‘The Complete Machine Learning Course with Python’ course will help you to Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning and more.
By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, detect cancer cells and much more. The course is usually available for INR 2,599 on Udemy but students can click on the link and get the ‘The Complete Machine Learning Course with Python’ for INR 449.
Who all can opt for this course?
- Anyone who wants to study Python-based machine learning algorithms
- Anyone with a keen interest in the application of machine learning in practise to issues in the real world
- Anyone who wants to study more than the fundamentals and gain a comprehensive understanding of machine learning methods
- Anyone who is unable to work with huge datasets and is an intermediate to advanced EXCEL user
- Someone with an interest in effectively and professionally communicating their findings
- Anyone looking to begin or advance their career as a data scientist
- Whomever wants to use machine learning into their industry
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 17 Hours |
| Rating | 4.4/5 |
| Student Enrollment | 33,965 students |
| Instructor | Codestars • over 2 million students worldwide! https://www.linkedin.com/in/codestars•over2millionstudentsworldwide! |
| Topics Covered | Machine learning, Python, SVM, Regression |
| Course Level | Intermediate |
| Total Student Reviews | 6,015 |
Learning Outcomes
- With this course, you can become an excellent candidate for the $166,000 average salary of machine learning engineers
- With the help of robust machine learning models, you can solve any issue in your business, career, or personal life
- Teach computer programmes to recognise handwriting, detect cancer cells, and other things
- Study Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning, etc
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (04 minutes) | What Does the Course Cover? |
| How to Succeed in This Course | ||
| Project Files and Resources | ||
| 2. | Getting Started with Anaconda (56 minutes) | Installing Applications and Creating Environment |
| Hello World | ||
| Iris Project 1: Working with Error Messages | ||
| Iris Project 2: Reading CSV Data into Memory | ||
| Iris Project 3: Loading data from Seaborn | ||
| Iris Project 4: Visualization | ||
| 3. | Regression (04 hours 06 minutes) | Scikit-Learn |
| EDA | ||
| Correlation Analysis and Feature Selection | ||
| Correlation Analysis and Feature Selection | ||
| Linear Regression with Scikit-Learn | ||
| Five Steps Machine Learning Process | ||
| Robust Regression | ||
| Evaluate Regression Model Performance | ||
| Multiple Regression 1 | ||
| Multiple Regression 2 | ||
| Regularized Regression | ||
| Polynomial Regression | ||
| Dealing with Non-linear Relationships | ||
| Feature Importance | ||
| Data Preprocessing | ||
| Variance-Bias Trade Off | ||
| Learning Curve | ||
| Cross Validation | ||
| CV Illustration | ||
| 4. | Classification (01 hour 44 minutes) | Logistic Regression |
| Introduction to Classification | ||
| Understanding MNIST | ||
| SGD | ||
| Performance Measure and Stratified k-Fold | ||
| Confusion Matrix | ||
| Precision | ||
| Recall | ||
| f1 | ||
| Precision Recall Tradeoff | ||
| Altering the Precision Recall Tradeoff | ||
| ROC | ||
| 5. | Support Vector Machine (SVM) (39 minutes) | Support Vector Machine (SVM) Concepts |
| Linear SVM Classification | ||
| Polynomial Kernel | ||
| Radial Basis Function | ||
| Support Vector Regression | ||
| 6. | Tree (01 hour 05 minutes) | Introduction to Decision Tree |
| Training and Visualizing a Decision Tree | ||
| Visualizing Boundary | ||
| Tree Regression, Regularization and Over Fitting | ||
| End to End Modeling | ||
| Project HR | ||
| Project HR with Google Colab | ||
| 7. | Ensemble Machine Learning (01 hour 12 minutes) | Ensemble Learning Methods Introduction |
| Bagging | ||
| Random Forests and Extra-Trees | ||
| AdaBoost | ||
| Gradient Boosting Machine | ||
| XGBoost Installation | ||
| XGBoost | ||
| Project HR – Human Resources Analytics | ||
| Ensemble of Ensembles Part 1 | ||
| Ensemble of ensembles Part 2 | ||
| 8. | k-Nearest Neighbours (kNN) (39 minutes) | kNN Introduction |
| Project Cancer Detection | ||
| Addition Materials | ||
| Project Cancer Detection Part 1 | ||
| 9. | Unsupervised Learning: Dimensionality Reduction (36 minutes) | Dimensionality Reduction Concept |
| PCA Introduction | ||
| Project Wine | ||
| Kernel PCA | ||
| Kernel PCA Demo | ||
| LDA vs PCA | ||
| Project Abalone | ||
| 10. | Unsupervised Learning: Clustering (24 minutes) | Clustering |
| k_Means Clustering | ||
| 11. | Deep Learning (01 hour 11 minutes) | Estimating Simple Function with Neural Networks |
| Neural Network Architecture | ||
| Motivational Example – Project MNIST | ||
| Binary Classification Problem | ||
| Natural Language Processing – Binary Classification | ||
| 12. | Appendix A1: Foundations of Deep Learning (02 hours 03 minutes) | Introduction to Neural Networks |
| Differences between Classical Programming and Machine Learning | ||
| Learning Representations | ||
| What is Deep Learning | ||
| Learning Neural Networks | ||
| Why Now? | ||
| Building Block Introduction | ||
| Tensors | ||
| Tensor Operations | ||
| Gradient Based Optimization | ||
| Getting Started with Neural Network and Deep Learning Libraries | ||
| Categories of Machine Learning | ||
| Over and Under Fitting | ||
| Machine Learning Workflow | ||
| 13. | Computer Vision and Convolutional Neural Network (CNN) (02 hours 34 minutes) | Outline |
| Neural Network Revision | ||
| Motivational Example | ||
| Visualizing CNN | ||
| Understanding CNN | ||
| Layer – Input | ||
| Layer – Filter | ||
| Activation Function | ||
| Pooling, Flatten, Dense | ||
| Training Your CNN 1 | ||
| Training Your CNN 2 | ||
| Loading Previously Trained Model | ||
| Model Performance Comparison | ||
| Data Augmentation | ||
| Transfer Learning | ||
| Feature Extraction | ||
| State of the Art Tools |
Resources Required
- Python programming fundamentals are required
- Excellent knowledge of linear algebra
Featured Review
Victor Oluwamayowa Olatunji (5/5) : the class was awesome!!!! but i will need to tutor to provide link to the resources to avoid being passive learner. with adequate and comprehensive we can learn and archive real quick.
Pros
- Raymond Carreon (5/5) : This is simply an awesome course.! An excellent reference material too.
- Mirza Baig (5/5) : It was a very good training which helped me to learn about machine language
- Raja Ranjith Garikapati (5/5) : Very good content and learned a lot of concepts on ML with this course.
- Johannes Tannert (4/5) : So it is not the ideal course to code along and learn.
Cons
- Russell Ritenour (2/5) : At the point where we get to bagging, the code provided in the github repository is useless for experimentation and bears no resemblance to the lecture.
- Ahmed Bagais (2/5) : I am still unable to get to the Anaconda Navigator screen.
- Niklas Gustafsson (2/5) : A lot of unecessary words are being used, it’s slightly irritating.
- Irene Pérez (2/5) : For the moment is it really slow, not on the content but on the way of speaking.
About the Author
The instructor of this course is Codestars • over 2 million students worldwide! who is a Teaching the Next Generation of Coders. With 4.5 Instructor Rating and 465,698 Reviews on Udemy, he/she offers 80 Courses and has taught 2,215,618 Students so far.
- Best-selling Rob Percival, an Udemy instructor, aims to transform the way people learn to code by making it easy, rational, entertaining, and above all, accessible
- Rob was able to develop some of the courses that his more than 500,000 students requested, but he was only one man
- Rob founded Codestars with that in mind
- Together, the teachers who make up the Codestars team design well-structured, incredibly dynamic, and simple-to-understand courses on all the subjects that students are interested in learning
- For students of all ages and skill levels, Codestars strives to make it as simple as possible for them to create useful websites and apps
Comparison Table
| Parameters | The Complete Machine Learning Course with Python | Data Science: Modern Deep Learning in Python | Data Science: Deep Learning and Neural Networks in Python |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 17.5 hours | 11.5 hours | 12 hours |
| Rating | 4.4/5 | 4.7/5 | 4.6/5 |
| Student Enrollments | 33,954 | 33,626 | 52,709 |
| Instructors | Codestars • over 2 million students worldwide! | Lazy Programmer Inc. | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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