machine learning

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 HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration44 Hours
Student Enrollment73,716 students
InstructorJose Portilla
Topics Covered
  • Programming with Python,
  • Full understanding of Matplotlib Programming Library,
  • Deep dive into seaborn for data visualizations.
Course LevelIntermediate
Total Student Reviews9,491

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!
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


  • 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.


  • 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

ParametersPython for Machine Learning & Data Science MasterclassDeep Learning: Recurrent Neural Networks in PythonDeep Learning: Convolutional Neural Networks in Python
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration44 hours12.5 hours12.5 hours
Student Enrollments73,71630,08832,219
InstructorsJose PortillaLazy Programmer Inc.Lazy Programmer Inc.
Register HereApply 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.

Ques. How to add the Udemy certificate to LinkedIn?

Ans. The steps to add the Udemy certificate to LinkedIn are mentioned below.

  • Go to your LinkedIn profile and click the ‘Add Profile Section’ option. From the drop-down menu select the ‘Licenses and Certificates’ option.
  • Click on the ‘+’ (plus) icon to add a new certificate.
  • Enter the name of the course and Udemy as issuing organization in the pop-up box.
  • Add the certificate number in the credential ID option. The credential ID is the certificate number found at the bottom left corner of your Udemy certificate.
  • 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.
  • Click on the ‘request a refund’ option just below the title of the course.
  • 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.

Ques. How to get coupons for Udemy?

Ans. You can get Udemy coupon codes either from Udemy’s official website directly or through various coupon listing pages. Udemy’s official website features multiple coupon codes during seasonal sales but for that, you need to check out the website every now and then. Coupon listing pages are another way to claim Udemy coupon codes but often they are either backdated or not applicable to Udemy’s courses.

Ques. How to get free coupons for Udemy?

Ans. You can check the coupon listing pages that offer Udemy coupons for free of cost. To check the authenticity of the coupon you can copy & paste the code and add it to your cart before checking out.

Ques. When will Udemy courses go on sale?

Ans. There is no time or event on which the Udemy courses go on sale. Udemy courses are subject to discounts & sales throughout the year.

Ques. Is the Udemy certificate valid?

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.

Leave feedback about this

  • Rating