What tools are used to build the first machine learning models in this course?

The course uses Python, Jupyter Notebooks, virtual environments, and Scikit-learn to build and test models.

What machine learning projects are covered in the course?

You will work with diabetes and pumpkin datasets to practice linear regression, polynomial regression, data cleaning, visualization, and classification.

How does the course explain logistic regression and ROC curves?

It shows how logistic regression classifies data, prepares categorical features, evaluates imbalanced datasets, and uses ROC curves to assess model performance.

Free Course Image Machine Learning for complete beginners

Free online course Machine Learning for complete beginners

Duration of the online course: 1 hours and 9 minutes

4.6

(5)

Build real machine learning skills from scratch in a free online course: set up Python, clean data, visualize insights, and train regression models.

In this free course, learn about

What machine learning is and the course’s main focus and goals
Key milestones in ML/AI history, including the 1956 start of the AI field
Core ML techniques and the first step in starting an ML project
How to set up Python tooling (easy-start options) and Jupyter notebooks for ML
Why virtual environments are used to manage dependencies and isolate projects
Regression basics and which regression type is used for predicting categories
Building a first linear regression model in Python on the diabetes dataset
How to analyze and clean datasets (e.g., pumpkin data) to prepare for modeling
Data visualization with Matplotlib to find patterns (e.g., cheapest pumpkin month)
Purpose of linear regression and how correlation supports linear modeling
Linear vs polynomial regression in scikit-learn and limits of linear models on some data
Encoding categorical features into numbers for regression and classification
Logistic regression fundamentals and preparing data for classification tasks
Evaluating classifiers with imbalance metrics and ROC curves (tradeoffs across thresholds)

About the free online course

If you are curious about machine learning but feel blocked by jargon, this course is designed to get you moving fast, with clarity and confidence. You will start with a beginner-friendly explanation of what machine learning is, why it matters, and how it connects to the broader story of artificial intelligence. Instead of assuming a technical background, the lessons build intuition first, helping you recognize what problems ML can solve and what it cannot.

From there, you will shift into hands-on practice in Python with an environment that makes experimentation easy. You will learn how to prepare your tools, work comfortably in notebooks, and create a clean workflow so your projects stay organized. Just as important, you will develop the habit of thinking like a practitioner: define the goal, look at the data, and decide what approach fits before writing code.

As you work with real datasets, you will practice the steps that often determine whether a model succeeds: analyzing what the data actually contains, cleaning messy values, and making information visible through simple, effective visualizations. This approach helps you understand patterns instead of blindly training models, and it prepares you for everyday ML work where data quality and interpretation are everything.

You will then progress through core regression techniques, gaining a practical understanding of linear regression, correlation, and the tradeoffs that appear when the relationship between variables is not perfectly linear. You will also see how to move beyond basic assumptions using polynomial regression, and how to handle categories in a way that models can learn from. Along the way, you will rely on widely used libraries such as scikit-learn to implement models with best practices.

Finally, you will learn how classification differs from prediction of numeric values by stepping into logistic regression. You will prepare data for classification, understand why evaluation can be tricky with imbalanced datasets, and use tools like ROC curves to interpret model performance more thoughtfully. By the end, you will be able to follow the full beginner workflow: set up your environment, explore and prepare data, choose an approach, train a model, and evaluate results with confidence.

Course content

Video class: Introduction to Machine Learning for Beginners [Part 1] | Machine Learning for Beginners 03m
Exercise: What is the primary focus of the course discussed in the video?
Video class: The history of Machine Learning [Part 2] | Machine Learning for Beginners 04m
Exercise: What marked the beginning of the AI field in 1956?
Video class: Techniques for Machine Learning [Part 3] | Machine Learning for Beginners 04m
Exercise: What is the first step in starting a machine learning project?

Video class: Setup your tools ready to build Machine Learning models [Part 4] | Machine Learning for Beginners 04m
Exercise: What is the easiest way to start using Python for ML with minimal setup?
Video class: Introduction to Regression models for Machine Learning [Part 5] | Machine Learning for Beginners 03m
Exercise: Which type of regression is suitable for predicting a category?
Video class: Set up Jupyter Notebooks to start building regression models [Pt 6] | Machine Learning for Beginners 05m
Exercise: What is the primary purpose of using a virtual environment in the video tutorial?

Video class: Your First Linear Regression Project in Python [Part 7] | Machine Learning for Beginners 04m
Exercise: What is used to predict disease progression in the diabetes dataset using linear regression?
Video class: How to Analyze and Clean a Dataset [Part 8] | Machine Learning for Beginners 03m
Exercise: What is the main goal when analyzing and cleaning the pumpkin data set?
Video class: How to Visualize Data with Matplotlib [Part 9] | Machine Learning for Beginners 03m
Exercise: Which month is the cheapest to buy pumpkins according to the data visualization?

Video class: Understanding Linear Regression [Part 10] | Machine Learning for Beginners 02m
Exercise: What is the purpose of linear regression in data analysis?
Video class: Looking for Correlation: The Key to Linear Regression [Part 11] | Machine Learning for Beginners 03m
Exercise: What does a high positive correlation indicate between two variables?
Video class: Linear and Polynomial Regression using Scikit-learn [Part 12] | Machine Learning for Beginners 05m
Exercise: What is a limitation of using linear regression with the pumpkin dataset?
Video class: Categorical Feature Predictions with Linear Regression [Part 13] | Machine Learning for Beginners 03m
Exercise: What method is suggested for encoding categorical data into numerical values for regression models?

Video class: Understanding Logistic Regression for Machine Learning Classification [Part 14] | ML for Beginners 03m
Exercise: What is the primary purpose of logistic regression?
Video class: Data Analysis and Preparation for Logistic Regression [Part 15] | Machine Learning for Beginners 05m
Exercise: Which transformation method is used for converting pumpkin size categories into numerical values?
Video class: Logistic Regression for classification of data [Part 16] | Machine Learning for Beginners 04m
Exercise: What metric is often used when training models with unbalanced data?
Video class: Analyzing Logistic Regression Performance with ROC Curves [Part 17] | Machine Learning for Beginners 04m
Exercise: What does the ROC curve help analyze in a logistic regression model?

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