Machine Learning | Types of Machine Learning | Examples

1. MACHINE LEARNING
Subdomain of artificial intelligence (AI)

2. Introduction
• Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and
algorithms to imitate the way that humans learn, gradually improving its accuracy.
• Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.
• Machine learning is the process of programming computers to optimize a performance criterion using example data or
past experience. It involves creating a model with adjustable parameters and then using a computer program to optimize
those parameters based on training data or past experiences.
• The model can be either predictive, making future predictions, or descriptive, extracting knowledge from data.
• The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is
capable of doing tasks that are too complex for a person to implement directly.

3. Types of Machine Learning
• Supervised Learning: In supervised learning, models are trained on labeled datasets where both input and output
parameters are known. These algorithms learn to map inputs to correct outputs, making them suitable for tasks like image
classification and regression.
• Unsupervised Learning: Unsupervised learning deals with unlabeled data. Algorithms in this category discover patterns,
clusters, or structures within the data without explicit output labels. Clustering and dimensionality reduction are common
applications.
• Semi-Supervised Learning: This approach combines elements of both supervised and unsupervised learning. It utilizes a
mix of labeled and unlabeled data for training. Semi-supervised learning is useful when obtaining fully labeled datasets is
challenging or expensive.
• Reinforcement Learning: In reinforcement learning, an agent learns by interacting with an environment and receiving
feedback (rewards or penalties). The goal is to maximize cumulative rewards over time.

4. Supervised Learning
In supervised learning, an algorithm is trained using labeled data. This means that the input data used for training is paired
with corresponding output labels.
The primary goal of supervised learning is to find a mapping or relationship between the input variables (features) and the
desired output (labels). This enables the algorithm to make precise predictions or classifications when faced with fresh,
unobserved data.
During training, the algorithm iteratively adjusts its parameters to minimize the discrepancy between its predicted output and
the actual output (ground truth) in the training set.
Supervised learning aims to approximate the mapping function so well that when new input data arrives, the corresponding
output variable can be predicted. Think of it as a teacher supervising the learning process: the algorithm iteratively makes
predictions on the training data and is corrected by the “teacher” until it achieves an acceptable level of performance.

5. Supervised Learning
Types:
• Regression: In regression problems, the goal is to predict a continuous output or value. For example, predicting the
price of a house based on features like the number of bedrooms, square footage, and location.
• Classification: In classification problems, the goal is to assign input data to one of several predefined categories or
classes. Examples include spam email detection, image classification (identifying whether an image contains a cat or
a dog), and sentiment analysis.

6. Unsupervised Learning
In unsupervised learning, the algorithm deals with unlabeled data. There are no predefined output labels.
The primary objective is to uncover patterns, structures, or clusters within the data without explicit guidance.
Common applications include:
• Clustering: Grouping similar data points together (e.g., customer segmentation based on purchasing behavior).
• Dimensionality Reduction: Reducing the number of features while preserving essential information (e.g., PCA).
• Association: Discovering relationships between items (e.g., market basket analysis).

7. Semi-Supervised Learning
This method combines the best of both worlds. It uses:
• A small amount of labeled data (where each data point has known output labels).
• A large amount of unlabeled data (where output labels are missing).
The goal remains similar to supervised learning: to learn a function that predicts the output variable based on input features.
Semi-supervised learning is particularly useful when labeling all available data is expensive or challenging. It’s like a teacher
teaching a few concepts in class and assigning homework questions related to similar concepts.
Applications of Semi-Supervised Learning:
• Text Classification: Train a text classification model using a small labeled dataset and a large amount of unlabeled text data.
• Image Classification: Build an image classification model using labeled images and a wealth of unlabeled image data.

8. Reinforcement Learning
Reinforcement Learning (RL) is a fascinating area of machine learning that combines elements of trial-and-error learning,
decision-making, and optimization. Here’s a concise explanation:
Objective:
• RL is all about goal-oriented algorithms. These algorithms learn how to achieve a complex objective (goal) or maximize a
specific dimension over multiple steps.
• For example, in a game, RL can maximize the points won over many moves.
The Basics:
• In RL, an agent interacts with an environment.
• The agent takes actions based on its current state, and the environment responds with rewards or penalties.
• The goal is to learn a policy (strategy) that maximizes cumulative rewards over time.

9. Why is Machine Learning Important?
Data Explosion:
• We live in an era of data abundance. ML helps us extract valuable insights from this vast ocean of information.
• Whether it’s analyzing customer behavior, predicting stock prices, or diagnosing diseases, ML thrives on data.
Automation and Efficiency:
• ML automates repetitive tasks, freeing up human resources for more creative and strategic work.
• For instance, ML algorithms can process invoices, recommend personalized content, and optimize supply chains.
Personalization:
• ML tailors experiences to individual preferences. Think of personalized recommendations on streaming platforms or e-
commerce sites.
• It enhances user satisfaction and engagement.

10. Why is Machine Learning Important?
Healthcare and Medicine:
• ML aids in early disease detection, drug discovery, and personalized treatment plans.
• Imagine predicting patient outcomes based on historical data.
Finance and Fraud Detection:
• ML models analyze transaction patterns to detect anomalies and prevent fraud.
• They also optimize investment portfolios.
Natural Language Processing (NLP):
• ML powers chatbots, virtual assistants, and language translation.
• It enables seamless communication across languages.

11. Why is Machine Learning Important?
Image and Speech Recognition:
• ML algorithms recognize faces, objects, and speech patterns.
• Applications range from self-driving cars to voice assistants.
Recommendation Systems:
• ML suggests products, movies, or music based on user behavior.
• It drives sales and enhances user engagement.
Scientific Research:
• ML accelerates scientific discoveries by analyzing complex data.
• From climate modeling to genomics, ML aids researchers.

12. Why is Machine Learning Important?
Predictive Analytics:
• ML predicts future trends, stock prices, and customer behavior.
• Businesses make informed decisions based on these insights.
In summary, ML is the backbone of innovation, enabling us to solve real-world problems, automate processes, and create
personalized experiences. Its impact is far-reaching and continues to grow.

13. THANK YOU