Machine Learning with Python
2.7 (3 ratings)
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Machine Learning with Python

All about Machine learning
2.7 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
69 students enrolled
Last updated 5/2017
English
Current price: $10 Original price: $150 Discount: 93% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 5.5 hours on-demand video
  • 2 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • Experience programming.
  • Experience with Python will be helpful.
Description

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.  

Who is the target audience?
  • Desire to learn machine learning concepts
  • Wants to build machine learning processing from the ground up
  • Ok with complex logic and data structures
Compare to Other Machine Learning Courses
Curriculum For This Course
50 Lectures
05:18:45
+
Course Introduction
1 Lecture 02:02

Download code here

Preview 02:02
+
Machine Learning Concepts
4 Lectures 14:00
Introduction
00:38

Supervised and Unsupervised Learning
08:34

Semi-Supervised Learning
04:25

Summary
00:23
+
First ML Application
10 Lectures 01:10:56
Introduction
01:49

Installing the Environment
02:54

Hello World
07:34


Email Spam Checker - Part 1
07:09

Email Spam Checker - Part 2
13:39

Email Spam Checker Results
08:35

Iris 70/30 - Part 1
08:56

Iris 70/30 - Part 2
09:18

Summary
00:44
+
Data Analysis
5 Lectures 33:44
Introduction
00:32

Data Analysis - Example 1
12:57

Data Analysis - Example 2
10:47

Data Visualization
08:44

Summary
00:44
+
Linear Algebra
9 Lectures 01:03:54
Introduction
00:59


Linear Algebra
09:35

Linear Regression Calculation - Part 1
12:42

Linear Regression Calculation - Part 2
05:03

Regression on Larger Dataset - Part 1
10:18

Regression on Larger Dataset - Part 2
07:38

Regression on Larger Dataset - Part 3
10:09

Summary
00:38
+
Natural Language Processing
9 Lectures 01:09:02
Introduction
01:03

Natural Language Processing - Part 1
08:57

Natural Language Processing - Part 2
03:57


Processing Unique Words
13:11

Summarizing Headlines - Part 1
09:18

Summarizing Headlines - Part 2
11:54

Summarizing Headlines - Part 3
08:31

Summary
00:50
+
Clustering
10 Lectures 01:03:54
Introduction
01:03


EM and M Clustering
06:18

Clustering Code Walkthrough
09:07

Clustering Iris Data - Part 1
08:57

Clustering Iris Data - Part 2
08:43

Clustering Iris Data - Part 3
08:15

Dendrogram Graphs
10:01


Course Summary
02:00
+
Bonus Material
2 Lectures 01:15
Bonus Lecture: Certificate of Completion
01:01

Bonus Lecture: Course Discounts
00:14
About the Instructor
Stone River eLearning
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