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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 Content :

Course Curriculum

Course Introduction

Start
Course Introduction (2:02)

Machine Learning Concepts

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

First ML Application

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

Data Analysis

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

Linear Algebra

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

Natural Language Processing

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

Clustering

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

Purchase Machine Learning with Python – Stone River eLearning courses at here with PRICE ~~$99~~ ~~$37~~

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