Machine Learning & AI - For Beginners
Master AI & ML from scratch through beginner-friendly lessons. Learn key concepts like Linear Regression, CV, RL, and ML algorithms with hands-on formulas and real-world examples. Perfect for non-tech learners exploring data science and practical machine learning applications.
Ayush Kumar Singh
Instructor
Included with Data Science & ML
Here’s what you will learn?
- Understand what AI, ML, and Deep Learning are — and how they differ
- Build strong foundational knowledge in Linear Regression, Cost Functions, and Prediction Models
- Learn and explain key ML algorithms like Decision Trees, Random Forest, SVM, Clustering, and more
- Understand R-Square, Least Squares, and how models evaluate performance
- Perform hands-on formula-based calculations used in machine learning manually
- Explore career opportunities and real-world applications of AI/ML in business, healthcare, marketing, etc.
- Gain the ability to understand ML models conceptually before jumping into code
- Be ready to take on more advanced AI/ML courses, certifications, and job interviews
This Course Includes
- Recorded Lessons: 7
- Recorded Hours: 1hr 27min
- Duration: 10 days (Avg)
- TD Assessment Available
- Access on Mobile
Course Description
Master AI & ML from scratch through beginner-friendly lessons. Learn key concepts like Linear Regression, CV, RL, and ML algorithms with hands-on formulas and real-world examples. Perfect for non-tech learners exploring data science and practical machine learning applications.
Course Content
7 Lessons | 1hr 27min | 1 Materials for reading
Artificial Intelligence (AI) is the broad science of making machines smart. Machine Learning (ML) is a subset of AI where machines learn from data. Deep Learning (DL) is a subset of ML that uses neural networks to mimic how the human brain learns—great for processing images, speech, and complex patterns.
Computer Vision
Computer Vision is a field of AI that enables machines to interpret and understand visual information from the world—like images and videos. It's used in face recognition, object detection, self-driving cars, and more.
Reinforcement Learning
Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by interacting with its environment. It improves through trial and error, aiming to maximize rewards—like how games or robots learn to win or complete tasks.
Data Science is the process of collecting, analyzing, and interpreting large amounts of data to extract meaningful insights. It combines statistics, computer science, and domain knowledge to solve real-world problems and support decision-making.
Linear Regression – Short Summary
Linear Regression is a supervised machine learning algorithm used to model the relationship between a dependent variable and one or more independent variables. It fits a straight line (best fit line) to the data points to make predictions.
Independent & Dependent Variables – Short Summary
Independent Variables (Input): The factors or features that influence the outcome (e.g., hours studied).
Dependent Variable (Output): The result or target we want to predict (e.g., exam score).
In regression, we try to understand how changes in independent variables affect the dependent variable.
Frequently Asked Questions
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