Step by Step Guide to Machine Learning

Step by Step Guide to Machine Learning

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
ENROLL NOW

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