Machine Learning Course
Machine Learning Course by KHOJ Evolutions is a free, beginner-level online course in English with 7 modules, 21 lessons and about 3 hours of video. It includes a certificate of completion.
Machine Learning is a core field of Artificial Intelligence that enables computers to learn patterns from data and make predictions or decisions. This course provides a practical, hands-on introduction to Machine Learning, covering supervised and unsupervised learning, regression, classification, decision trees, model evaluation, and essential machine learning techniques. Through practical examples and real-world datasets, you'll learn how to prepare data, train machine learning models, evaluate their performance, visualize results, and apply appropriate algorithms to solve real-world problems. Whether you're a student, aspiring data scientist, developer, or AI enthusiast, this course will help you build a strong foundation in Machine Learning and develop the practical skills needed to work with machine learning models.
At a glance
- Price — Free
- Level — Beginner
- Language — English
- Format — Online, self-paced video lessons
- Length — 7 modules, 21 lessons, about 3 hours of video
- Certificate — Yes, certificate of completion
- Category — Machine Learning
- Provider — KHOJ Evolutions (khojs.com)
What you will learn
- Understand the fundamentals of Machine Learning and how machine learning systems work.
- Explain the difference between supervised and unsupervised learning.
- Understand regression and classification problems.
- Prepare and preprocess datasets for machine learning.
- Work with features, target variables, training data, and testing data.
- Build and train Machine Learning models using Python and Scikit-learn.
- Understand and implement algorithms such as Linear Regression, Logistic Regression, Decision Trees, and other essential ML techniques.
- Understand concepts such as entropy, information gain, and Gini impurity in Decision Trees.
- Visualize and interpret machine learning models and their predictions.
- Evaluate classification models using accuracy and confusion matrices.
- Understand model performance and select appropriate evaluation techniques.
- Apply Machine Learning algorithms to real-world datasets and practical problems.
Course syllabus
- Machine Learning
- Decision Trees
- K-Nearest Neighbors
- Linear Regression
- Classification & Logistic Regression
- Model Optimization & Validation
- Random Forest & Ensemble Learning
Machine Learning Skills You Will Build
You will learn core machine learning ideas, supervised and unsupervised learning, model evaluation, and a practical workflow for applying algorithms to datasets. A Python foundation helps, and the lessons start from the basics.
Who Is This Machine Learning Training For?
The course is for students and professionals building machine learning skills step by step. A certificate is listed on this page only when the course includes one.
Prerequisites
- Understand the fundamentals of Machine Learning and how machine learning systems work.
- Explain the difference between supervised and unsupervised learning.
- Understand regression and classification problems.
- Prepare and preprocess datasets for machine learning.
- Work with features, target variables, training data, and testing data.
- Build and train Machine Learning models using Python and Scikit-learn.
- Understand and implement algorithms such as Linear Regression, Logistic Regression, Decision Trees, and other essential ML techniques.
- Understand concepts such as entropy, information gain, and Gini impurity in Decision Trees.
- Visualize and interpret machine learning models and their predictions.
- Evaluate classification models using accuracy and confusion matrices.
- Understand model performance and select appropriate evaluation techniques.
- Apply Machine Learning algorithms to real-world datasets and practical problems.
Who this course is for
- Students,
- Beginners,
- Aspiring Data Scientists,
- Aspiring Machine Learning Engineers, Python Developers, AI Enthusiasts
Topics covered
Machine Learning, Python, Scikit-learn, Artificial Intelligence, Supervised Learning, Unsupervised Learning, Regression, Classification, Decision Trees, Data Science.
Frequently asked questions
Is the Machine Learning Course on Khoj free?
Yes. The Machine Learning Course is free on Khoj, so you can enroll without a course fee.
Do I get a certificate for the Machine Learning Course?
Yes. The Machine Learning Course includes a certificate of completion from KHOJ Evolutions when you finish the course requirements.
How long is the Machine Learning Course?
The course has 7 modules and 21 lessons with about 3 hours of video. It is self-paced, so you can learn on your own schedule.
What do I need before starting the Machine Learning Course?
Prerequisites: Understand the fundamentals of Machine Learning and how machine learning systems work, Explain the difference between supervised and unsupervised learning, Understand regression and classification problems, Prepare and preprocess datasets for machine learning, Work with features, target variables, training data, and testing data, Build and train Machine Learning models using Python and Scikit-learn, Understand and implement algorithms such as Linear Regression, Logistic Regression, Decision Trees, and other essential ML techniques, Understand concepts such as entropy, information gain, and Gini impurity in Decision Trees, Visualize and interpret machine learning models and their predictions, Evaluate classification models using accuracy and confusion matrices, Understand model performance and select appropriate evaluation techniques and Apply Machine Learning algorithms to real-world datasets and practical problems.
Who is the Machine Learning Course for?
This course is for: Students,, Beginners,, Aspiring Data Scientists, and Aspiring Machine Learning Engineers, Python Developers, AI Enthusiasts.
How long do I have access after I enroll?
Once you enroll, you have lifetime access. You can learn at your own pace and revisit lessons later.
