English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 33 lectures (5h 0m) | 2.71 GB
How to use Python 3, Jupyter Notebooks and Visual Studio Code to solve business problems with 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.
What you’ll 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
Table of Contents
Data Pre-Processing
1 ML Environment Setup Anaconda
2 ML Environment Setup VSCode
3 ML Environment Setup VSCode Themes
4 ML Environment Setup VSCode + Jupyter Notebooks!
5 Step 1 Importing Machine Learning Libraries
6 Step 2 Importing your Data
7 Step 3 Managing Missing Data
8 Step 4 Encoding Categorical Data
9 Step 5 Splitting the Data
10 Step 6 Feature Scaling
11 Refresher Object Oriented Programming in Python
Simple Linear Regression
12 Step 1 Intuition
13 Step 2 Library Import
14 Step 3 Data Import
15 Step 4 Data Split
16 Step 5 Model Training
17 Step 6 Data Visualization
18 Step 7 Equation Intuition
Multiple Linear Regression
19 Step 1a Intuition
20 Step 1b More Intuition
21 Step 2 Library Import
22 Step 3 Data Import
23 Step 4 Categorical Encoding
24 Step 5 Data Split
25 Step 6 Model Training
26 Step 7 Making Predictions!
Polynomial Linear Regression
27 Step 1 Intuition
28 Step 2 Problem Set
29 Step 3 Library Import
30 Step 4 Data Import
31 Step 5 Model Training
32 Step 6 Visualizing Results
33 Step 7 Making Predictions
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