Machine Learning Journey

Machine Learning Journey, available at $54.99, has an average rating of 5, with 34 lectures, based on 3 reviews, and has 33 subscribers.

You will learn about How to use Python, Visual Studio Code and Jupyter Notebooks to code Machine Learning models How to intuitively understand Machine Learning models How to know your model is making accurate predictions How to apply Machine Learning models to real work business situations This course is ideal for individuals who are Beginners interested in Machine Learning or Beginners interested in Data Science or Beginners interested in using Python to code Machine Learning Models It is particularly useful for Beginners interested in Machine Learning or Beginners interested in Data Science or Beginners interested in using Python to code Machine Learning Models.

Enroll now: Machine Learning Journey

Summary

Title: Machine Learning Journey

Price: $54.99

Average Rating: 5

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

How to use Python, Visual Studio Code and Jupyter Notebooks to code Machine Learning models
How to intuitively understand Machine Learning models
How to know your model is making accurate predictions
How to apply Machine Learning models to real work business situations

Who Should Attend

Beginners interested in Machine Learning
Beginners interested in Data Science
Beginners interested in using Python to code Machine Learning Models

Target Audiences

Beginners interested in Machine Learning
Beginners interested in Data Science
Beginners interested in using Python to code Machine Learning Models

What does Siri, Alexa and Google Play have in common?

How is Capital One and Paypal able to instantly detect fraudulent transfers?

How is Google Photos able to identify faces in photos?

How is Youtube able to make wickedly smart suggested videos?

Or Amazon know what you want before you do?

How does FexEx know the best routes and time of day to ship packages?

These are all made possible through Machine Learning algorithms and in this course, you will not only understand them but you will actually BUILD machine learning models in Python.

And you will use them to make predictions on data! Not only that, you will learn how to validate your models are accurate so you can prove to your peers and superiors that your models are trustworthy.

Have always been a little interested in Machine Learning but have been a little intimidated by the math?

Do you feel like you’re way behind the times and it’s too late to get in on the ML Hype?

Maybe you feel like coding in Python and Data Science sounds too hard. Is that you?

If you answered yes to any of those questions this course is for you. I built this course for complete beginners and had a blast building it for you guys.

Here’s a few of things you will build in this course:

How to setup the perfect development environment for coding ML Algorithms
How to use Anaconda, VSCode, Jupyter Notebooks and Python3 to build and test accurate ML models
How to build the perfect preprocessing template for ML engineering
How to understand what One Hot encoding is and why it’s important
How to use the Numpy, Pandas, Matplotlib and Seaborn Python libraries to build beautiful ML models
Understanding Feature Scaling and when you would use it
7 steps to understanding and building Simple Linear Regression models
7 steps to understanding and building Multiple Linear Regression models
7 steps to understanding and building Polynomial Linear Regression models

If you are a data analyst, cyber security professional, college student or just someone not happy in their current job looking for a lucrative career change, then this course is for you. Machine Learning isn’t just a buzz word, it’s a real set of tools people just like you are using to solve real business problems. It’s not to late to get in on this rising trend.

No prior Machine Learning or coding experience is required.

Now is your time!

Let’s get coding shall we?

Course Curriculum

Chapter 1: Data Pre-Processing

Lecture 1: ML Environment Setup: Anaconda

Lecture 2: ML Environment Setup: VSCode

Lecture 3: ML Environment Setup: VSCode Themes

Lecture 4: ML Environment Setup: VSCode + Jupyter Notebooks!

Lecture 5: Step 1: Importing Machine Learning Libraries

Lecture 6: Step 2: Importing your Data

Lecture 7: Step 3: Managing Missing Data

Lecture 8: Step 4: Encoding Categorical Data

Lecture 9: Step 5: Splitting the Data

Lecture 10: Step 6: Feature Scaling

Lecture 11: Refresher: Object Oriented Programming in Python

Chapter 2: Simple Linear Regression

Lecture 1: Step 1: Intuition

Lecture 2: Step 2: Library Import

Lecture 3: Step 3: Data Import

Lecture 4: Step 4: Data Split

Lecture 5: Step 5: Model Training

Lecture 6: Step 6: Data Visualization

Lecture 7: Step 7: Equation Intuition

Chapter 3: Multiple Linear Regression

Lecture 1: Step 1a: Intuition

Lecture 2: Step 1b: More Intuition

Lecture 3: Step 2: Library Import

Lecture 4: Step 3: Data Import

Lecture 5: Step 4: Categorical Encoding

Lecture 6: Step 5: Data Split

Lecture 7: Step 6: Model Training

Lecture 8: Step 7: Making Predictions!

Chapter 4: Polynomial Linear Regression

Lecture 1: Step 1: Intuition

Lecture 2: Step 2: Problem Set

Lecture 3: Step 3: Library Import

Lecture 4: Step 4: Data Import

Lecture 5: Step 5: Model Training

Lecture 6: Step 6: Visualizing Results

Lecture 7: Step 7: Making Predictions

Chapter 5: BONUS SECTION: THANK YOU!!!

Lecture 1: BONUS LECTURE

Instructors

Vonnie Hudson
Ethical Hacker and Teacher

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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!

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