MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch

Genre: eLearning | Language: English | Duration: 16 lectures (6 hour, 49 mins) | Size: 4.36 GB

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