Hands-on Supervised Machine Learning with Python

Hands-on Supervised Machine Learning with Python

Teach your machine to think for itself!

Bestseller
Created By: Taylor Smith
16.05 9.62

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
    00:02:34
  • Getting Our Machine Learning Environment Setup
    00:13:15
  • Supervised Learning
    00:04:33
  • Hill Climbing and Loss Functions
    00:11:23
  • Model Evaluation and Data Splitting
    00:04:24
  • Introduction to Parametric Models and Linear Regression
    00:06:36
  • Implementing Linear Regression from Scratch
    00:11:15
  • Introduction to Logistic Regression Models
    00:03:07
  • Implementing Logistic Regression from Scratch
    00:10:06
  • Parametric Models AIPros/Cons
    00:02:41
  • 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
    00:19:41
  • Various Clustering Methods
    00:03:44
  • Implementing K-Nearest Neighbors from Scratch
    00:05:37
  • Non-Parametric Models AIPros/Cons
    00:02:46
  • Recommender Systems & an Introduction to Collaborative Filtering
    00:14:06
  • Matrix Factorization
    00:07:00
  • Matrix Factorization in Python
    00:10:22
  • Content-Based Filtering
    00:05:14
  • Neural Networks and Deep Learning
    00:08:54
  • Neural Networks
    00:11:02
  • Use Transfer Learning
    00:08:30
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Packt Publication

  • 4.4 (15)
  • 7 Reviews
  • 15 Students
  • 935 Courses