This document provides an overview of machine learning. It defines machine learning as allowing computers to learn from experience without being explicitly programmed. It discusses the differences between machine learning and traditional programming, and describes the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Several common algorithms used in supervised and unsupervised learning are also outlined. The document then explains how machine learning algorithms learn and their basic working procedure. It concludes by discussing model evaluation, providing some case studies of machine learning applications, and addressing requirements for math/programming background and platforms for developing machine learning code.

1. Road To
Machine
Learning
Md. Mahfujur Rahman,
Lecturer, CSE, DIU

3. CONTENTS
❏ What is Machine Learning?
❏ Machine Learning VS Traditional Programming
❏ Types of Machine learning
❏ Supervised Learning
❏ Unsupervised Learning
❏ ML VS DEEP Learning
❏ How a machine learning algorithm learns?
❏ Working Procedure of ML
❏ Confusion Matrix
❏ ML Performance Analysis
❏ ML Case Studies
❏ Need Math Background?
❏ Programming experience
❏ Where we do ML Code?

4. What is Machine Learning?
❖ Show the computer some real world data and
let it learn from it
❖ Machine Learning is the study of
algorithms that improve their
performance at some task with
experience
❖ Machine learning is a field of computer science
that gives computers the ability to learn
without being explicitly programmed.

5. Machine Learning vs Traditional Programming

6. Types of Machine Learning
❖ Supervised learning
– Given: training data +
desired outputs (labels)
❖ Unsupervised learning – Given: training
data (without desired outputs)
❖ Reinforcement learning – Rewards from
sequence of actions

7. Supervised Learning Algorithms
Common classification algorithms include:
❖ Support vector machines (SVM)
❖ Neural networks
❖ Naïve Bayes classifier
❖ Decision trees
❖ Discriminant analysis
❖ Nearest neighbors (kNN)
Common regression algorithms include:
❖ Linear regression
❖ Nonlinear regression
❖ Generalized linear models
❖ Decision trees
❖ Neural networks

8. Unsupervised Algorithms
❖ K-means clustering
❖ Principal Component Analysis (PCA)
❖ Gaussian Mixture Models (GMM)
❖ Self-organizing Maps (SOM)
❖ Hidden Markov Models (HMM)

9. Supervised / Unsupervised Algorithms

10. ML vs DL
- When this problem is solved through machine learning and
when deep learning

11. ML vs DL(Cont)

12. How a Machine Learning Algorithms Learns

13. Working Procedure of ML
ML step 1: get samples (training data)
ML step 2: pre-process the training data
ML step 3: choose an algorithm
ML step 4: train your algorithm
ML step 5: getting predictions
ML step 6: evaluation

14. Model Evaluation
❖ Cross Validation
❖ Classification Metrics
- Confusion Matrix
- Accuracy
- Precision
- Recall
- Area Under Curve
- F Measures

15. Confusion Matrix

16. ML Case Studies

17. ML Case Studies
CASE STUDY 1: Predicting House Price

18. ML Case Studies
CASE STUDY 2: Sentiment Analysis

19. ML Case Studies
CASE STUDY 3: Document Retrieval

20. ML Case Studies
CASE STUDY 4: Product Recommendation

21. ML Case Studies
CASE STUDY 5: Visual Product Recommendation

22. Need Math Background?
Basic calculus
- Concept of derivatives
Basic linear algebra
- Vectors
- Matrices
- Matrix multiply

23. Programming Experience
Programming Language:
- Python
ML/DL Tool Kits:
- Scikit-Learn
- Tensorflow
- PyTorch
- Keras

24. Where We do ML Code
Off Line Platform:
- Anaconda: https://www.anaconda.com/products/individual
- Pycharm: https://www.jetbrains.com/pycharm/
Online Platform:
- Google Colaboratory:
https://colab.research.google.com/notebooks/intro.ipynb
-Jupyter: https://jupyter.org/

25. Questions?