The ‘Cluster Analysis and Unsupervised Machine Learning in Python’ course will teach you data science techniques for pattern recognition, data mining, K-means clustering, hierarchical clustering, and KDE. The course will also teach how to apply Scipy’s Hierarchical Clustering library to data.
The course will help you to write a GMM in Python code and explains algorithmically how Hierarchical Agglomerative Clustering works. The course is usually available for INR 1,299 on Udemy but students can click on the link and get the ‘Cluster Analysis and Unsupervised Machine Learning in Python’ for INR 449.
Who all can opt for this course?
- Data science and machine learning enthusiasts, both students and professionals
- Who wish to learn more about cluster analysis and unsupervised machine learning
- Those who are interested in writing their own clustering code
- Professionals with an interest in automatically identifying patterns in large data sets
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 08 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 24,960 students |
| Instructor | Lazy Programmer Team https://www.linkedin.com/in/lazyprogrammerteam |
| Topics Covered | K-Means Clustering, Hierarchical Clustering, Gaussian Mixture Models |
| Course Level | Intermediate |
| Total Student Reviews | 4,700 |
Learning Outcomes
- Recognize the standard K-Means algorithm
- Recognize and list the drawbacks of K-Means clustering
- The soft or fuzzy K-Means Clustering algorithm should be understood
- Create code that uses soft k-means clustering
- Be familiar with hierarchical clustering
- Describe the algorithmic operation of hierarchical agglomerative clustering
- Use the Hierarchical Clustering package in Scipy to analyse data
- Know how to interpret a dendrogram
- Recognize the various distance metrics that are applied to clustering
- Know the distinctions between UPGMA, Ward linkage, full linkage, and single linkage
- Knowing how to estimate density using the Gaussian mixture model
- Codify a GMM in Python
- When does GMM replace K-Means Clustering, the algorithm for expectation-maximization, in detail
- Learn how GMM circumvents several K-Means limitations
- Recognize the Singular Covariance issue and how to resolve it
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction to Unsupervised Learning (37 minutes) | Introduction |
| Course Outline | ||
| What is unsupervised learning used for? | ||
| Why Use Clustering? | ||
| Where to get the code | ||
| How to Succeed in this Course | ||
| 2. | K-Means Clustering (02 hours 29 minutes) | An Easy Introduction to K-Means Clustering |
| Hard K-Means: Exercise Prompt 1 | ||
| Hard K-Means: Exercise 1 Solution | ||
| Hard K-Means: Exercise Prompt 2 | ||
| Hard K-Means: Exercise 2 Solution | ||
| Hard K-Means: Exercise Prompt 3 | ||
| Hard K-Means: Exercise 3 Solution | ||
| Hard K-Means Objective: Theory | ||
| Hard K-Means Objective: Code | ||
| Soft K-Means | ||
| The Soft K-Means Objective Function | ||
| Soft K-Means in Python Code | ||
| How to Pace Yourself | ||
| Visualizing Each Step of K-Means | ||
| Examples of where K-Means can fail | ||
| Disadvantages of K-Means Clustering | ||
| How to Evaluate a Clustering (Purity, Davies-Bouldin Index) | ||
| Using K-Means on Real Data: MNIST | ||
| One Way to Choose K | ||
| K-Means Application: Finding Clusters of Related Words | ||
| Clustering for NLP and Computer Vision: Real-World Applications | ||
| Suggestion Box | ||
| 3. | Hierarchical Clustering (43 minutes) | Visual Walkthrough of Agglomerative Hierarchical Clustering |
| Agglomerative Clustering Options | ||
| Using Hierarchical Clustering in Python and Interpreting the Dendrogram | ||
| Application: Evolution | ||
| Application: Donald Trump vs. Hillary Clinton Tweets | ||
| 4. | Gaussian Mixture Models (GMMs) (01 hour 38 minutes) | Gaussian Mixture Model (GMM) Algorithm |
| Write a Gaussian Mixture Model in Python Code | ||
| Practical Issues with GMM / Singular Covariance | ||
| Comparison between GMM and K-Means | ||
| Kernel Density Estimation | ||
| GMM vs Bayes Classifier (pt 1) | ||
| GMM vs Bayes Classifier (pt 2) | ||
| Expectation-Maximization (pt 1) | ||
| Expectation-Maximization (pt 2) | ||
| Expectation-Maximization (pt 3) | ||
| Future Unsupervised Learning Algorithms You Will Learn | ||
| 5. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||
| 6. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (42 minutes) | How to Code by Yourself (part 1) |
| How to Code by Yourself (part 2) | ||
| Proof that using Jupyter Notebook is the same as not using it | ||
| Python 2 vs Python 3 | ||
| 7. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes) | How to Succeed in this Course (Long Version) |
| Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? | ||
| Machine Learning and AI Prerequisite Roadmap (pt 1) | ||
| Machine Learning and AI Prerequisite Roadmap (pt 2) | ||
| 8. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |
| BONUS |
Resources Required
- Python and Numpy coding skills are required
- Install Numpy and Scipy
- Probability, matrix maths
Featured Review
Arnav Arya (5/5) : Well structured and pragmatic course for best practices in clustering analysis
Pros
- Mathieu Fortier (5/5) : Focus is placed on pragmatism, and the coding examples are excellent in providing a deeper understanding of the concepts in an applied setting.
- Quang Hoang (5/5) : I will try my best to advance my understanding by using what I’ve learned.
- Haresh Patel (5/5) : This was an excellent introductory course to clustering data using python.
- Thomas Chiang (5/5) : A very good course for students with a programming mindset, who want to understand the algos and program them from scratch.
Cons
- Octavio Vite S. (2.5/5) : the part of how the show you the theory and maybe how to write the code is OK, but you not have almost nothing to practice, he does not take data from the real word to show you how to do it.
- Carlos C. (2.5/5) : The course is quite fast
- Vishal B. (2.5/5) : Other resources online have looked much better . Not much explanation behind each step
- Sergey M. (2.5/5) : Other courses present the same concepts in clearer way
About the Author
The instructor of this course is Lazy Programmer Team who is a Artificial Intelligence and Machine Learning Engineer. With 4.7 Instructor Rating and 51,803 Reviews on Udemy, he/she offers 17 Courses and has taught 188,652 Students so far.
- Although Instructor have also been recognised as a data scientist, big data engineer, and full stack software engineer, Instructor currently spend the majority of his/her time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
- Instructor earned the first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
- Instructor’s second master’s degree in statistics with a focus on financial engineering was awarded to the Instructor
- Data scientist and big data engineer with experience in online advertising and digital media (optimising click and conversion rates) (building data processing pipelines)
- Instructor routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
- Instructor developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation
- In Instructor’s work with recommendation systems, I’ve used collaborative filtering and reinforcement learning, and we validated the findings using A/B testing
- Instructor have instructed students at universities like Columbia University, NYU, Hunter College, and The New School in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics
- Instructor web programming skills have helped numerous businesses
- Instructor handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work
Comparison Table
| Parameters | Cluster Analysis and Unsupervised Machine Learning in Python | Data Science: Supervised Machine Learning in Python | Unsupervised Deep Learning in Python |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 8 hours | 6.5 hours | 10 hours |
| Rating | 4.7 /5 | 4.6 /5 | 4.8 /5 |
| Student Enrollments | 24,951 | 20,365 | 20,183 |
| Instructors | Lazy Programmer Team | Lazy Programmer Team | Lazy Programmer Team |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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