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 beginnerfriendly. 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 

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 Scikitlearn.
 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 machinelearning 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 Checkin: Creating NumPy Arrays  
NumPy Indexing and Selection  
Coding Exercise Checkin: Selecting Data from Numpy Array  
NumPy Operations  
CheckIn: Operations on NumPy Array  
NumPy Exercises  
Numpy Exercises – Solutions  
5.  Pandas (06 hours 26 minutes)  Introduction to Pandas 
Series – Part One  
Checkin: 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 ScikitLearn and Python  
Linear Regression – ScikitLearn Train Test Split  
Linear Regression – ScikitLearn 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 TradeOff  
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 ScikitLearn – Part One – EDA  
Logistic Regression with ScikitLearn – Part Two – Model Training  
Classification Metrics – Confusion Matrix and Accuracy  
Classification Metrics – Precision, Recall, F1Score  
Classification Metrics – ROC Curves  
Logistic Regression with ScikitLearn – Part Three – Performance Evaluation  
MultiClass Classification with Logistic Regression – Part One – Data and EDA  
MultiClass 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 ScikitLearn and Python – Classification Part One  
SVM with ScikitLearn and Python – Classification Part Two  
SVM with ScikitLearn and Python – Regression Tasks  
Support Vector Machine Project Overview  
Support Vector Machine Project Solutions  
16.  TreeBased Methods: Decision Tree Learning (01 hour 21 minutes)  Introduction to TreeBased 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 OutofBag 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 TreeBased 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 ScikitLearn  
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.  KMeans Clustering (02 hours 29 minutes)  Introduction to KMeans Clustering Section 
Clustering General Overview  
KMeans Clustering Theory  
KMeans Clustering – Coding Part One  
KMeans Clustering Coding Part Two  
KMeans Clustering Coding Part Three  
KMeans Color Quantization – Part One  
KMeans Color Quantization – Part Two  
KMeans Clustering Exercise Overview  
KMeans Clustering Exercise Solution – Part One  
KMeans Clustering Exercise Solution – Part Two  
KMeans 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 – ScikitLearn  
24.  DBSCAN – Densitybased spatial clustering of applications with noise (01 hour 29 minutes)  Introduction to DBSCAN Section 
DBSCAN – Theory and Intuition  
DBSCAN versus KMeans 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 – SciKitLearn  
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 onsite.
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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 Click on the ‘+’ (plus) icon to add a new certificate.
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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.
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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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 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 firsttime 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.
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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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