Machine Learning with Python
Self-paced course
Certification program
Price
Rating
Overview
Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.
This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.
You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.
With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.
By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
Very informative course, showing mostly how to use many different Machine Learning techniques. Although mathematical details are not discussed much, the intuition of the methods are discussed.
This course walks us through the fundamentals of machine learning methods. The capstone project is very useful for those who have previous knowledge of machine learning and Python programming.
I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
You will learn
1
Describe the various types of Machine Learning algorithms and when to use them
2
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
3
Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
4
Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics
Learning outcomes
Post this credential on your LinkedIn profile, resume, or CV, and don’t forget to celebrate your achievement by sharing it across your social networks or mentioning it during your performance review
