Hands-on Supervised Machine Learning with Python

Hands-on Supervised Machine Learning with Python

Teach your machine to think for itself!

Created By: Taylor Smith
15.87 9.52

About This Course

Supervised 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

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

1) Crack how a machine learns a concept and generalize its understanding to new data
2) Uncover the fundamental differences between parametric and non-parametric models. Distinguish why you might opt for one over the other.
3) Implement and grok several well-known supervised learning algorithms from scratch; build out your github portfolio and show off what you’re capable of!
4) Work with model families like recommender systems, which are immediately applicable in domains such as ecommerce and marketing
5) Expand your expertise using various algorithms like regression, decision trees, clustering and many to become a much stronger Python developer
6) Build your own models capable of making predictions
7) Delve into some of the most popular approaches in deep learning like transfer learning and neural networks

Are There Any Course Requirements Or Prerequisites?

Intermediate knowledge of Python is required for the course.

Who Are Your Target Students?

This course is suitable for developers/aspiring data scientists who want to enter the field of data science and are new to machine learning.

Course Content

  • 24 lectures
  • 03:05:52
  • The Course Overview
  • Getting Our Machine Learning Environment Setup
  • Supervised Learning
  • Hill Climbing and Loss Functions
  • Model Evaluation and Data Splitting
  • Introduction to Parametric Models and Linear Regression
  • Implementing Linear Regression from Scratch
  • Introduction to Logistic Regression Models
  • Implementing Logistic Regression from Scratch
  • Parametric Models AIPros/Cons
  • The Bias/Variance Trade-off
  • Introduction to Non-Parametric Models and Decision Trees
  • Decision Trees
  • Implementing a Decision Tree from Scratch
  • Various Clustering Methods
  • Implementing K-Nearest Neighbors from Scratch
  • Non-Parametric Models AIPros/Cons
  • Recommender Systems & an Introduction to Collaborative Filtering
  • Matrix Factorization
  • Matrix Factorization in Python
  • Content-Based Filtering
  • Neural Networks and Deep Learning
  • Neural Networks
  • Use Transfer Learning

Packt Publication

  • 4.48 (20)
  • 13 Reviews
  • 20 Students
  • 935 Courses