Overview of Machine Learning Techniques | IABAC

Machine learning techniques, including algorithms like decision trees, neural networks, and clustering, enable systems to learn from data, identify patterns, and make decisions. These techniques are widely used in applications like prediction, classification, and automation.

1. Machine Learning
Techniques
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Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Key Metrics in Machine Learning
Challenges in Machine Learning
Future Trends in Machine Learning
Agenda
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3. Introduction to Machine Learning
Overview of Machine Learning
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Machine learning is a subset of artificial intelligence that involves the
development of algorithms allowing computers to learn from and make
decisions based on data.
It is crucial for automating tasks, improving accuracy and efficiency, and
uncovering insights from large datasets.
Industries such as healthcare, finance, retail, and transportation are leveraging
machine learning for predictive analytics, personalized recommendations,
fraud detection, and autonomous systems.
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4. Supervised Learning
Common algorithms include Linear Regression, which predicts continuous
values, and Decision Trees, which are used for both classification and
regression tasks.
This technique requires a large amount of labeled data and is used for tasks
like classification and regression. It involves a training phase and a testing
phase to validate the model's accuracy.
Supervised learning involves training a model on labeled data, where the
desired output is known. The algorithm learns to map inputs to outputs from
the labeled examples.
Definition
Example
Algorithms
Key
Characteristics
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5. Unsupervised learning involves
training models on data without
labeled responses. The goal is to
uncover hidden patterns or intrinsic
structures within the data.
This approach does not require
labeled data, making it ideal for
exploring data with unknown
structures. It often uses methods
like clustering and dimensionality
reduction.
Common algorithms include
K-means clustering for grouping
data points and Apriori for
discovering association rules in
datasets.
Concept and Overview Key Features Examples
Unsupervised Learning
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6. Reinforcement learning involves an
agent that learns by interacting
with its environment to achieve a
goal. It uses rewards and
punishments as signals for positive
and negative behavior.
The agent takes actions in the
environment, receives feedback
through rewards or penalties, and
updates its strategy to maximize
cumulative rewards over time
through trial and error.
Utilized in various domains such as
robotics for autonomous control,
and game AI for developing
strategies and improving gameplay
through continuous learning.
Concept of Reinforcement
Learning
How Reinforcement
Learning Works
Reinforcement Learning
Applications of Reinforcement
Learning
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7. Key Metrics in Machine Learning
92% 89% 85%
Accuracy Precision Recall
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8. Data quality issues can lead to
inaccurate models, as machine
learning relies heavily on large,
high-quality datasets.
Overfitting occurs when a model
learns the training data too well,
including noise and outliers, leading
to poor generalization on new data.
High computational resources are
often required for training complex
models, making it difficult for
organizations with limited
resources to adopt machine
learning widely.
Challenges in Machine Learning
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9. Federated learning enables training models across decentralized devices while
maintaining data privacy, crucial for sensitive data applications.
AutoML automates model selection and hyperparameter tuning, making machine
learning accessible to non-experts and speeding up the development process.
Explainable AI focuses on creating transparent models that provide understandable
justifications for decisions, increasing trust and adoption in critical sectors.
Future Trends in Machine Learning
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10. Thank you
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