A beginners guide to learn Machine Learning from scratch. Learn various algorithms and techniques using ML libraries.
What you'll learn Learn how to use NumPy to do fast mathematical calculations Learn what is Machine Learning and Data Wrangling Learn how to use scikit-learn for data-preprocessing Learn different model selection and feature selections techniques Learn about cluster analysis and anomaly detection Learn about SVMs for classification, regression and outliers detection. Requirements Basic knowledge of scripting and programming Basic knowledge of python programming Description If you are looking to start your career in machine learning then this is the course for you. This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels. This course has 5 parts as given below: Introduction to Machine Learning & Data Wrangling Linear Models, Trees & Preprocessing Model Evaluation, Feature Selection & Pipelining Bayes, Nearest Neighbours & Clustering SVM, Anomalies, Imbalanced Classes, Ensemble Methods For the code explained in each lecture, you can find a GitHub link in the resources section. Who this course is for: Beginners who want to become a data scientist Software developers who want to learn machine learning from scratch Python developers who are interested to learn machine learning Professionals who want to start their career in Machine Leaning
Download link : (If you need these, buy and download immediately before they are delete)