mind!
To understand how these different domains fit together, how they are different, and how to avoid the marketing fluff.
To understand what id reinforcement learning and how it is associated with machine learning and artificial intelligence
Classical Statistical Regression Modelling Techniques Such As Linear Regression, Polynomial regression and Lasso regression
Clustering models of Machine Learning like the k-means and hierarchical clustering
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.
Who this course is for:
- Before you load Python, Before you start R – you need this course. This introductory course will introduce you to the Fundamentals, that you need before you start getting “Hands-on”.
- Anyone interested in understanding how Machine Learning is used for Data Science.
- Including business leaders, managers, app developers, consumers – you!
- Adventurous folks, who are ready to strap themselves into the exotic world of Data Science and Machine Learning.
- Students Interested In Getting Started With Data Science Applications.
- Introduction to Machine Learning
- Getting started with ML
- Groundwork for ML
- Reinforcement Learning
- Statistics as the base
- Regression Models of Machine Learning
- Clustering Models of Machine Learning