Python – Machine Learning

Description

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Python – 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.
COURSE CURRICULUM
Course Introduction

Course Introduction (2:02)

Machine Learning Concepts

Section Introduction (0:38)
Supervised and Unsupervised Learning (8:34)
Semi-Supervised Learning (4:25)
Section Summary (0:23)

First ML Application

Section Introduction (1:49)
Installing the Environment (2:54)
Hello World (7:34)
Installing Aaconda and Deep Learning Libraries (10:18)
Email Spam Checker – Part 1 (7:09)
Email Spam Checker – Part 2 (13:39)
Email Spam Checker Results (8:35)
Iris 70:30 – Part 1 (8:56)
Iris 70:30 – Part 2 (9:18)
Section Summary (0:44)

Data Analysis

Section Introduction (0:32)
Data Analysis – Example 1 (12:57)
Data Analysis – Example 2 (10:47)
Data Visualization (8:44)
Section Summary (0:44)

Linear Algebra

Section Introduction (0:59)
Parametric Algorithms (6:52)
Linear Algebra (9:35)
Linear Regression Calculation – Part 1 (12:42)
Linear Regression Calculation – Part 2 (5:03)
Regression on Larger Dataset – Part 1 (10:18)
Regression on Larger Dataset – Part 2 (7:38)
Regression on Larger Dataset – Part 3 (10:09)
Section Summary (0:38)

Natural Language Processing

Section Introduction (1:03)
Natural Language Processing – Part 1 (8:57)
Natural Language Processing – Part 2 (3:57)
Tokenizing Content (11:21)
Processing Unique Words (13:11)
Summarizing Headlines – Part 1 (9:18)
Summarizing Headlines – Part 2 (11:54)
Summarizing Headlines – Part 3 (8:31)
Section Summary (0:50)

Clustering

Section Introduction (1:03)
Cluster Introduction (8:31)
EM and M Clustering (6:18)
Clustering Code Walkthrough (9:07)
Clustering Iris Data – Part 1 (8:57)
Clustering Iris Data – Part 2 (8:43)
Clustering Iris Data – Part 3 (8:15)
Dendrogram Graphs (10:01)
Section Summary (0:59)
Course Summary (2:00)