Step by Step Guide to Machine Learning

Step by Step Guide to Machine Learning

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:

1. Introduction to Machine Learning & Data Wrangling
2. Linear Models, Trees & Preprocessing
3. Model Evaluation, Feature Selection & Pipelining
4. Bayes, Nearest Neighbours & Clustering
5. 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

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