The Python for Machine Learning & Data Science Masterclass is categorized as one of the best courses for Python. The course is suitable for the ones who wish to analyze, visualize and gain data insight. The course is usually available for INR 3,499 on Udemy but you can click now to get 87% off and get the Python for Machine Learning & Data Science Masterclass for INR 455.
The Python for Machine Learning & Data Science Masterclass course is categorized as beginner-friendly. This is one of the most popular courses online which is apt for learning about Python, Data Science, and Machine Learning. Over 2.6 million students enroll in Jose Portilla’s course to enrich their knowledge.
Who Can Opt for this Course?
- Beginners who are interested in Machine Learning and Data Science
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 44 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 73,716 students |
| Instructor | Jose Portilla |
| Topics Covered |
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| Course Level | Intermediate |
| Total Student Reviews | 9,491 |
Learning Outcomes
- Python will be used to teach you data science and machine learning.
- In order to analyze, visualize, and acquire insights from data, you will develop data pipeline procedures.
- Using data from the real world, you will develop a portfolio of data science projects.
- Data science will enable you to examine your own data sets and obtain new perspectives.
- Develop key data science abilities.
- Know every aspect of machine learning.
- Recreate actual circumstances and data reports.
- Python numerical processing can be done by learning NumPy.
- Perform feature engineering on case studies from the actual world.
- For Python data manipulation, become familiar with Pandas.
- To anticipate classes, develop supervised machine learning algorithms.
- To build completely unique data visualizations using Python, learn Matplotlib.
- For the purpose of predicting continuous values, develop regression machine learning techniques.
- Learn Python’s Seaborn to produce stunning statistical charts.
- Create a futuristic resume project portfolio for data science and machine learning.
- Learn how to implement effective machine learning algorithms with Scikit-learn.
- Utilize the Anaconda data science stack environment to start up rapidly.
- Learn the best techniques for using actual data sets.
- Recognize the entire machine learning lifecycle product workflow.
- Examine the use of interactive APIs for deploying your machine-learning models.
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction to Course (28 minutes) | Welcome to the Course! |
| COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP! | ||
| Anaconda Python and Jupyter Install and Setup | ||
| Note on Environment Setup – Please read me! | ||
| Environment Setup | ||
| 2. | OPTIONAL: Python Crash Course (50 minutes) | OPTIONAL: Python Crash Course |
| Python Crash Course – Part One | ||
| Python Crash Course – Part Two | ||
| Python Crash Course – Part Three | ||
| Python Crash Course – Exercise Questions | ||
| Python Crash Course – Exercise Solutions | ||
| 3. | Machine Learning Pathway Overview (10 minutes) | Machine Learning Pathway |
| 4. | NumPy (52 minutes) | Introduction to NumPy |
| NumPy Arrays | ||
| Coding Exercise Check-in: Creating NumPy Arrays | ||
| NumPy Indexing and Selection | ||
| Coding Exercise Check-in: Selecting Data from Numpy Array | ||
| NumPy Operations | ||
| Check-In: Operations on NumPy Array | ||
| NumPy Exercises | ||
| Numpy Exercises – Solutions | ||
| 5. | Pandas (06 hours 26 minutes) | Introduction to Pandas |
| Series – Part One | ||
| Check-in: Labeled Index in Pandas Series | ||
| Series – Part Two | ||
| DataFrames – Part One – Creating a DataFrame | ||
| DataFrames – Part Two – Basic Properties | ||
| DataFrames – Part Three – Working with Columns | ||
| DataFrames – Part Four – Working with Rows | ||
| Pandas – Conditional Filtering | ||
| Pandas – Useful Methods – Apply on Single Column | ||
| Pandas – Useful Methods – Apply on Multiple Columns | ||
| Pandas – Useful Methods – Statistical Information and Sorting | ||
| Missing Data – Overview | ||
| Missing Data – Pandas Operations | ||
| GroupBy Operations – Part One | ||
| GroupBy Operations – Part Two – MultiIndex | ||
| Combining DataFrames – Concatenation | ||
| Combining DataFrames – Inner Merge | ||
| Combining DataFrames – Left and Right Merge | ||
| Combining DataFrames – Outer Merge | ||
| Pandas – Text Methods for String Data | ||
| Pandas – Time Methods for Date and Time Data | ||
| Pandas Input and Output – CSV Files | ||
| Pandas Input and Output – HTML Tables | ||
| Pandas Input and Output – Excel Files | ||
| Pandas Input and Output – SQL Databases | ||
| Pandas Pivot Tables | ||
| Pandas Project Exercise Overview | ||
| Pandas Project Exercise Solutions | ||
| 6. | Matplotlib (01 hours 51 minutes) | Introduction to Matplotlib |
| Matplotlib Basics | ||
| Matplotlib – Understanding the Figure Object | ||
| Matplotlib – Implementing Figures and Axes | ||
| Matplotlib – Figure Parameters | ||
| Matplotlib – Subplots Functionality | ||
| Matplotlib Styling – Legends | ||
| Matplotlib Styling – Colors and Styles | ||
| Advanced Matplotlib Commands (Optional) | ||
| Matplotlib Exercise Questions Overview | ||
| Matplotlib Exercise Questions – Solutions | ||
| 7. | Seaborn Data Visualizations (02 hours 37 minutes) | Introduction to Seaborn |
| Scatterplots with Seaborn | ||
| Distribution Plots – Part One – Understanding Plot Types | ||
| Distribution Plots – Part Two – Coding with Seaborn | ||
| Categorical Plots – Statistics within Categories – Understanding Plot Types | ||
| Categorical Plots – Statistics within Categories – Coding with Seaborn | ||
| Categorical Plots – Distributions within Categories – Understanding Plot Types | ||
| Categorical Plots – Distributions within Categories – Coding with Seaborn | ||
| Seaborn – Comparison Plots – Understanding the Plot Types | ||
| Seaborn – Comparison Plots – Coding with Seaborn | ||
| Seaborn Grid Plots | ||
| Seaborn – Matrix Plots | ||
| Seaborn Plot Exercises Overview | ||
| Seaborn Plot Exercises Solutions | ||
| 8. | Data Analysis and Visualization Capstone Project Exercise (01 hour 04 minutes) | Capstone Project Overview |
| Capstone Project Solutions – Part One | ||
| Capstone Project Solutions – Part Two | ||
| Capstone Project Solutions – Part Three | ||
| 9. | Machine Learning Concepts Overview (38 minutes) | Introduction to Machine Learning Overview Section |
| Why Machine Learning? | ||
| Types of Machine Learning Algorithms | ||
| Supervised Machine Learning Process | ||
| Companion Book – Introduction to Statistical Learning | ||
| 10. | Linear Regression (05 hours 05 minutes) | Introduction to Linear Regression Section |
| Linear Regression – Algorithm History | ||
| Linear Regression – Understanding Ordinary Least Squares | ||
| Linear Regression – Cost Functions | ||
| Linear Regression – Gradient Descent | ||
| Python coding Simple Linear Regression | ||
| Overview of Scikit-Learn and Python | ||
| Linear Regression – Scikit-Learn Train Test Split | ||
| Linear Regression – Scikit-Learn Performance Evaluation – Regression | ||
| Linear Regression – Residual Plots | ||
| Linear Regression – Model Deployment and Coefficient Interpretation | ||
| Polynomial Regression – Theory and Motivation | ||
| Polynomial Regression – Creating Polynomial Features | ||
| Polynomial Regression – Training and Evaluation | ||
| Bias Variance Trade-Off | ||
| Polynomial Regression – Choosing Degree of Polynomial | ||
| Polynomial Regression – Model Deployment | ||
| Regularization Overview | ||
| Feature Scaling | ||
| Introduction to Cross Validation | ||
| Regularization Data Setup | ||
| L2 Regularization – Ridge Regression Theory | ||
| L2 Regularization – Ridge Regression – Python Implementation | ||
| L1 Regularization – Lasso Regression – Background and Implementation | ||
| L1 and L2 Regularization – Elastic Net | ||
| Linear Regression Project – Data Overview | ||
| 11. | Feature Engineering and Data Preparation (01 hour 50 minutes) | A note from Jose on Feature Engineering and Data Preparation |
| Introduction to Feature Engineering and Data Preparation | ||
| Dealing with Outliers | ||
| Dealing with Missing Data: Part One – Evaluation of Missing Data | ||
| Dealing with Missing Data: Part Two – Filling or Dropping data based on Rows | ||
| Dealing with Missing Data: Part 3 – Fixing data based on Columns | ||
| Dealing with Categorical Data – Encoding Options | ||
| 12. | Cross Validation, Grid Search, and the Linear Regression Project (01 hour 15 minutes) | Section Overview and Introduction |
| Cross Validation – Test | Train Split | ||
| Cross Validation – Test | Validation | Train Split | ||
| Cross Validation – cross_val_score | ||
| Cross Validation – cross_validate | ||
| Grid Search | ||
| Linear Regression Project Overview | ||
| Linear Regression Project – Solutions | ||
| 13. | Logistic Regression (02 hours 37 minutes) | Early Bird Note on Downloading .zip for Logistic Regression Notes |
| Introduction to Logistic Regression Section | ||
| Logistic Regression – Theory and Intuition – Part One: The Logistic Function | ||
| Logistic Regression – Theory and Intuition – Part Two: Linear to Logistic | ||
| Logistic Regression – Theory and Intuition – Linear to Logistic Math | ||
| Logistic Regression – Theory and Intuition – Best fit with Maximum Likelihood | ||
| Logistic Regression with Scikit-Learn – Part One – EDA | ||
| Logistic Regression with Scikit-Learn – Part Two – Model Training | ||
| Classification Metrics – Confusion Matrix and Accuracy | ||
| Classification Metrics – Precision, Recall, F1-Score | ||
| Classification Metrics – ROC Curves | ||
| Logistic Regression with Scikit-Learn – Part Three – Performance Evaluation | ||
| Multi-Class Classification with Logistic Regression – Part One – Data and EDA | ||
| Multi-Class Classification with Logistic Regression – Part Two – Model | ||
| Logistic Regression Exercise Project Overview | ||
| Logistic Regression Project Exercise – Solutions | ||
| 14. | KNN – K Nearest Neighbors (01 hours 08 minutes) | Introduction to KNN Section |
| KNN Classification – Theory and Intuition | ||
| KNN Coding with Python – Part One | ||
| KNN Coding with Python – Part Two – Choosing K | ||
| KNN Classification Project Exercise Overview | ||
| KNN Classification Project Exercise Solutions | ||
| 15. | Support Vector Machines (01 hours 56 minutes) | Introduction to Support Vector Machines |
| History of Support Vector Machines | ||
| SVM – Theory and Intuition – Hyperplanes and Margins | ||
| SVM – Theory and Intuition – Kernel Intuition | ||
| SVM – Theory and Intuition – Kernel Trick and Mathematics | ||
| SVM with Scikit-Learn and Python – Classification Part One | ||
| SVM with Scikit-Learn and Python – Classification Part Two | ||
| SVM with Scikit-Learn and Python – Regression Tasks | ||
| Support Vector Machine Project Overview | ||
| Support Vector Machine Project Solutions | ||
| 16. | Tree-Based Methods: Decision Tree Learning (01 hour 21 minutes) | Introduction to Tree-Based Methods |
| Decision Tree – History | ||
| Decision Tree – Terminology | ||
| Decision Tree – Understanding Gini Impurity | ||
| Constructing Decision Trees with Gini Impurity – Part One | ||
| Constructing Decision Trees with Gini Impurity – Part Two | ||
| Coding Decision Trees – Part One – The Data | ||
| Coding Decision Trees – Part Two -Creating the Model | ||
| 17. | Random Forests (01 hours 53 minutes) | Introduction to Random Forests Section |
| Random Forests – History and Motivation | ||
| Random Forests – Key Hyperparameters | ||
| Random Forests – Number of Estimators and Features in Subsets | ||
| Random Forests – Bootstrapping and Out-of-Bag Error | ||
| Coding Classification with Random Forest Classifier – Part One | ||
| Coding Classification with Random Forest Classifier – Part Two | ||
| Coding Regression with Random Forest Regressor – Part One – Data | ||
| Coding Regression with Random Forest Regressor – Part Two – Basic Models | ||
| Coding Regression with Random Forest Regressor – Part Three – Polynomials | ||
| Coding Regression with Random Forest Regressor – Part Four – Advanced Models | ||
| 18. | Boosting Methods (01 hour 20 minutes) | Introduction to Boosting Section |
| Boosting Methods – Motivation and History | ||
| AdaBoost Theory and Intuition | ||
| AdaBoost Coding Part One – The Data | ||
| AdaBoost Coding Part Two – The Model | ||
| Gradient Boosting Theory | ||
| Gradient Boosting Coding Walkthrough | ||
| 19. | Supervised Learning Capstone Project – Cohort Analysis and Tree-Based Methods (01 hour 17 minutes) | Introduction to Supervised Learning Capstone Project |
| Solution Walkthrough – Supervised Learning Project – Data and EDA | ||
| Solution Walkthrough – Supervised Learning Project – Cohort Analysis | ||
| Solution Walkthrough – Supervised Learning Project – Tree Models | ||
| 20. | Naive Bayes Classification and Natural Language Processing (Supervised Learning) (01 hour 51 minutes) | Introduction to NLP and Naive Bayes Section |
| Naive Bayes Algorithm – Part One – Bayes Theorem | ||
| Naive Bayes Algorithm – Part Two – Model Algorithm | ||
| Feature Extraction from Text – Part One – Theory and Intuition | ||
| Feature Extraction from Text – Coding Count Vectorization Manually | ||
| Feature Extraction from Text – Coding with Scikit-Learn | ||
| Natural Language Processing – Classification of Text – Part One | ||
| Natural Language Processing – Classification of Text – Part Two | ||
| Text Classification Project Exercise Overview | ||
| Text Classification Project Exercise Solutions | ||
| 21. | Unsupervised Learning (08 minutes 17 seconds) | Unsupervised Learning Overview |
| 22. | K-Means Clustering (02 hours 29 minutes) | Introduction to K-Means Clustering Section |
| Clustering General Overview | ||
| K-Means Clustering Theory | ||
| K-Means Clustering – Coding Part One | ||
| K-Means Clustering Coding Part Two | ||
| K-Means Clustering Coding Part Three | ||
| K-Means Color Quantization – Part One | ||
| K-Means Color Quantization – Part Two | ||
| K-Means Clustering Exercise Overview | ||
| K-Means Clustering Exercise Solution – Part One | ||
| K-Means Clustering Exercise Solution – Part Two | ||
| K-Means Clustering Exercise Solution – Part Three | ||
| 23. | Hierarchical Clustering (57 minutes) | Introduction to Hierarchical Clustering |
| Hierarchical Clustering – Theory and Intuition | ||
| Hierarchical Clustering – Coding Part One – Data and Visualization | ||
| Hierarchical Clustering – Coding Part Two – Scikit-Learn | ||
| 24. | DBSCAN – Density-based spatial clustering of applications with noise (01 hour 29 minutes) | Introduction to DBSCAN Section |
| DBSCAN – Theory and Intuition | ||
| DBSCAN versus K-Means Clustering | ||
| DBSCAN – Hyperparameter Theory | ||
| DBSCAN – Hyperparameter Tuning Methods | ||
| DBSCAN – Outlier Project Exercise Overview | ||
| DBSCAN – Outlier Project Exercise Solutions | ||
| 25. | PCA – Principal Component Analysis and Manifold Learning (01 hours 19 minutes) | Introduction to Principal Component Analysis |
| PCA Theory and Intuition – Part One | ||
| PCA Theory and Intuition – Part Two | ||
| PCA – Manual Implementation in Python | ||
| PCA – SciKit-Learn | ||
| PCA – Project Exercise Overview | ||
| PCA – Project Exercise Solution | ||
| 26. | Model Deployment (01 hour 02 minutes) | Model Deployment Section Overview |
| Model Deployment Considerations | ||
| Model Persistence | ||
| Model Deployment as an API – General Overview | ||
| Note on Upcoming Video | ||
| Model API – Creating the Script | ||
| Testing the API |
Resources Required
- Basic Python Knowledge (capable of functions)
Featured Review
Eric E.: Jose is a pro, and has perfected online teaching. I’ve taken basically all his courses, and I am never disappointed. He is thorough, clear, and concise in his teaching and this course is no different. Every Python course he puts out on Udemy, I will continue to take! Thanks, Eric
Pros
- Washington Alto: This online course is one of the best if not the best course that I’ve come across.
- Jae Doo: Going through this amount of ML concepts is awesome considering the quality and practicality of this course!
- Mahesh Tanpure: Best course for all levels of students who get started with ML.
- Boho Ning: This course is absolutely wonderful to get into the field of data science.
Cons
- Bhagyesha Anant Khairnar: During enviroment setup, I followed the steps and still was unable to install the panda’s module, I had to separately import the module and that too of version 1.4.2
- Prashant Deshmukh: I think there are a lot of fake reviews for this course maybe, as this is not a good course if you do not know MATHS required for Machine learning.
About the Author
The instructor of this course is Jose Portilla who is a Head of Data Science at Pierian Training. With a 4.6 Instructor Rating and 954,437 Reviews on Udemy, he/she offers 54 Courses and has taught 3,052,886 Students so far. Jose Marcial Portilla holds degrees in mechanical engineering from Santa Clara University (BS and MS), and he has years of experience working as a qualified instructor and trainer for Python programming, machine learning, and data science. He has written articles and received patents in a number of disciplines, including data science, materials science, and microfluidics. He has acquired a set of abilities for data analysis throughout the course of his career, and he wants to combine both his teaching and data science knowledge to educate others on the power of programming, how to analyze data, and how to display the data in attractive visualizations.
Comparison Table
| Parameters | Python for Machine Learning & Data Science Masterclass | Deep Learning: Recurrent Neural Networks in Python | Deep Learning: Convolutional Neural Networks in Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 44 hours | 12.5 hours | 12.5 hours |
| Rating | 4.6/5 | 4.6/5 | 4.5/5 |
| Student Enrollments | 73,716 | 30,088 | 32,219 |
| Instructors | Jose Portilla | Lazy Programmer Inc. | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Python for Machine Learning & Data Science Masterclass: FAQs
Ques. Which Python course is best for machine learning?
Ans. Machine Learning: DeepLearning.AI. – Applied Data Science with Python: University of Michigan. – Python for Data Science, AI & Development: IBM Skills Network. – Machine Learning with Python: IBM Skills Network.
Ques. Who is Jose Portilla?
Ans. He currently serves as the Head of Data Science for Pierian Training, where he trains people at prestigious organizations like General Electric, Cigna, The New York Times, Credit Suisse, McKinsey, and others in data science and python programming on-site.
Ques. How much does it cost to learn Python for Data Science?
Ans. Generally, the fee for python certification courses in India can range from 20,000 INR to 50,000 INR.
Ques. How to get a certificate from Udemy?
Ans. You can get a certificate of completion from Udemy after you complete a paid course. Once all of the course modules are completed, the trophy icon on the top right corner of the course preview window will change color. You can click on the trophy icon and click on the download icon to download the certificate in .pdf or .jpg format.
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Ans. The steps to add the Udemy certificate to LinkedIn are mentioned below.
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- Add credential URL after credential ID. You can find the credential URL just below the credential number. Make sure to only copy the content after ‘udemy’.
- Save the changes.
Ques. Why Udemy course price change?
Ans. Udemy course price keeps changing to reach a wider audience. As most of the buyers are students, who cannot afford to pay the full price hence Udemy offers heavy discounts on the courses and keeps them changing over time.
Ques. How do return courses on Udemy?
Ans. You can return the courses on Udemy from the purchase history.
- Click on the purchase history option and click on the course that you want to return.
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- Select the refund method. If the transaction is eligible for a refund to the original payment method then choose that or else you can request a refund for Udemy credits too.
- Submit a valid reason for requesting a refund. Click on the submit button.
Ques. How to get an Udemy discount?
Ans. If you are a first-time user, then you can get any Udemy courses for just INR 455. For others, Udemy offers heavy discounts every now and then. They can check the official website for updates about sales and discounts.
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Ans. Yes, Udemy certificates demonstrate your accomplishments to potential recruiters or employers. However, Udemy is not an accredited institution so Udemy certificates cannot be used for formal accreditation.
Ques. Do Udemy courses expire?
Ans. No, paid Udemy courses have lifetime access provided you have an active Udemy account and Udemy continues to have the license for the course.
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