In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Science and Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K–Nearest Neighbor, Decision Trees, and Neural Networks. You’ll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data. By the end of this course, you will understand the ABCs of Machine Learning and be able to implement what you’ve learnt on your own, more specifically, be able to implement what you’ve learnt on Python. There is no ideal student as there are no prior requirements needed – everybody is welcome!!
Please feel free to ask me any question! Don’t like the course? Ask for a 30–day refund!!
Real Testaments >
1) Excellent course!! Dana is very knowledgeable about Machine Learning, and is able to present the concepts and practices in a way that is easy to understand, along with actionable exercises to implement and practice. The presentation is very detailed and direct. A topic is introduced, explained, displayed with example and then we began implementing it. Joseph, 5 star rating
Courses : 2
Specification: A Beginner’s Guide to Machine Learning (in Python)
17 reviews for A Beginner’s Guide to Machine Learning (in Python)