Fundamentals of Machine Learning | IABAC

Machine learning involves training algorithms to recognize patterns in data, enabling computers to make predictions or decisions without explicit programming. It combines statistics, data analysis, and computer science to create models that learn and improve over time.

1. Fundamentals of
Machine Learning
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2. Introduction to Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Real-World Applications
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Agenda
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3. Introduction to Machine Learning
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Machine Learning (ML) is a subset of artificial intelligence (AI) focused on
building systems that learn from data to improve their performance.
ML algorithms identify patterns in data and make decisions with minimal
human intervention.
Importance: Machine Learning drives innovations in various fields such as
healthcare, finance, and technology, enhancing efficiency and enabling new
solutions.
Key Concepts
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4. Types of Machine Learning
Main Types of Machine Learning
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Supervised Learning: Involves training a model on labeled data. Examples include
classification and regression tasks.
Unsupervised Learning: Deals with unlabeled data. Examples include clustering and
association tasks.
Reinforcement Learning: Involves training agents through rewards and penalties. Used
in robotics, gaming, and navigation.
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5. Supervised Learning
Key Points
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Supervised Learning involves training a model on labeled data, where the correct
output is known.
Common algorithms include Linear Regression, Logistic Regression, Decision Trees, and
Support Vector Machines (SVMs).
Applications include spam detection in emails, fraud detection in financial transactions,
and image recognition.
Example: In email spam detection, the model is trained on a dataset of emails labeled
as 'spam' or 'not spam' to classify new emails.
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6. Unsupervised Learning
Overview and Applications
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Unsupervised Learning involves training models on data without labeled responses.
Common techniques include clustering and dimensionality reduction.
Applications include customer segmentation, anomaly detection, and genetic data
analysis.
K-means clustering is a popular method for grouping similar data points.
Principal Component Analysis (PCA) is used for reducing data dimensionality.
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7. Reinforcement Learning
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Reinforcement Learning (RL) involves training models to make sequences of
decisions by rewarding desired behaviors and penalizing undesired ones.
Agents learn optimal behaviors through trial and error interactions with a
dynamic environment, receiving rewards or penalties.
Common applications include robotics (e.g., teaching robots to walk), game
playing (e.g., AlphaGo), and dynamic pricing strategies in finance.
In healthcare, RL is used for personalized treatment plans, optimizing the
timing and dosing of medications for individual patients.
RL is also utilized in autonomous driving, where vehicles learn to navigate
complex environments safely and efficiently.
Key Concepts and Applications
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8. Real-World Applications
Utilizing ML for diagnostics, personalized
medicine, and predicting patient outcomes.
Employing ML for fraud detection, algorithmic
trading, and risk management.
Enhancing customer experiences through
recommendation systems and inventory
management.
Healthcare
Finance
Retail
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