Machine Learning with Python

Machine Learning with Python
Machine Learning with Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5.5 Hours | 1.48 GB
eLearning | Skill level: All Levels


All about Machine learning

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.

+ Table of Contents

01 – Course Introduction
02 – Section 1 Introduction
03 – Supervised and Unsupervised Learning
04 – Semi-Supervised Learning
05 – Section 1 Summary
06 – Section 2 Introduction
07 – Installing the Environment
08 – Hello World
09 – Installing Aaconda and Deep Learning Libraries
10 – Email Spam Checker – Part 1
11 – Email Spam Checker – Part 2
12 – Email Spam Checker Results
13 – Iris 70_30 – Part 1
14 – Iris 70_30 – Part 2
15 – Section 2 Summary
16 – Section 3 Introduction
17 – Data Analysis – Example 1
18 – Data Analysis – Example 2
19 – Data Visualization
20 – Section 3 Summary
21 – Section 4 Introduction
22 – Parametric Algorithms
23 – Linear Algebra
24 – Linear Regression Calculation – Part 1
25 – Linear Regression Calculation – Part 2
26 – Regression on Larger Dataset – Part 1
27 – Regression on Larger Dataset – Part 2
28 – Regression on Larger Dataset – Part 3
29 – Section 4 Summary
30 – Section 5 Introduction
31 – Natural Language Processing – Part 1
32 – Natural Language Processing – Part 2
33 – Tokenizing Content
34 – Processing Unique Words
35 – Summarizing Headlines – Part 1
36 – Summarizing Headlines – Part 2
37 – Summarizing Headlines – Part 3
38 – Section 5 Summary
39 – Section 6 Introduction
40 – Cluster Introduction
41 – EM and M Clustering
42 – Clustering Code Walkthrough
43 – Clustering Iris Data – Part 1
44 – Clustering Iris Data – Part 2
45 – Clustering Iris Data – Part 3
46 – Dendrogram Graphs
47 – Section 6 Summary
48 – Course Summary

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