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Python Machine Learning & Data Science with Scikit-learn

Name: Python Machine Learning & Data Science with Scikit-learn
Rating: 4.6 (106 reviews)

Build real-world machine learning models in Python using Scikit-Learn — from beginner to advanced techniques in AI

Created byTM Quest

Last updated 10/2025

English

What you'll learn

Understand the fundamental concepts in machine learning.
Use Python and Scikit-Learn to create machine learning models.
Make data ready for machine learning algorithms to use.
Critically examine machine learning models and algorithms.
Use regression models to predict continuous values.
Use classification models to predict classes.
Use unsupervised algorithms to cluster data.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

19 sections • 109 lectures • 9h 4m total length

Introduction to the Course3:12
In this video, you will get a quick introduction to the course and who we are.
All the material in the course!0:11
(Background) Introduction to Jupyter Notebooks8:27
In this video, you will learn how to use Jupyter Notebooks. We will use this environment for the rest of the course.
(Background) Introduction to NumPy11:30
In this video, you will learn how to work with vectors and matrices in NumPy.
(Background) Introduction to Pandas12:35
In this video, you will learn how to work with tabular data by using Pandas.

What is Machine Learning?4:49
In this video, you will learn why machine learning is useful for solving certain problems!
ML Terminology6:27
In this video, you will learn some terminology that is useful when dealing with machine learning. The most important distinction is between supervised vs. unsupervised machine learning and classification vs. regression problems.
Basic ML Quiz
Anatomy of a ML Project9:02
In this video, you will learn how a machine learning project is structured. I will briefly show you the different major stages of a machine learning project so that you get a good overview.
The Steps in ML
Introducing Scikit Learn8:16
In this video, I will show you how to navigate around the Scikit-Learn homepage. After that, we will look at how to import machine learning models.
Importing a Machine Learning Model
Exploring the Diabetes Dataset11:45
In this video, we will explore the diabetes dataset and do some simple data exploration.
Further Resources0:20

Introduction1:23
This video is a short overview of what we are going to do in this module.
Idea of Linear Regression2:20
In this video you are going to learn the idea behind linear regression.
The Theory of Linear Regression5:55
In this video you are going to learn the theory behind linear regression.
Linear Regression
Linear Regression in Scikit-Learn3:58
In this video, we will walk through how to initialize and train a linear regression model.
Your First Linear Regression Model!
Evaluating the Model4:36
In this video, you will learn about mean-squared-error (MSE) and why one should divide the dataset into training and testing sets.
Evaluating the Model
Is our Model any Good?8:36
In this video, you will see how to evaluate a model using the mean square error.
Splitting the Data into Training and Testing Sets
Evaluating using MSE
How is Training Done? (Optional Theory)19:10
In this optional theory video, you will get some insight into how linear regression works under the hood.
Further Resources0:16

Introduction1:29
In this intro video, we will tell you a bit about what to expect in this module.
Binary Classification and Logistic Regression7:07
In this video, you will learn what binary classification and logistic regression are from a high-level standpoint.
Binary Classification and Logistic Regression
The Iris Dataset9:40
In this video, you will get familiar with the Iris dataset and get ready to apply logistic regression in the next video.
Implementing Logistic Regression7:09
In this video, you will implement a logistic regression model for binary classification.
Implementing Logistic Regression
Accuracy Score5:00
In this video, you will understand the accuracy score as a metric for binary classification algorithms.
Accuracy Score
Predictors and Accuracy Score
Further Resources0:17

Introduction1:14
We now start with learning about preprocessing and pipelines in scikit-learn.
Preprocessing7:40
In this video, you will learn the basics of preprocessing and the role it plays in a machine learning project.
Preprocessing
Filling in Missing Values9:24
In this video, you will work with the titanic dataset and handle missing values.
Filling in Missing Values
Choosing Relevant Features7:33
In this video, you will remove duplicate features and encode columns to make them easier to work with.
Standard Scaling in Scikit-Learn8:48
In this video, you will learn how to apply standard scaling to a dataset.
Standard Scaling
Pipelines6:38
In this video, you will learn what pipelines are and how to implement them in scikit-learn.
Pipelines
Further Resources0:21

Introduction1:45
In this video, we will outline what we will go through for polynomial regression and overfitting.
Understanding Polynomial Regression8:06
In this video, you will get a conceptual understanding of polynomial features and polynomial regression.
Polynomial Regression
Adding Polynomial Features Manually6:40
In this video, you will learn how to add polynomial features manually in Pandas. While this is one solution, we will learn a better way later.
Adding Polynomial Features
Evaluating with Mean Absolute Error9:39
In this video, you will learn what the mean absolute error is and how it differs from the mean squared error. We will also train a polynomial regression model and check how well it performs with respect to the mean absolute error.
Mean Absolute Error
Using the Polynomial Features Class6:16
In this video, you will learn how to use the built-in PolynomialFeatures class in scikit-learn to create polynomial features.
Adding Polynomial Features Properly
Fitting Everything Into a Pipeline6:06
In this video, you will get some practice with fitting what you have done into a pipeline.
Overfitting and Underfitting5:43
In this video, you will learn what the terms overfitting and underfitting mean. In the next video, we will look at overfitting in practice in scikit-learn.
Overfitting and Underfitting
Overfitting in Practice6:02
In this video, you will get some exposure to overfitting in practice in scikit-learn.
Further Resources0:15

Introduction0:52
In this video, I will give an introduction to what you will learn in this module.
Introduction to Trees3:08
In this video, you will learn basic definitions about trees.
Decision Trees4:29
In this video, you will learn about decision trees and how they predict on a new observation.
Trees and Decision Trees
Implementing Decision Trees6:20
In this video, you will learn how to implement decision trees for classification in scikit-learn.
Decision Trees for Regression
False Positives and False Negatives3:44
In this video, you will learn what false positives and false negatives are.
Understanding Precision and Recall4:07
In this video, you will learn what the two metrics precision and recall are, and why you might use one over the other.
Unbalanced Datasets
Using Precision and Recall6:37
In this video, you will learn how to implement precision and recall in scikit-learn.
Finding Precision of a Decision Tree
Further Resources0:18

Introduction0:57
In this video, you will get an overview for what we will cover in this section.
What is Ensemble Learning?4:43
In this video, you will learn what ensemble methods are and why they are useful.
Ensemble Learning
Creating Multiple Models Fast4:20
In this video, you will learn how to use list comprehensions to create several models fast.
Creating an Ensemble Majority Vote7:02
In this video, you will learn how to use a Voting Classifier in scikit-learn.
Training and Fitting Multiple Models
Weak Learners and Bagging5:45
In this video, you will learn what weak learners are, and how decision trees are weak learners that can be combined through a procedure called bagging to form random forests.
Weak Learners and Bagging
Using Random Forests6:52
In this video, you will learn how to implement random forests in scikit-learn.
Random Forests
Further Resources0:20

Introduction1:20
In this video, you will learn what we will go through in this module.
One Hot Encoding2:59
In this video, you will learn how one hot encoding works in theory.
One Hot Encoding
Using One Hot Encoding8:23
In this video, you will learn how to implement one hot encoding in scikit-learn.
Using One Hot Encoding
Cross-Validation2:08
In this video, you will learn about cross-validation and how this helps with not throwing away the testing set.
Using Cross-Validation5:51
In this video, you will learn how to implement cross-validation in scikit-learn.
Using Cross-Validation
Validation and Test Set2:11
In this video, you will learn what validation sets are, and how you can use both validation sets and test sets for evaluating models.
Validation, Test Set, and Cross-Validation
One Hot Encoding and Pipelines6:59
In this video, you will learn how to put one hot encoding into a pipeline by using a ColumnTransformer.
Cross-Validation and Pipelines5:05
In this video, you will learn how to use cross-validation with pipelines.
Cross-Validation and Pipelines
Further Resources0:19

Requirements

Some basic knowledge of Python

Description

Ready to master machine learning in Python and launch your career in data science? This hands-on, comprehensive course is the definitive guide to becoming a skilled practitioner, taking you from the fundamentals of Scikit-learn to building powerful, real-world AI models.

You'll gain a deep understanding of Scikit-learn, Python's most essential and widely used machine learning library. By focusing on practical application, you will not only learn the algorithms but also how to implement the full data science workflow—a critical skill for employers.

Master the Complete Data Science and Machine Learning Workflow

This masterclass will teach you to:

Prepare and Preprocess complex, real-world datasets using Python (Pandas & NumPy) and the integrated tools within Scikit-learn.
Build Powerful Models using core Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVMs).
Optimize Performance with advanced techniques like Regularization, Cross-Validation, and Principal Component Analysis (PCA) for Dimensionality Reduction.
Apply both Supervised and Unsupervised Learning to solve diverse business problems in data science.
Understand the AI Landscape by covering the basics of Neural Networks and their role in Deep Learning.
Work through short coding exercises and large, project-style assignments, mirroring the daily work of a professional data scientist.

Why Learn Machine Learning with Us?

We're Eirik and Stine, a professional data scientist and a university-level programming instructor who love making complex AI topics accessible. We blend practical, real-world experience with top-tier teaching methods. You won't just memorize formulas; you'll build intuition and problem-solving skills, making your transition into a Machine Learning or AI role seamless.

Who This Course Is For

This course is perfectly suited for:

Beginners who want a fast, hands-on path into machine learning and data science.
Python Programmers or Data Analysts who want to master Scikit-learn and predictive modeling for career growth.
Students and Researchers looking to add powerful ML and AI techniques to their academic toolkit.

No extensive prior experience is required! A basic grasp of Python programming will get you started!

Start your AI Journey Today

You're covered by Udemy's 30-day money-back guarantee, so you can explore the course risk-free. Enroll now and take the definitive step toward becoming a confident machine learning and data science practitioner!

Who this course is for:

Anyone who wants to break into machine learning and data science
Beginner Python developers curious about machine learning

Python Machine Learning & Data Science with Scikit-learn

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction and Overview5 lectures • 36min

What is Machine Learning?6 lectures • 41min

Our First Model: Linear Regression8 lectures • 46min

Binary Classification with Logistic Regression6 lectures • 31min

Preprocessing and Pipelines7 lectures • 42min

Polynomial Regression and Overfitting9 lectures • 51min

Project 1 - Regression2 lectures • 16min

Decision Trees and Different Metrics8 lectures • 30min

Ensemble Learning and Random Forests7 lectures • 30min

One-Hot-Encoding and Cross-Validation9 lectures • 35min

Requirements

Description

Who this course is for: