Machine learning is a subset of artificial intelligence that enables systems to learn from experience without being explicitly programmed. It includes various types such as supervised, unsupervised, and reinforcement learning, and common algorithms include decision trees, support vector machines, and neural networks. Applications span across multiple industries, including healthcare, finance, and retail, making systems more efficient in handling large volumes of data.

1. What Is Machine
Learning and How Does
It Work?
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2. Introduction to Machine Learning
Types of Machine Learning
How Machine Learning Works
Common Algorithms in Machine Learning
Applications of Machine Learning
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Agenda
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3. Introduction to Machine
Learning
Understanding Machine Learning
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Machine learning is a subset of artificial intelligence that enables
systems to learn and improve from experience without being explicitly
programmed.
It is important because it allows for the automation of analytical model
building, making systems more efficient and capable of handling large
volumes of data.
Applications of machine learning include image and speech
recognition, recommendation systems, fraud detection, and predictive
analytics in various industries.
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4. Types of Machine Learning
Learning from labeled data to make predictions or classify
information.
Learning through trial and error to achieve long-term goals.
Finding patterns and relationships in data without predefined labels.
Reinforcement
Learning
Supervised Learning
Unsupervised Learning
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5. How Machine Learning Works
Steps in Machine Learning Workflow
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Data Collection: Gather relevant data from various sources. The quality and quantity of
data are crucial for building effective models.
Data Preprocessing: Clean and prepare the data by handling missing values,
normalizing features, and splitting datasets into training and testing sets.
Model Training: Use training data to teach the machine learning algorithm. This step
involves selecting the appropriate algorithm and tuning hyperparameters.
Evaluation: Assess the model's performance using test data, ensuring it generalizes well
to new, unseen data. Metrics like accuracy, precision, and recall are often used.
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6. Common Algorithms in Machine Learning
A tree-like model used for
classification and regression tasks by
splitting data into branches.
A supervised learning model that uses
hyperplanes to separate different
classes in the data.
Inspired by the human brain, these
networks consist of interconnected
layers that process data iteratively.
Decision Trees Support Vector Machines Neural Networks
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7. Applications of Machine Learning
ML is used for disease prediction,
personalized treatment plans, and
medical imaging analysis.
ML aids in fraud detection, algorithmic
trading, and risk management.
ML supports inventory management,
customer segmentation, and
personalized marketing.
Healthcare Finance Retail
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