Machine Learning with Python
Machine Learning with Python, available at $19.99, has an average rating of 3.7, with 49 lectures, based on 16 reviews, and has 118 subscribers.
You will learn about 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 This course is ideal for individuals who are Desire to learn machine learning concepts or Wants to build machine learning processing from the ground up or Ok with complex logic and data structures It is particularly useful for Desire to learn machine learning concepts or Wants to build machine learning processing from the ground up or Ok with complex logic and data structures.
Enroll now: Machine Learning with Python
Summary
Title: Machine Learning with Python
Price: $19.99
Average Rating: 3.7
Number of Lectures: 49
Number of Published Lectures: 49
Number of Curriculum Items: 49
Number of Published Curriculum Objects: 49
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Desire to learn machine learning concepts
- Wants to build machine learning processing from the ground up
- Ok with complex logic and data structures
Target Audiences
- Desire to learn machine learning concepts
- Wants to build machine learning processing from the ground up
- Ok with complex logic and data structures
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
Chapter 1: Course Introduction
Lecture 1: Introduction
Chapter 2: Machine Learning Concepts
Lecture 1: Introduction
Lecture 2: Supervised and Unsupervised Learning
Lecture 3: Semi-Supervised Learning
Lecture 4: Summary
Chapter 3: First ML Application
Lecture 1: Introduction
Lecture 2: Installing the Environment
Lecture 3: Hello World
Lecture 4: Installing Aaconda and Deep Learning Libraries
Lecture 5: Email Spam Checker – Part 1
Lecture 6: Email Spam Checker – Part 2
Lecture 7: Email Spam Checker Results
Lecture 8: Iris 70/30 – Part 1
Lecture 9: Iris 70/30 – Part 2
Lecture 10: Summary
Chapter 4: Data Analysis
Lecture 1: Introduction
Lecture 2: Data Analysis – Example 1
Lecture 3: Data Analysis – Example 2
Lecture 4: Data Visualization
Lecture 5: Summary
Chapter 5: Linear Algebra
Lecture 1: Introduction
Lecture 2: Parametric Algorithms
Lecture 3: Linear Algebra
Lecture 4: Linear Regression Calculation – Part 1
Lecture 5: Linear Regression Calculation – Part 2
Lecture 6: Regression on Larger Dataset – Part 1
Lecture 7: Regression on Larger Dataset – Part 2
Lecture 8: Regression on Larger Dataset – Part 3
Lecture 9: Summary
Chapter 6: Natural Language Processing
Lecture 1: Introduction
Lecture 2: Natural Language Processing – Part 1
Lecture 3: Natural Language Processing – Part 2
Lecture 4: Tokenizing Content
Lecture 5: Processing Unique Words
Lecture 6: Summarizing Headlines – Part 1
Lecture 7: Summarizing Headlines – Part 2
Lecture 8: Summarizing Headlines – Part 3
Lecture 9: Summary
Chapter 7: Clustering
Lecture 1: Introduction
Lecture 2: Cluster Introduction
Lecture 3: EM and M Clustering
Lecture 4: Clustering Code Walkthrough
Lecture 5: Clustering Iris Data – Part 1
Lecture 6: Clustering Iris Data – Part 2
Lecture 7: Clustering Iris Data – Part 3
Lecture 8: Dendrogram Graphs
Lecture 9: Summary
Lecture 10: Course Summary
Chapter 8: Bonus Material
Lecture 1: Bonus Lecture
Instructors
-
Stone River eLearning
Over 1,000,000 Happy Students
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 2 votes
- 3 stars: 6 votes
- 4 stars: 3 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024