This document serves as a beginner's guide to machine learning (ML), explaining its definition, popular algorithms, and types including supervised, unsupervised, and reinforcement learning. It discusses challenges in ML such as data bias and the need for high-quality datasets, tools for building models like scikit-learn and TensorFlow, and real-life applications across various industries. Finally, it touches on the future of ML, emphasizing the importance of ethics and responsible AI.

1. A BEGINNER’S GUIDE TO
MACHINE LEARNING
ALGORITHMS
iabac.org

2. INTRODUCTION TO
MACHINE LEARNING
Machine Learning (ML) is a type of AI where computers
learn from data to identify patterns, make predictions,
and improve. Used in recommendations, medical
research, and fraud detection, ML enables data-driven
decision-making across industries.
iabac.org

3. TYPES OF ML
ALGORITHMS
2
3
Supervised Learning
Unsupervised Learning
Reinforcement Learning
1
iabac.org

4. POPULAR ML
ALGORITHMS
Linear Regression predicts continuous
values from data features, while
Decision Trees use a flowchart of
questions for decisions. K-Means
Clustering groups similar data points,
helpful in segmenting data. Naive
Bayes, a probability-based classifier, is
commonly applied in tasks like spam
detection, leveraging likelihoods to
categorize information efficiently.
iabac.org

5. TRAINING AND
TESTING
Data is divided into training sets for learning and testing sets for
evaluation. This separation checks model accuracy on new data.
Overfitting can happen if a model learns details too closely from the
training data, causing it to perform poorly on unseen data, as it fails to
generalize broader patterns accurately.
iabac.org

6. CHALLENGES IN ML
Bias in Data: Can lead to unfair or inaccurate
predictions.
Data Requirements: Large, quality datasets are
essential.
Computational Power: Complex models require high
processing power.
iabac.org

7. TOOLS FOR BUILDING ML
MODELS
Scikit-Learn: Simple Python library for data analysis.
TensorFlow and PyTorch: Used for advanced models, like neural
networks.
No-Code Tools: Tools like Google’s Teachable Machine allow
beginners to experiment.
iabac.org

8. REAL-LIFE
APPLICATIONS OF
ML
Healthcare: Analyzing medical images and
diagnostics.
E-commerce: Personalized
recommendations.
Finance: Fraud detection and risk analysis.
iabac.org

9. FUTURE OF MACHINE
LEARNING
Enhanced Healthcare through diagnostics
and personalized treatments.
Autonomous Systems like self-driving cars
and drones.
Quantum Computing Integration for faster,
complex data processing.
Ethics and Responsible AI ensuring fairness
and transparency.
Proactive ML systems anticipating user
needs in real time.
iabac.org

10. GETTING STARTED IN ML
Take online courses (DataMites, YouTube).
Begin with small projects (Kaggle datasets).
Explore no-code tools to ease into ML concepts.
iabac.org

11. THANK YOU
Visit www.iabac.org