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Machine Learning with Python

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Machine Learning with Python, available at $19.99, has an average rating of 3.7, with 49 lectures, based on 16 reviews, and has 118 subscribers.

You will learn about Speak fluently about machine learning Have an understanding of what is involved with using machine learning in Python Understand what machine learning is and can do This course is ideal for individuals who are Desire to learn machine learning concepts or Wants to build machine learning processing from the ground up or Ok with complex logic and data structures It is particularly useful for Desire to learn machine learning concepts or Wants to build machine learning processing from the ground up or Ok with complex logic and data structures.

Enroll now: Machine Learning with Python

Summary

Title: Machine Learning with Python

Price: $19.99

Average Rating: 3.7

Number of Lectures: 49

Number of Published Lectures: 49

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

  • Speak fluently about machine learning
  • Have an understanding of what is involved with using machine learning in Python
  • Understand what machine learning is and can do

Who Should Attend

  • Desire to learn machine learning concepts
  • Wants to build machine learning processing from the ground up
  • Ok with complex logic and data structures

Target Audiences

  • Desire to learn machine learning concepts
  • Wants to build machine learning processing from the ground up
  • Ok with complex logic and data structures

If you’re plugged into the tech industry, you’ll know that two things have been making consistent waves in many areas over the past few years; machine learning and Python. What happens when you combine the new gold standard programming language with the most significant tech development in areas such as financial trading, online search, digital marketing and even data and personal security (among others)? Great things, that’s what. This course will show you what’s what, and get you started on becoming a machine learning guru.

Learn the New Future of Programming

  • Understand what machine learning is and what it can do
  • Discover how Python utilises machine learning
  • Build machine learning processing from the ground up
  • Delve into complex logic and data structures

Increase Your Python Expertise

If you have a desire to learn machine learning concepts and have some previous programming or Python experience, this course is perfect for you. If you’re more of a beginner than an intermediate, don’t worry; each module starts with theory to explain upcoming concepts. Once you’re comfortable, you’ll put your knowledge into practice with a code walk through.

The goal of this course is to build procedural machine learning from the ground up. Writing processing from scratch allows students to gain a more in-depth insight into data processing, and as each machine learning app is created, explanations and comments are provided to help students understand why things are being done in certain ways. Each code walk through also shows the building process in real time.

The course begins with an introduction to machine learning concepts, after which you’ll build your first machine learning application. Following that, we look at data analysis, linear algebra, natural language processing and clustering, all within the context of Python. At the end of the course, you’ll be issued with a certificate of completion and will have gained a full introduction into the world of machine learning with Python.

What is Machine Learning?

Machine learning is a method of data analysis that essentially allows computers to ‘learn’ on their own without being explicitly programmed. For example, when you stop scrolling through Facebook to read a friend or a page’s post, algorithms automatically work to make sure you’ll see more content from those sources earlier in your news feed in future. 

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Chapter 2: Machine Learning Concepts

Lecture 1: Introduction

Lecture 2: Supervised and Unsupervised Learning

Lecture 3: Semi-Supervised Learning

Lecture 4: Summary

Chapter 3: First ML Application

Lecture 1: Introduction

Lecture 2: Installing the Environment

Lecture 3: Hello World

Lecture 4: Installing Aaconda and Deep Learning Libraries

Lecture 5: Email Spam Checker – Part 1

Lecture 6: Email Spam Checker – Part 2

Lecture 7: Email Spam Checker Results

Lecture 8: Iris 70/30 – Part 1

Lecture 9: Iris 70/30 – Part 2

Lecture 10: Summary

Chapter 4: Data Analysis

Lecture 1: Introduction

Lecture 2: Data Analysis – Example 1

Lecture 3: Data Analysis – Example 2

Lecture 4: Data Visualization

Lecture 5: Summary

Chapter 5: Linear Algebra

Lecture 1: Introduction

Lecture 2: Parametric Algorithms

Lecture 3: Linear Algebra

Lecture 4: Linear Regression Calculation – Part 1

Lecture 5: Linear Regression Calculation – Part 2

Lecture 6: Regression on Larger Dataset – Part 1

Lecture 7: Regression on Larger Dataset – Part 2

Lecture 8: Regression on Larger Dataset – Part 3

Lecture 9: Summary

Chapter 6: Natural Language Processing

Lecture 1: Introduction

Lecture 2: Natural Language Processing – Part 1

Lecture 3: Natural Language Processing – Part 2

Lecture 4: Tokenizing Content

Lecture 5: Processing Unique Words

Lecture 6: Summarizing Headlines – Part 1

Lecture 7: Summarizing Headlines – Part 2

Lecture 8: Summarizing Headlines – Part 3

Lecture 9: Summary

Chapter 7: Clustering

Lecture 1: Introduction

Lecture 2: Cluster Introduction

Lecture 3: EM and M Clustering

Lecture 4: Clustering Code Walkthrough

Lecture 5: Clustering Iris Data – Part 1

Lecture 6: Clustering Iris Data – Part 2

Lecture 7: Clustering Iris Data – Part 3

Lecture 8: Dendrogram Graphs

Lecture 9: Summary

Lecture 10: Course Summary

Chapter 8: Bonus Material

Lecture 1: Bonus Lecture

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 2 votes
  • 3 stars: 6 votes
  • 4 stars: 3 votes
  • 5 stars: 3 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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