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1. Introduction to Machine Learning
Machine learning algorithms learn from data to improve performance.
Instead of explicit programming, ML identifies patterns autonomously. It's
used in spam filtering, recommendation systems, and medical diagnoses.
The core idea is to 'learn from experience.'
Eng. Anas Khodayf
2. What is Machine Learning?
Definition
Machine learning is all about
training the computer with lots
and lots of data to make it do
intelligent tasks
Key Components
Data, algorithms, models, and
evaluation metrics are crucial.
Types of ML
Supervised, unsupervised, and reinforcement learning approaches.
3. The Machine Learning Process
Data Collection
Gather relevant data from diverse sources.
Data Preprocessing
Clean, transform, and format raw data.
Model Selection
Choose appropriate algorithms for the task.
Training
Fit the model to the preprocessed data.
4. ML Algorithm Overview
Supervised Learning
Use labeled data for training models.
Includes regression and classification.
Unsupervised Learning
Leverage unlabeled data for pattern
discovery. Includes clustering and
dimensionality reduction.
Reinforcement Learning
Learn through trial and error in
dynamic environments.
5. Supervised Learning
Supervised learning involves training models with labeled data. The goal is
to predict outputs for new, unseen inputs. Spam detection and image
classification are examples of supervised learning. Training data informs a
model to map inputs to outputs.
Labeled Data
Input-output pairs used for
training.
Prediction
Predicting outputs for new data
points.
Model Training
Mapping inputs to outputs using the data.
6. Classification vs. Regression
Classification
• Predicts categories (discrete output)
• Examples: Spam detection, image labeling
• Algorithms: Logistic Regression, SVM, Decision Trees
• Metrics: Accuracy, Precision, Recall, F1-score
Regression
• Predicts continuous values
• Examples: House prices, temperature forecasting
• Algorithms: Linear Regression, SVR, Random Forest Regression
• Metrics: MSE, RMSE, MAE, R-squared
7. Linear Regression
Linear regression predicts a continuous output variable. It uses one or more
input variables. The equation is Y = b0 + b1*X1 + b2*X2 + ... + bn*Xn. A
practical example is predicting house prices based on square footage, using
national average of $250/sq ft. Evaluation metrics are MSE and R-squared
(target > 0.7).
Continuous Output
Predicts a continuous
variable based on inputs.
House Prices
Example using square
footage to predict price.
Evaluation
Metrics include MSE and R-squared.
8. Logistic Regression
Logistic regression predicts a binary outcome. It outputs probabilities using a sigmoid function (threshold 0.5). Predicting customer
churn is a use case, based on demographics and usage. Accuracy, Precision, Recall, and F1-score are the main evaluation metrics
(target > 0.8).
Binary Outcome Sigmoid Function Customer Churn
9. K-Nearest Neighbors (KNN)
KNN classifies a new data point. It uses the majority class of its k nearest neighbors. Small K is sensitive to noise, large K is expensive.
10. Support Vector Machines (SVM)
SVM finds the optimal hyperplane. It separates data points into different
classes. The kernel trick maps data into higher dimensions. Image
classification (cats and dogs) is a use case. SVM is effective in high
dimensional spaces.
Optimal Hyperplane Kernel Trick Image Classification
11. Decision Trees
Decision Trees use a tree-like structure. Each node represents a feature. Each branch represents a decision. They are easy to
interpret and visualize.
12. Random Forests
Random Forests combine multiple decision trees. This ensemble learning method reduces overfitting. It also improves
accuracy. Important parameters are the number of trees and max depth.
14. Unsupervised Learning: Clustering Algorithms
K-Means Clustering
• Partitions data into k clusters
• Example: Customer segmentation
• Simple, scalable algorithm
Hierarchical Clustering
• Builds tree of clusters
• Example: Grouping documents
• Detailed cluster relationships
Metrics: Silhouette score, Davies-Bouldin index
15. Reinforcement Learning: Key Concepts
Agent
Decision maker
Environment
Interacts with agent
State
Current situation
Action
Agent's choice
Reward
Feedback signal
Policy
Strategy for actions
16. Eng : Anas Khodayf
[email protected]
Summary
Machine learning empowers systems to learn from data. Supervised learning leverages labeled data for
predictive models. Regression handles continuous outcomes, while classification addresses categorical ones.
Unsupervised Learning, Discover patterns, no labels needed.
Reinforcement Learning Learn actions from feedback.
Choose the algorithm that best suits your data and problem.
