This course is designed for those interested to learn the basics of machine learning and how to implement different machine learning classification algorithms using MATLAB.
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Who this course is for:
- This course is for you if you want to have a real feel of the machine learning techniques without having to learn all the complicated math.Â
- This course is for you if you have had previous hours and hours of machine learning theory but could never figure out how to implement and solve data science problems with it.
What you’ll learn:Â
- How to implement different machine learning classification algorithms using MATLAB
- How to implement different machine learning clustering algorithms using MATLAB
- How to preprocess data before analysis
- When and how to use dimensionality reduction
- Take away code templates
- Visualization results of algorithms
- Decide which algorithm to choose for your dataset
Requirements:Â
- No prior knowledge of MATLAB is required
The approach in this course is very practical and we will start everything from scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal.Â
Below is a brief outline of this course.
Segment 1: Introduction To Course And Say Hi To MATLAB
Segment 2: Data Preprocessing
Segment 3: Classification Algorithms In MATLAB
Segment 4: Clustering Algorithms In MATLAB
Segment 5: Dimensionality Reduction
Segment 6: Project: Malware Analysis
All the coding will be done in MATLAB which is one of the fundamental programming languages for engineering and data science students and is frequently used by top data science research groups worldwide.
Our Promise to You
By the end of this course, you will have learned about machine learning using MATLAB.
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Course Curriculum
| Section 1 - Introduction To Course And MATLAB | |||
| Downloadable Material | 00:00:00 | ||
| Course Introduction | 00:00:00 | ||
| MATLAB Essentials For The Course | 00:00:00 | ||
| Section 2 - Data Preprocessing | |||
| Section Introduction | 00:00:00 | ||
| Importing The Dataset | 00:00:00 | ||
| Removing Missing Data – Part 1 | 00:00:00 | ||
| Removing Missing Data – Part 2 | 00:00:00 | ||
| Feature Scaling | 00:00:00 | ||
| Handling Outliers – Part 1 | 00:00:00 | ||
| Handling Outliers – Part 2 | 00:00:00 | ||
| Dealing With Categorical Data – Part 1 | 00:00:00 | ||
| Dealing With Categorical Data – Part 2 | 00:00:00 | ||
| Your Preprocessing Template | 00:00:00 | ||
| Section 3 - K - Nearest Neighbor | |||
| K – Nearest Neighbor Intuition | 00:00:00 | ||
| K – Nearest Neighbor In MATLAB – Part 1 | 00:00:00 | ||
| K – Nearest Neighbor In MATLAB – Part 2 | 00:00:00 | ||
| Visualizing The Decision Boundaries Of K – Nearest Neighbor | 00:00:00 | ||
| Explaining The Code For Visualization | 00:00:00 | ||
| Here Is Our Classification Template | 00:00:00 | ||
| How To Change Default Options And Customize Classifiers | 00:00:00 | ||
| Customization Options For K – Nearest Neighbor | 00:00:00 | ||
| Section 4 - Naive Bayes | |||
| Naive Bayesain Intuition – Part 1 | 00:00:00 | ||
| Naive Bayesain Intuition – Part 2 | 00:00:00 | ||
| Naive Bayesain In MATLAB | 00:00:00 | ||
| Customization Options For Naive Bayesain | 00:00:00 | ||
| Section 5 - Decision Trees | |||
| Decision Trees Intuition | 00:00:00 | ||
| Decision Trees In MATLAB | 00:00:00 | ||
| Visualizing Decision Trees Using The View Function | 00:00:00 | ||
| Customization Options For Decision Trees | 00:00:00 | ||
| Section 6 - Support Vector Machines | |||
| Support Vector Machines Intuition | 00:00:00 | ||
| Kernel Support Vector Machines Intuition | 00:00:00 | ||
| Support Vector Machines In MATLAB | 00:00:00 | ||
| Customization Options For Support Vector Machines | 00:00:00 | ||
| Section 7 - Discriminant Analysis | |||
| Discriminant Analysis Intuition | 00:00:00 | ||
| Discriminant Analysis In MATLAB | 00:00:00 | ||
| Customization Options For Discriminant Analysis | 00:00:00 | ||
| Section 8 - Ensembles | |||
| Ensembles Intuition | 00:00:00 | ||
| Ensembles In MATLAB | 00:00:00 | ||
| Customization Options For Ensembles | 00:00:00 | ||
| Section 9 - Performance Evaluation | |||
| Evaluating Classifiers: Confusion Matrix (Theory) | 00:00:00 | ||
| Validation Methods (Theory) | 00:00:00 | ||
| Validation Methods In MATLAB – Part 1 | 00:00:00 | ||
| Validation Methods In MATLAB – Part 2 | 00:00:00 | ||
| Evaluating Classifiers In MATLAB | 00:00:00 | ||
| Section 10 - K-Means | |||
| K-Means Clustering Intuition | 00:00:00 | ||
| Choosing The Number Of Clusters | 00:00:00 | ||
| K-Means In MATLAB – Part 1 | 00:00:00 | ||
| K-Means In MATLAB – Part 2 | 00:00:00 | ||
| Section 11 - Hierarchical Clustering | |||
| Hierarchical Clustering Intuition – Part 1 | 00:00:00 | ||
| Hierarchical Clustering Intuition – Part 2 | 00:00:00 | ||
| Hierarchical Clustering In MATLAB | 00:00:00 | ||
| Section 12 - Dimensionality Reduction | |||
| Principal Component Analysis | 00:00:00 | ||
| Principal Component Analysis In MATLAB – Part 1 | 00:00:00 | ||
| Principal Component Analysis In MATLAB – Part 2 | 00:00:00 | ||
| Section 13 - Project: Malware Analysis | |||
| Problem Description | 00:00:00 | ||
| Customizing Code Templates For Completing Task 1 And 2 – Part 1 | 00:00:00 | ||
| Customizing Code Templates For Completing Task 1 And 2 – Part 2 | 00:00:00 | ||
| Customizing Code Templates For Completing Task 3, Task 4, And 5 | 00:00:00 | ||
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