What is machine learning and how does it work | IABAC

Machine learning is a branch of AI that enables systems to learn from data and improve over time. It works by identifying patterns, making predictions, and automating decision-making without explicit programming.

1. What Is Machine
Learning and How
Does It Work?
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Introduction to Machine Learning
How Machine Learning Works
Applications of Machine Learning
Challenges in Machine Learning
Future of Machine Learning
Agenda
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3. How Machine Learning
Works
General Process of Machine Learning
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Data Collection: Gather and aggregate large volumes of data from
various sources to create a dataset.
Data Preparation: Clean and preprocess the data to handle missing
values, remove duplicates, and normalize the data for analysis.
Deployment: Implement the model in a real-world environment to make
predictions or automate decisions based on new data.
Model Training: Use algorithms to recognize patterns within the data
and build a predictive model.
Model Evaluation: Assess the model's performance using various metrics such
as accuracy, precision, and recall to ensure it meets the desired criteria.
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4. Applications of Machine Learning
Key Applications Across Industries
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Healthcare: Machine learning algorithms are used for early diagnosis of
diseases, personalized treatment plans, and predicting patient outcomes.
Finance: Fraud detection systems, risk assessment models, and algorithmic
trading rely heavily on machine learning for accurate and real-time analysis.
Transportation: Autonomous vehicles, traffic pattern analysis, and predictive
maintenance of infrastructure are driven by machine learning technologies.
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5. Challenges in Machine Learning
Key Challenges
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Data Quality: Ensuring the availability of clean, relevant, and
comprehensive data is critical for training effective models.
Overfitting: Developing models that generalize well to new, unseen
data rather than just memorizing the training data.
Interpretability: Making machine learning models transparent and
understandable to humans, especially in critical applications like
healthcare and finance.
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6. Future of Machine Learning
Future Trends and Developments
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Integration of quantum computing to solve complex machine learning
problems more efficiently.
Expansion of machine learning applications in personalized medicine,
smart cities, and autonomous systems.
Development of more energy-efficient algorithms to reduce the carbon
footprint of machine learning processes.
Increased Use of Automated Machine Learning (AutoML) tools to simplify
the model creation process.
Advancements in Explainable AI (XAI) to improve transparency and trust
in machine learning models.
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