Machine Learning for Data Science using MATLAB course will teach you all the fundamentals of machine learning techniques without having to study all the intricate maths. This course takes a highly practical approach, and starts from scratch on everything. After a few basic lessons, the course will jump right into coding, trying to keep the theory to a minimum. The entire coding will be done in MATLAB, which is one of the core programming languages for engineering and science students and is regularly utilised by leading data science research groups globally.
The course covers the fundamental preprocessing methods along with issues like removing outliers, handling missing values, transforming categorical data into numerical form, and feature scalability. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Machine Learning for Data Science using MATLAB for INR 449.
Who all can opt for this course?
- Researchers, Business owners, Professors, College students, Engineers, and Programmers
- Anybody who wants to examine the data
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 16 Hours |
| Rating | 4.5/5 |
| Student Enrollment | 1,917 students |
| Instructor | Nouman Azam https://www.linkedin.com/in/noumanazam |
| Topics Covered | Data Science, Data processing, Classification of algorithms in MATLAB, Malware Analysis, Dimensionality reduction, and Data Preprocessing |
| Course Level | Beginner |
| Total Student Reviews | 371 |
Learning Outcomes
- How to use Matlab to create several machine learning classification methods
- How to use Matlab to implement several machine learning clustering techniques
- Information on how to prepare data for analysis
- Using dimensionality reduction: when and how
- Remove the code templates
- Visualisation of algorithmic results
- How to choose the appropriate algorithm for your dataset
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction to course and MATLAB (16 minutes) | Course introduction |
| MATLAB essentials for the course | ||
| Tell us about the course | ||
| 2. | ————————— Data Preprocessing ————————— (01 hour 08 minutes) | Code and Data |
| Section Introduction | ||
| Importing the Dataset | ||
| Removing Missing Data (Part 1) | ||
| Removing Missing Data (Part 2) | ||
| Feature Scaling | ||
| Handling Outliers (Part 1) | ||
| Handling Outliers (Part 2) | ||
| Dealing with Categorical Data (Part 1) | ||
| Dealing with categorical data (Part 2) | ||
| Your Preprocessing Template | ||
| 3. | ————————— Classification ————————— (02 seconds) | Code and Data |
| 4. | K-Nearest Neighbor (01 hour 15 minutes) | KNN Intuition |
| KNN in MATLAB (Part 1) | ||
| KNN in MATLAB (Part 2) | ||
| Visualizing the Decision Boundaries of KNN | ||
| Explaining the code for visualization | ||
| Here is our classification template | ||
| How to change default options and customize classifiers | ||
| Customization options for KNN | ||
| 5. | Naive Bayes (36 minutes) | Naive Bayesain Intuition (Part 1) |
| Naive Bayesain Intuition (Part 2) | ||
| Naive Bayesain in MATLAB | ||
| Customization Options for Naive Bayesain | ||
| 6. | Decision Trees (33 minutes) | Decision trees intuition |
| Decision Trees in MATLAB | ||
| Visualizing Decision Trees using the View Function | ||
| Customization Options for Decision Trees | ||
| 7. | Support Vector Machines (38 minutes) | SVM Intuition |
| Kernel SVM Intuition | ||
| SVM in MATLAB | ||
| Customization Options for SVM | ||
| 8. | Discriminant Analysis (22 minutes) | Discriminant Analysis Intuition |
| Discriminant Analysis in MATLAB | ||
| Customization Options for Discriminant Analysis | ||
| 9. | Ensembles (36 minutes) | Ensembles Intuition |
| Ensembles in MATLAB | ||
| Customization options for Ensembles | ||
| 10. | Performance Evaluation (56 minutes) | Evaluating Classifiers: Confusion matrix (Theory) |
| Validation Methods (Theory) | ||
| Validation methods in MATLAB (Part 1) | ||
| Validation methods in MATLAB (Part 2) | ||
| Evaluating Classifiers in MATLAB | ||
| 11. | ————————– Clustering ————————— (02 seconds) | Code and Data |
| 12. | K-Means (01 hour 26 minutes) | K-Means Clustering Intuition |
| Choosing the number of clusters | ||
| k-means in MATLAB (Part 1) | ||
| k-means in MATLAB (Part 2) | ||
| KMeans Limitations – (Part 1-Clusters with different sizes) | ||
| KMeans Limitations – (Part-2-Clusters with non spherical shapes) | ||
| KMeans Limitations – (Part 3-Clusters with varying densities) | ||
| 13. | Mean Shift Clustering (39 minutes) | Intuition of Mean Shift |
| Mean Shift in MATLAB | ||
| Mean Shift Performance in Cases where Kmean Fails (Part 1) | ||
| Mean Shift Performance in Cases where Kmean Fails (Part 2) | ||
| 14. | DBSCAN (49 minutes) | Intuition of DBSCAN |
| DBSCAN in MATLAB | ||
| DBSCAN on clusters with varying sizes | ||
| DBSCAN on clusters with different shapes and densities | ||
| DBSCAN for handling noise | ||
| Practical Activity | ||
| 15. | Hierarchical Clustering (37 minutes) | Hierarchical Clustering Intuition (Part 1) |
| Hierarchical Clustering Intuition (Part 2) | ||
| Hierachical Clustering in MATLAB | ||
| 16. | Projects: Image Compression and Sentence Clustering (45 minutes) | Image Compression (Part 1) |
| Image Compression (Part 2) | ||
| Clustering sentences (Part 1) | ||
| Clustering sentences (Part 2) | ||
| 17. | ————————– Dimensionality Reduction —————— (38 minutes) | Code and data |
| Principal Component Analysis | ||
| PCA in MATLAB (Part 1) | ||
| PCA in MATLAB (Part 2) | ||
| 18. | Project: Malware Analysis (41 minutes) | Code and data |
| Problem Discription | ||
| Customizing code templates for completing Task 1 and 2 (Part 1) | ||
| Customizing code templates for completing Task 1 and 2 (Part 2) | ||
| Customizing code templates for completing Task 3, 4 and 5 | ||
| Here is the project | ||
| 19. | ———————- Data Preprocessing (Detailed) ————————— (02 seconds) | Codes and Data |
| 20. | Handling Missing Values (53 minutes) | Deletion strategies |
| Using mean and mode | ||
| Considering as a special value | ||
| Class specific mean and mode | ||
| Random Value Imputation | ||
| 21. | Dealing with Categorical Variables (38 minutes) | Categorical data with no order |
| Categorical data with order | ||
| Frequency based encoding | ||
| Target based encoding | ||
| 22. | Outlier Detection (01 hour 21 minutes) | 3 sigma rule with deletion strategy |
| 3 sigma rule with filling strategy | ||
| Box plots and iterquartile rule | ||
| Class specific box plots | ||
| Histograms for outliers | ||
| Local Outlier Factor (Part 1) | ||
| Local Outlier Factor (Part 2) | ||
| Outliers in Categorical Variables | ||
| 23. | Feature Scaling and Data Discretization (32 minutes) | Feature Scalling |
| Discretization using Equal width binning | ||
| Discretization using Equal Frequency binning | ||
| 24. | Project: Selecting the Right Method for your Data (27 minutes) | Selecting the right method (Part 1) |
| Selecting the right method (Part 2) |
Resources Required
- 2017a or a newer version of MATLAB
- No prior MATLAB experience is necessary
- Several features may not operate in versions 2017a and lower
Featured Review
Oamar Kanji (5/5) : Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!
Pros
- Supriyo Choudhury (5/5) : Dr Azam is one of the best teachers I ever came across.
- Edward Ingenito (5/5) : This is one of the best Udemy courses I have participated in.
- Mana Sriphet (5/5) : Very good explanation of ML steps and how to implement it via MATLAB.
- Grigoris Athanasiadis (5/5) : It is a very good tutorial for a starter to machine learning projects with matlab.
Cons
- Emmanuel G. (2.5/5) : Content is good so far but the presenter is speaking really fast as such it is diffifcult to follow.
- Sadhana P. (2.5/5) : less hands-on session for complicated stuff
- Chidvilas Karpenahalli R. (3/5) : The approach and explanation are good. However, the content could have been better.
About the Author
The instructor of this course is Nouman Azam who is a MATLAB Professor. With 4.4 Instructor Rating and 5,284 Reviews on Udemy, he/she offers 7 Courses and has taught 41,566 Students so far.
- Instructor is an Associate Professor of Computer Science.
- Instructor have a large student body of more than 25,000 students across many online platforms and have a dynamic community of students who take instructor’s online MATLAB programming classes
- These courses concentrate on MATLAB’s various features and how to use them successfully in day-to-day work
- Students can learn about MATLAB programming, GUI design, data analysis, and visualisation in this course
- One of instructor’s favourite topics is the use of MATLAB in machine learning techniques
- Instructor investigate the application of MATLAB in bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems, and medical decision making
Comparison Table
| Parameters | Machine Learning for Data Science using MATLAB | Machine Learning, Deep Learning & Neural Networks in Matlab | LEARNING PATH: MATLAB: Powerful Machine Learning with MATLAB |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 16 hours | 4 hours | 4 hours |
| Rating | 4.5 /5 | 4.4 /5 | 4.5 /5 |
| Student Enrollments | 1,917 | 515 | 337 |
| Instructors | Nouman Azam | Eliott Wertheimer | Packt Publishing |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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