The Complete Machine Learning Course with Python from Udemy

Overview

The Complete Machine Learning Course in Python has been fully updated for November 2019, offering fresh and improved content to help you master Machine Learning. With brand new sections on Foundations of Deep Learning and Computer Vision, the course also includes updates on Google Colab, Deep Learning, NLP, binary and multi-class classifications, and much more. Prepare to be equipped with the most up-to-date information and practices in the field.

On top of gaining a comprehensive understanding of machine learning, you’ll also build a portfolio of 12 Machine Learning projects that can help you land a high-paying job or solve real-world problems in various aspects of your life. Taught by Anthony NG, a Senior Lecturer from Singapore, this course features over 18 hours of content and boasts a strong rating. Through the hands-on learning approach, you’ll train machine learning algorithms to tackle tasks like classifying flowers, predicting house prices, identifying handwritings or digits, and even detecting cancer cells. With no prior Machine Learning knowledge required, you’ll develop everything from complete machine learning toolsets to the effective use of Matplotlib and Seaborn for visual communication.

Current Coupon

Udemy usually has a very limited availability of discount codes, by clicking the check and activate coupon button below we’ll try to automatically find and apply a coupon for you (if any are available).

Check & Activate Coupon

Skills you’ll learn

1. Master the foundations of deep learning and understand the differences between classical programming and machine learning.
2. Acquire skills in computer vision using convolutional neural networks.
3. Gain a complete machine learning toolset to tackle real-world problems.
4. Understand and apply various regression, classification, and other machine learning algorithms.
5. Develop skills in using Jupyter (IPython) notebook, Spyder, and other IDEs.
6. Effectively communicate and visualize data using Matplotlib and Seaborn.
7. Engineer new features to improve algorithm predictions.
8. Utilize train/test, K-fold, and Stratified K-fold cross-validation techniques to select the correct model and predict model performance with unseen data.

Summary

Discover the world of machine learning through an incredible course designed for aspiring Python enthusiasts! Guided by renowned instructors from Codestars, with a community of over 2 million students globally, and industry experts like Anthony NG and Rob Percival, this comprehensive course offers 203 insightful lessons. With more than 35,000 delighted students and an outstanding rating of 4.5 out of 5, this machine learning journey promises to leave you inspired and enriched with invaluable knowledge. Don’t miss your chance to dive into this fascinating and rewarding field!