Stone River eLearning – Machine Learning with Python

Description

Stone River eLearning – Machine Learning with Python download, Stone River eLearning – Machine Learning with Python review, Stone River eLearning – Machine Learning with Python free

Stone River eLearning – Machine Learning with Python

Machine Learning with Python
Linear Algebra, Natural Language and more

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.

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)

Frequently Asked Questions:

1. Innovative Business Model:
   • Embrace the reality of a genuine business! Our approach involves forming a group buy, where we collectively share the costs among members. Using these funds, we purchase sought-after courses from sale pages and make them accessible to individuals facing financial constraints. Despite potential reservations from the authors, our customers appreciate the affordability and accessibility we provide.
2. The Legal Landscape: Yes and No:
   • The legality of our operations falls into a gray area. While we lack explicit approval from the course authors for resale, there’s a technicality at play. When procuring the course, the author didn’t specify any restrictions on resale. This legal nuance presents both an opportunity for us and a boon for those seeking budget-friendly access.
3. Quality Assurance: Unveiling the Real Deal:
   • Delving into the heart of the matter – quality. Acquiring the course directly from the sale page ensures that all documents and materials are identical to those obtained through conventional means. However, our differentiator lies in going beyond personal study; we take an extra step by reselling. It’s important to note that we are not the official course providers, meaning certain premium services aren’t included in our package:
     • No coaching calls or scheduled sessions with the author.
     • No access to the author’s private Facebook group or web portal.
     • No entry to the author’s exclusive membership forum.
     • No direct email support from the author or their team.

   We operate independently, aiming to bridge the affordability gap without the additional services offered by official course channels. Your understanding of our unique approach is greatly appreciated.

Refund is acceptable:

• Firstly, item is not as explained
• Secondly, Item do not work the way it should.
• Thirdly, and most importantly, support extension can not be used.

Thank you for choosing us! We’re so happy that you feel comfortable enough with us to forward your business here.