## Introduction to Python

Chapter 34 – Sets in Python Authors Ms. Samavi Salman Dr. Rao Muhammad Adeel Nawab Supporting Material Download Supporting Material (Code): here Quick Recap Quick Recap – Tuple in Python In previous Chapter, I presented Tuple Definition Tuples are core data structures in Python that can store an ordered sequence of items Individual values in […]

## Machine Learning

Chapter 2 – Basics of Human Learning Chapter Outline Chapter Outline Quick Recap What is Human Learning Human Learning Cycle Deductive Learning vs. Inductive Learning Chapter Summary Quick Recap Quick Recap – Introduction to Author and Book In previous Chapter, I presented The power of smile and appreciation How to achieve excellence in both Technical […]

## Machine Learning

Chapter 15 – Book Main Findings, Conclusion and Future Work Chapter Outline Chapter Outline Quick Recap Main Findings and Conclusion of the Book Future Work and Feedback Quick Recap Quick Recap – Evaluating Hypothesis (Models) To completely and correctly learn any task follow the Learning Cycle The four main phases of a Learning Cycle […]

## Machine Learning

Chapter 14 – Evaluating Hypothesis (Models) Chapter Outline Chapter Outline Quick Recap Why Evaluate Hypotheses (Model)? Two Main Diseases of Machine Learning Algorithms Comparing Machine Learning Algorithms Evaluation Measures for Classification Problems Evaluation Measures for Regression Problems Evaluation Measures for Sequence-to-Sequence Problems Chapter Summary Quick Recap Quick Recap – Evaluating Hypothesis (Models) Machine Learning […]

## Machine Learning

Chapter 13- Instance Based Learning Chapter Outline Chapter Outline Quick Recap Basics of Instance-based Learning k-Nearest Neighbor (k-NN) Algorithm Applying k-NN Algorithm on Numeric Data – A Step by Step Example Applying k-NN Algorithm on Categorical Data – A Step by Step Example Chapter Summary Quick Recap Quick Recap – Bayesian Learning Alhumdulilah, Up […]

## Machine Learning

Chapter 12- Bayesian Learning Chapter Outline Chapter Outline Quick Recap Bayes Theorem and Bayesian Learning Algorithms Bayes Optimal Algorithm Gibbs Algorithm Naive Bayes Algorithm Chapter Summary Quick Recap Quick Recap – artificial Neural Networks Following Machine Learning Algorithms are based on Symbolic Representation FIND-S Algorithm List Then Eliminate Algorithm Candidate Elimination Algorithm ID3 Algorithm Symbolic […]

## Machine Learning

Chapter 11- Artificial Neural Networks Chapter Outline Chapter Outline Quick Recap Artificial Neural Networks General Architecture – Artificial Neural Networks Perceptron – Two Layer Artificial Neural Networks Machine Learning Cycle – Perceptron Multi-layer Artificial Neural Networks Overfitting – Artificial Neural Networks Chapter Summary Quick Recap Quick Recap – Decision Tree Learning Main Problems – Candidate […]

## Machine Learning

Chapter 10 – Decision Tree Learning Chapter Outline Chapter Outline Quick Recap Decision Tree Learning ID3 – Basic Decision Tree Learning Algorithm Machine Learning Cycle – ID3 Algorithm Overfitting – ID3 Algorithm Chapter Summary Quick Recap Quick Recap – Version Space Algorithm Problem – FIND-S Algorithm Only returns one hypothesis which best fits the […]

## Machine Learning

Chapter 9 – Version Space Algorithms Chapter Outline Chapter Outline Quick Recap Version Space List Then Eliminate Algorithm Candidate Elimination Algorithm Chapter Summary Quick Recap Quick Recap – FIND-S Algorithm Learning is a Searching Problem Research is defined as a systematic investigation of sources and materials , to develop solutions for Real-world Problems to improve […]

## Machine Learning

Chapter 7 – Concept Learning and Hypothesis Representation Chapter Outline Chapter Outline Quick Recap Basics of Concept Learning Instance Space, Training Data, Concept Space and Hypothesis Space Hypothesis (h) Representation Hypothesis Space (H) Representation Chapter Summary Quick Recap Quick Recap – Treating a Problem as a Machine Learning Problem – Step by Step Examples […]