What you will learn

• Supervised learning
• Classification
• Clustering
• Neural networks

Course content (21 lessons)

1. 1. Introduction to Machine Learning — Welcome to Machine Learning Machine learning (ML) is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us…
2. 2. Introduction to Data Science — What is Data Science? Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy,…
3. 3. Exploratory Data Analysis (EDA) — What is EDA? Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It is the critical "first…
4. 4. Data Visualization — The Power of Visualization A picture is worth a thousand rows of data. Data visualization is the graphical representation of information and data. By using visual elements like…
5. 5. Data Engineering & Preprocessing — What is Data Preprocessing? Real-world data is dirty. It is often incomplete, inconsistent, lacking in certain behaviors or trends, and is likely to contain many errors. Data…
6. 6. Web Scraping — What is Web Scraping? Web scraping is the automated process of extracting information from websites. It turns unstructured data on the web (HTML) into structured data (like a…
7. 7. Supervised Learning – Regression — What is Regression? Regression analysis is a set of statistical processes for estimating the relationships among variables. In ML, it is used to predict a continuous outcome…
8. 8. Supervised Learning – Classification — What is Classification? Classification is the task of predicting a discrete class label for a given input. Unlike regression, which predicts a number, classification predicts a…
9. 9. K-Nearest Neighbors (KNN) — What is KNN? K-Nearest Neighbors is a simple, easy-to-understand, versatile, and strong machine learning algorithm. It is used for both classification and regression problems.…
10. 10. Support Vector Machines (SVM) — What is SVM? Support Vector Machine (SVM) is a powerful supervised learning algorithm used for classification and regression. Goal: Find the best decision boundary (hyperplane)…
11. 11. Naive Bayes — Bayes' Theorem A way to calculate the probability of an event based on prior knowledge of conditions related to the event. P(A B) = (P(B A) P(A)) / P(B) P(A B) : Probability of A…
12. 12. Ensemble Methods and Boosting — The Power of Crowds Ensemble methods combine multiple machine learning models to create a more powerful model. Ideally, the errors of one model are compensated by the strengths of…
13. 13. Model Evaluation and Hyperparameter Tuning — Why Evaluate? Training a model is easy. Knowing if it's good is hard. We need to split data to avoid "memorizing" (overfitting). Cross-Validation (K-Fold) Instead of just one…
14. 14. Unsupervised Learning – Clustering — What is Clustering? Clustering is the task of grouping a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in…
15. 15. Unsupervised Learning – Dimensionality Reduction — The Curse of Dimensionality As the number of features (dimensions) increases, the amount of data needed to generalize accurately increases exponentially. Dimensionality reduction…
16. 16. Natural Language Processing (NLP) — What is NLP? Teaching computers to understand, interpret, and generate human language. Applications : Chatbots, Translation, Sentiment Analysis. Key Techniques 1. Tokenization :…
17. 17. Recommendation Systems — Content-Based Filtering Recommends items similar to those a user liked in the past. Example: Netflix recommends "Action Movies" because you watched "Terminator". Collaborative…
18. 18. Reinforcement Learning — Learning by Trial and Error An agent learns to make decisions by performing actions in an environment and receiving rewards or penalties. Agent : The learner. Environment : The…
19. 19. Developing API using Flask / Webapp with Streamlit — Model Deployment A model in a notebook is useless. We need to serve it to the world. Flask (API) A lightweight web framework for Python. We can create endpoints (e.g., /predict )…
20. 20. Deployment and Web Development — Moving to Production 1. Containerization (Docker) : Packaging the app with all dependencies so it runs everywhere. 2. Cloud Platforms : AWS, Google Cloud, Azure, Heroku, Render.…
21. PYQ: End Term December 2023

1. Introduction to Machine Learning

Welcome to Machine Learning

Machine learning (ML) is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

What is Machine Learning?

Formal Definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." — Tom Mitchell.

Why Machine Learning?

• Adaptability: ML models can adapt to new data without manual intervention.
• Scale: Handling massive datasets that are impossible for humans to process manually.
• Insight: Discovering hidden patterns and correlations in complex data.

Machine Learning Career Paths

The field offers diverse and high-paying roles:

Types of Machine Learning

1. Supervised Learning: Learning with labeled data (e.g., Spam/Not Spam).
2. Unsupervised Learning: Finding patterns in unlabeled data (e.g., Customer Segmentation).
3. Reinforcement Learning: Learning through trial and error (e.g., Game playing AI).

2. Introduction to Data Science

What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data. It unifies statistics, data analysis, informatics, and their related methods in order to analyze actual phenomena with data.

Data Science vs Machine Learning

While often used interchangeably, they are distinct:

• Data Science is the broad umbrella that covers the entire data lifecycle (collection, cleaning, analysis, visualization, and modeling).
• Machine Learning is a tool or subset of data science focused specifically on building predictive models.

Applications of Data Science

Data Science is transforming every industry:

The Data Science Lifecycle

1. Capture: Data Acquisition, Data Entry, Signal Reception.
2. Maintain: Data Warehousing, Data Cleansing, Staging, Processing.
3. Process: Data Mining, Clustering/Classification, Data Modeling.
4. Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining.
5. Communicate: Data Reporting, Data Visualization, Business Intelligence.

Frequently asked questions

Is the Machine Learning course really free?

Yes. The entire Machine Learning course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.

Do I get a certificate for Machine Learning?

Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.

Can I run code while learning?

Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.