Machine Learning and NLP Introduction

Introduction to Machine Learning and NLP

• Supervised learning algorithms learn from labeled data to predict outcomes for new, unseen data.
  • Predicting house prices and spam detection are examples of supervised learning.
  • Supervised learning is guided by labeled data, similar to learning from a teacher.
• Unsupervised learning groups similar items together without specific instructions, finding patterns and structures in unlabeled data.
  • Customer segmentation and grouping news articles are examples of unsupervised learning.
  • Unsupervised learning involves finding hidden groups in unlabeled data.
• Reinforcement learning involves training agents to make optimal decisions in an environment to maximize rewards through feedback (rewards or penalties).
  • Game playing and robot navigation are examples of reinforcement learning.
  • Reinforcement learning involves learning by doing through rewards and penalties.

Overview of NLP

• NLP bridges the gap between human language and computers.
  • Chatbots (customer service) and machine translation are examples of applications.
  • NLP helps computers understand human language.
• Text Processing Basics involves preparing text data for computers to understand.
  • Removing punctuation and converting text to lowercase are examples of text processing.
  • Text processing involves cleaning and organizing text.

ML/NLP Workflow

• Preprocessing cleans and transforms raw data into a usable format for ML models.
  • Removing stop words and converting text to numerical vectors are examples of preprocessing.
  • Preprocessing involves preparing data to get it ready for modeling.
• Model Building involves selecting, training, and evaluating machine learning models to perform desired NLP tasks.
  • Training a sentiment analysis model and building a chatbot are examples of model building.
  • Model building can be seen as creating the brain and the method for teaching an AI to learn.

Supervised ML Algorithms

• Linear Regression predicts a continuous output based on a linear relationship with input features.
  • Predicting house prices based on size and crop yield based on rainfall are examples of linear regression.
  • Linear regression involves drawing a line through numbers.
• Logistic Regression predicts a categorical output and outputs a probability score between 0 and 1.
  • Email spam detection and predicting customer churn are examples of logistic regression.
  • Logistic regression is about using logic to categorize data.
• Decision Trees use a tree-like structure to make decisions based on a series of rules, similar to a flowchart.
  • Deciding what to eat and medical diagnosis based on symptoms are examples of decision trees.
  • Decision trees can be thought of a flowchart of rules.
• Random Forest combines multiple Decision Trees to improve accuracy and robustness, like asking many experts for their opinions and taking a vote.
  • Credit risk assessment and image classification are examples of random forests.
  • Random forests involve voting among many trees.
• Support Vector Machine (SVM) finds the best line that separates different classes of data.
  • Image classification and handwriting recognition are examples of SVM
  • Support Vectors finds the best separating line to maximize the margin.
• K-Nearest Neighbors (KNN) classifies a new data point based on the majority class among its "k" nearest neighbors, similar to asking your neighbors to vote.
• Recommending products and classifying medical conditions are examples of KNN
• KNN uses the Neighbors for data, and uses a majority to vote.

Text Preprocessing in NLP

• Tokenization: Breaking text into individual words or phrases (tokens).
  • Regular Expression-Based Tokenization: Uses regular expressions to define how to split text.
  • Whitespace Tokenization splits text based on spaces.
  • Byte-Pair Encoding (BPE) starts with individual characters and merges frequently occurring pairs into subword units.
• Stemming reduces words to their root form by removing prefixes and suffixes, which can create non-real words.
  • Porter Stemmer is a widely used rule-based stemming algorithm, is known as basic stemming.
  • Lancaster Stemmer is more aggressive stemming, often producing shorter stems.
  • Snowball Stemmer is a multilingual stemming algorithm.
• Lemmatization reduces words to their base or dictionary form (lemma) and produces real words.
  • WordNet Lemmatizer uses the WordNet lexical database to find lemmas.
  • SpaCy Lemmatizer is and accurate lemmatizer within the SpaCy library, designed for fast and efficient lematization
  • Stanford Lemmatizer, considered robust and comprehensive, is part of the Stanford CoreNLP suite.
• Part-of-Speech (POS) Tagging: Assigning grammatical tags (noun, verb, adjective, etc.) to words in a sentence.
  • Hidden Markov Model (HMM) is a statistical model used for POS tagging based on word sequences and probabilities, it is probabilistic.
  • Stanford POS Tagger, from Stanford CoreNLP, provides detailed tagging information and is accurate.
  • Maximum Entropy POS uses contextual information to improve accuracy.

Unsupervised Learning and Advanced NLP Techniques

• K-means partitions data into k clusters based on similarity, similar to grouping students by test scores.
  • Customer segmentation and image compression are examples of K-means.
  • K-means involves grouping by similarity into K clusters.
• Hierarchical Clustering builds a hierarchy of clusters by iteratively merging or splitting clusters, similar to creating a family tree.
  • Document clustering and phylogenetic analysis are examples of hierarchical clustering.
  • Hierarchical clustering is tree-like and creates a cluster hierarchy.

Transformer Models

• BERT (Bidirectional Encoder Representations from Transformers) considers the context of a word from both left and right directions.
  • Improved search engine results and sentiment analysis are examples of BERT.
  • BERT provides contextual understanding through bidirectionality.
• GPT (Generative Pre-trained Transformer) generates human-like text.
  • Chatbot conversations and automated content creation are examples of GPT.
  • GPT generates text.

NLP Tasks

• Text Classification assigns predefined categories to text, like sorting emails into different folders.
  • Spam detection and topic classification are examples of text classification.
  • Text classification sorts and categorizes text.
• Sentiment Analysis determines the emotional tone of text, like understanding audience reactions to movies.
  • Brand monitoring and product feedback analysis are examples of sentiment analysis.
  • Sentiment analysis focuses on emotion and how text feels.
• Language Generation creates new text, like a computer writing a poem or article.
  • Chatbots generating responses and machine translation are examples of language generation.
  • Language generation creates new text.

Applications and Ethical Considerations in NLP and ML

• ML and NLP applications in healthcare can improve diagnosis and personalized medicine.
• ML and NLP applications in finance focuses more on smarter decisions and risk management.
  • Fraud detection and algorithmic trading are examples of financial applications.
• Customer service applications uses more AI driven assistants focusing on 24/7 support
• ML models can reflect and amplify existing biases in data, leading to unfair outcomes, which presents an ethical concern.
  • A hiring algorithm discriminating against groups and facial recognition systems showing higher error rates are examples of bias.
  • Bias leads to unfairness and reflects existing prejudices in data.
• Collecting and using personal data for ML raises privacy concerns.
  • Data breaches and using personal data without consent are examples of privacy violations.
  • Privacy involves protecting personal data with data security and consent.
• Fairness ensures that ML systems treat everyone fairly and equitably.
  • Algorithms discriminating based on race or gender and unequal access to AI-powered healthcare are examples of fairness issues.
  • Fairness ensures equal treatment and no discrimination.

Real-World Case Studies Demonstrating the Applications of AI and ML

• Real-World Case Studies focus on the applications of AI and ML in areas like healthcare, finance, and customer service.
  • Netflix uses machine learning to personalize movie recommendations.
  • Banks use AI to detect fraudulent transactions.
• Hands-on Projects and Exercises puts knowledge into practice.
  • Build a sentiment analysis model for Twitter data.
  • Create a simple chatbot that answers basic questions about your college or university.