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Questions and Answers
Who is the instructor for the Introduction to Machine Learning course?
Keerthana Vinod Kumar
What is the percentage weight of the End-sem exam?
Which of the following is a method of Machine Learning?
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What are the basic libraries needed for Machine Learning in Python?
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Define Machine Learning.
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What is an example of an AI application for machine translation?
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Machines can make decisions on their own.
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AI is also called _____ and _____ intelligence.
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What is an example of a self-driving vehicle?
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Which of the following is a type of machine learning?
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Study Notes
Course Overview
- Course titled "Introduction to Machine Learning" taught by Keerthana Vinod Kumar at IIT Bombay.
- Attendance contributes 10% to final grade, with quizzes/assignments at 20%, mid-sem at 30%, and end-sem at 40%.
- Attendance is compulsory; sharing meeting links is prohibited.
Course Content
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Module 1: Basics of Machine Learning
- Concepts include Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning.
- Types of ML: Supervised, Unsupervised, Reinforcement Learning.
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Module 2: Python Basics for ML
- Introduction to Google Collaboratory and setting up Python environment.
- Core libraries: Numpy, Pandas, Matplotlib, Seaborn, Sklearn.
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Module 3: Data Collection and Processing
- Techniques for data collection and importing data via Kaggle API.
- Methods for handling missing values and standardizing data.
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Module 4: Basics of Mathematics for ML
- Fundamental topics: Linear Algebra, Calculus, Statistics, Probability.
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Module 5: Training ML Models
- Focuses on selecting and optimizing machine learning models, alongside model evaluation techniques.
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Module 6: Classification Models in ML
- Key models include Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors.
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Module 7: Regression Models in ML
- Models include Linear Regression, Logistic Regression, Support Vector Machine Regression, Decision Tree Regression, Random Forest Regression.
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Module 8: Clustering Models in ML
- Concepts of K-Means Clustering and Hierarchical Clustering explored.
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Module 9: ML Projects with Python
- Focus on practical applications through disease prediction projects.
Artificial Intelligence (AI)
- AI enables machines to perform human-like functions: talking, thinking, learning, planning, and understanding.
- Alternate names for AI: Machine Intelligence, Computer Intelligence.
- Example: Amazon Echo using Alexa as a virtual assistant.
Characteristics of AI
- Non-intelligent machines execute tasks but cannot make independent decisions.
- Intelligent machines can make decisions autonomously.
Applications of AI
- Examples include:
- Machine Translation (e.g., Google Translate).
- Self-Driving Vehicles (e.g., Tesla).
- AI robots (e.g., Sophia, Aibo).
- Speech recognition systems (e.g., Siri, OK Google).
Machine Learning (ML)
- ML is a subset of AI focusing on training machines to emulate human intelligence by learning from data autonomously.
Applications of Machine Learning
- Key uses include:
- Sales forecasting.
- Fraud detection in banking.
- Product recommendations.
- Stock price predictions.
Understanding ML in Layman's Terms
- Machines require clear instructions to perform tasks and lack the capacity for independent decision-making.
- Unlike humans, machines are unable to learn from past experiences without explicit programming.
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Description
This quiz focuses on key concepts and applications of Machine Learning. Designed for students under Instructor Keerthana Vinod Kumar at IIT Bombay, it assesses understanding through various evaluation methods including attendance, quizzes, and exams.
