“Data Science: Supervised Machine Learning in Python” will help the students to understand and implement K-Nearest Neighbors in Python. Google famously declared that they are now putting “machine learning first,” which means that machine learning will receive a lot more attention going forward and will be the main driver of innovation. It is incorporated into a wide range of items. Numerous sectors, including finance, online advertising, healthcare, and robotics, use machine learning.
No matter what business students work in, it is a tool that they can use to their advantage, and if you master it, it will provide you with a wealth of professional prospects. Additionally, machine learning brings up certain philosophical issues. Currently, udemy is offering Data Science: Supervised Machine Learning in Python for up to 80 % off i.e. INR 499 (INR 1,299).
Who Can Opt for This Course?
- Who wish to apply machine learning techniques to their datasets, including academics and professionals
- Students and professionals that desire to use machine learning methods to solve issues in the real world
- Anyone who wants to study fundamental algorithms for machine learning and data science
- Anyone interested in learning more about artificial intelligence (AI)
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 499 ( |
| Duration | 06 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 19,623 students |
| Instructor | Lazy Programmer Team https://www.linkedin.com/in/lazyprogrammerteam |
| Topics Covered |
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| Course Level | Advanced/Experienced |
| Total Student Reviews | 2,595 |
Learning Outcomes
- Learn about and use Python’s K-Nearest Neighbors algorithm
- Recognize KNN’s limitations
- KNN can be used to resolve a variety of binary and multiclass classification issues
- Learn how to use the Python Naive Bayes and General Bayes Classifiers
- Recognize the Bayes Classifier’s constraints
- Python Decision Tree implementation and comprehension
- Python Perceptron implementation and understanding
- Recognize the Perceptron’s limitations
- Recognize hyperparameters and cross-validation techniques
- Recognize the ideas behind feature selection and feature extraction
- Recognize the advantages and disadvantages of deep learning against traditional machine learning techniques
- Apply Sci-Kit Learn
- Use a web service for machine learning
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction and Review (32 minutes) | Introduction and Outline |
| How to Succeed in this Course | ||
| Where to get the Code and Data | ||
| Review of Important Concepts | ||
| 2. | K-Nearest Neighbor (38 minutes) | K-Nearest Neighbor Intuition |
| K-Nearest Neighbor Concepts | ||
| KNN in Code with MNIST | ||
| When KNN Can Fail | ||
| KNN for the XOR Problem | ||
| KNN for the Donut Problem | ||
| Effect of K | ||
| KNN Exercise | ||
| Suggestion Box | ||
| 3. | Naive Bayes and Bayes Classifiers (01 hour 02 minutes) | Bayes Classifier Intuition (Continuous) |
| Bayes Classifier Intuition (Discrete) | ||
| Naive Bayes | ||
| Naive Bayes Handwritten Example | ||
| Naive Bayes in Code with MNIST | ||
| Non-Naive Bayes | ||
| Bayes Classifier in Code with MNIST | ||
| Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) | ||
| Generative vs Discriminative Models | ||
| 4. | Decision Trees (38 minutes) | Decision Tree Intuition |
| Decision Tree Basics | ||
| Information Entropy | ||
| Maximizing Information Gain | ||
| Choosing the Best Split | ||
| Decision Tree in Code | ||
| 5. | Perceptrons (19 minutes) | Perceptron Concepts |
| Perceptron in Code | ||
| Perceptron for MNIST and XOR | ||
| Perceptron Loss Function | ||
| 6. | Practical Machine Learning (31 minutes) | Hyperparameters and Cross-Validation |
| Feature Extraction and Feature Selection | ||
| Comparison to Deep Learning | ||
| Multiclass Classification | ||
| Sci-Kit Learn | ||
| Regression with Sci-Kit Learn is Easy | ||
| 7. | Building a Machine Learning Web Service (10 minutes) | Building a Machine Learning Web Service Concepts |
| Building a Machine Learning Web Service Code | ||
| 8. | Conclusion (02 minutes) | What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest) |
| 9. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn | ||
| 10. | 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 | ||
| 11. | 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) | ||
| 12. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |
| BONUS |
Resources Required
- Experience with Python, Numpy, and Pandas
- Statistics and probability (Gaussian distribution)
- Strong algorithmic writing skills
Festured Review
Dev Narine (5/5): coming from a computer science background this is a superb guide to machine learning. better than typical classroom lectures which don’t involve much coding and application.
Pros
- Shaurya Sinha (5/5): The best part is that you know what questions/curiosity going on in students’ minds while watching these videos.
- Yash Chabra (5/5): A very good course to learn the foundations of machine learning
- Sharad Tiwari (5/5): This is the ideal course for getting started on machine learning.
- Abhyuday Desai (5/5): This instructor’s courses are great if you want to learn the theory.
Cons
- Paedru F. (3/5): He is good in explaining the holistic view and maths to a good extent that an average person good in maths and programming can understand
- Ajayjain J. (3/5): you know the content but the level of understanding at ground level is much different then what u think . the way of teaching is much boring, u can’t understand the people what u trying to convey. I/t’s not an easy task to get the link with u.
- Sunny W. (3/5): Great explanation on the purpose of the statistic testing and math behind it for the pseudocode
- Sebastian M. (3/5): Depends on what you are expecting. I would have preferred more visuals and real world examples explaining the algorithms. Also specific tasks would have been nice instead of ‘Here is the theory, just go implement it yourself’.
About the Author
The instructor of this course is Lazy Programmer Team who is an Artificial Intelligence and Machine Learning Engineer with a 4.6 Instructor Rating and 48,043 Reviews on Udemy. He/she offers 16 Courses and has taught 179,391 Students so far.
- Although Lazy Programmer Team has also been recognized as a data scientist, big data engineer, and full stack software engineer, he currently spends the majority of my time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
- Lazy Programmer Team earned their first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
- Lazy Programmer Team earns second master’s degree in statistics with a focus on financial engineering was awarded to them
- Data scientist and big data engineer with experience in online advertising and digital media
- They routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
- I’ve developed deep learning models for text modeling, image and signal processing, user behavior prediction, and click-through rate estimation
- In their work with recommendation systems, They have used collaborative filtering and reinforcement learning, and we validated the findings using A/B testing
- They 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
- Their web programming skills have helped numerous businesses
- They handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work
Comparison Table
| Parameters | Data Science: Supervised Machine Learning in Python | Unsupervised Machine Learning Hidden Markov Models in Python | Ensemble Machine Learning in Python: Random Forest, AdaBoost |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 6.5 hours | 10 hours | 5.5 hours |
| Rating | 4.7 /5 | 4.6 /5 | 4.7 /5 |
| Student Enrollments | 19,623 | 25,050 | 14,398 |
| Instructors | Lazy Programmer Team | Lazy Programmer Team | Lazy Programmer Team |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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