by Taylor Smith | Released August 2018 | ISBN: 9781789347654

Teach your machine to think for itself!About This VideoTake a deep dive into supervised learning and grasp how a machine “learns” from dataFollow detailed and thorough coding examples to implement popular machine learning algorithms from scratch, developing a deep understanding along the wayWork your Python muscle! This course will help you grow as a developer by heavily relying on some of the most popular scientific and mathematical libraries in the Python language.In DetailSupervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine “learns” under the hood.This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning.Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning.By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.All the codes of the course are uploaded on GitHub: https://bit.ly/2nR4aMU Show and hide more

1. Chapter 1 : First Step Towards Supervised Learning
   • The Course Overview 00:02:34
   • Getting Our Machine Learning Environment Setup 00:13:16
   • Supervised Learning 00:04:34
   • Hill Climbing and Loss Functions 00:11:23
   • Model Evaluation and Data Splitting 00:04:24
2. Chapter 2 : Implementing Parametric Models
   • Introduction to Parametric Models and Linear Regression 00:06:37
   • Implementing Linear Regression from Scratch 00:11:16
   • Introduction to Logistic Regression Models 00:03:08
   • Implementing Logistic Regression from Scratch 00:10:06
   • Parametric Models –Pros/Cons 00:02:42
3. Chapter 3 : Working with Non-Parametric Models
   • The Bias/Variance Trade-off 00:05:12
   • Introduction to Non-Parametric Models and Decision Trees 00:08:27
   • Decision Trees 00:05:23
   • Implementing a Decision Tree from Scratch 00:19:42
   • Various Clustering Methods 00:03:45
   • Implementing K-Nearest Neighbors from Scratch 00:05:38
   • Non-Parametric Models –Pros/Cons 00:02:46
4. Chapter 4 : Advanced Topics in Supervised ML
   • Recommender Systems and an Introduction to Collaborative Filtering 00:14:07
   • Matrix Factorization 00:07:01
   • Matrix Factorization in Python 00:10:23
   • Content-Based Filtering 00:05:14
   • Neural Networks and Deep Learning 00:08:55
   • Neural Networks 00:11:03
   • Use Transfer Learning 00:08:30
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