Lecture_1.pdf

Email: [email protected] Phone no: 9400543249 Introduction to Machine Learning Instructor: Keerthana Vinod Kumar PMRF Scholar, Koita Centre for Digital health...

Email: [email protected] Phone no: 9400543249 Introduction to Machine Learning Instructor: Keerthana Vinod Kumar PMRF Scholar, Koita Centre for Digital health Indian Insitute of Technology Bombay  Attendance: 10% Quiz/Assignment: 20% Mid-sem: 30% End-sem: 40% Course Plan Attendance compulsory Do not Share meeting link with anyone Course Content Module 1: Basics of ML: Introduction to ML, Artificial Intelligence vs Machine Learning vs Deep Learning, Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning, Supervised Learning & its Types, Unsupervised Learning & its Types, Deep Learning – Basics Module 2: Python Basics for ML: Google Collaboratory for Python – Getting Systems Ready, Python Basics, Basic libraries needed for ML: Numpy, Pandas, Matplotlib, Seaborn and Sklearn Module 3: Data Collection and Processing: Collection of data, importing data through Kaggle API, Handling missing values, Data standardization Module 4: Basics Math for ML: Fundamentals of Linear Algebra, Calculus, Statistics, Probability Module 5: Training ML Models: Machine Learning Model, Selecting a model for training, Model Optimization Techniques, Model Evaluation Course Content Module 6: Classification Models in ML: Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree Classification, Random Forest Classification, Naive Bayes, K-Nearest Neighbors Module 7: Regression Models in ML: Linear Regression, Logistic Regression, Support Vector Machine Regression, Decision Tree Regression, Random Forest Regression Module 8: Clustering Models in ML: K-Means Clustering, Hierarchical Clustering Module 9: ML projects with python: Disease prediction projects What is Artificial Intelligence? Artificial Intelligence suggest that machines can mimic humans in: Talking Thinking Learning Planning Understanding Artificial Intelligence is also called Machine Intelligence and Computer Intelligence. Example: Amazon Echo is a smart speaker that uses Alexa, the virtual assistant AI technology developed by Amazon  What is AI? Non-Intelligence machines Intelligence machines Can make decisions on Cannot make their their own own decisions Perform given task Applications of Artificial Intelligence Machine Translation such as Google Translate Self-Driving Vehicles such as Tesla AI Robots such as Sophia and Aibo Speech Recognition applications like Apple’s Siri or OK Google What is Machine Learning? Machine Learning is a subfield of Artificial intelligence "Learning machines to imitate human intelligence“ It’s a technique to implement AI by learning from data by themselves without being explicitly programmed Applications of Machine learning Sales forecasting for different products Fraud analysis in banking Product recommendations Stock price prediction Dog Cat  ML in LAYMAN'S TERM What is this object? It's a Car Human can learn from past experience and make decision of its own Let us ask the same question to him What is this object? [ But, he is a human being. He can observe and learn ] WHY COULDN'T HE?? CAR CAR BIKE BIKE LET US ASK THE SAME QUESTION NOW It's a Car  WHAT ABOUT MACHINE? Machines follow instructions It cannot take decision of its own We can ask a machine To perform an arithmetic operations such as Addition Comparison Multiplication Print Division Plotting a chart We want a machine to act like a human To identify this object Predict the price in future recognize face I made met him yesterday Natural Language understand, and correct grammar What do we do? Just like, what we did to human, we need to provide experience to the machine. What is Machine Learning? This what we called as Data or Training dataset So, we first need to provide training dataset to the machine What is Machine Learning? Then, devise algorithms and execute programs on the data With respect to the underlying target tasks What is Machine Learning? Then, using the programs, Identify required rules What is Machine Learning? extract required patterns What is Machine Learning? Identify relations What is Machine Learning? So that machine can derive inferences from the data “Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge.” “Learning is any process by which a system improves performance from experience.” Machine Learning Definition by Tom Mitchell (1998): Machine Learning is the study of algorithms that --improve their performance P --at some task T --with experience E In summary Given a machine learning problem Identify and create the appropriate dataset Perform computation to learn required rules, pattern and relations Output the decision Why Machine Learning? No human experts industrial/manufacturing control mass spectrometer analysis, drug design Black-box human expertise face/handwriting/speech recognition driving a car, flying a plane Rapidly changing phenomena Financial modeling Diagnosis, fraud detection Need for customization/personalization personalized news reader Automating automation​​ movie/book recommendation Getting computers to program themselves​​ Let the data do the work instead!​​  What is ML? Example: predict whether a given email is spam or ham (no spam) What is Deep learning? Deep Learning is a subset of Machine Learning. Deep Learning is responsible for the AI boom of the last years Deep learning is an advanced type of ML that handles complex tasks like image recognition. Machine Learning Deep Learning A subset of AI A subset of Machine Learning Uses smaller data sets Uses larger datasets Trained by humans Learns on its own Creates simple algorithms Creates complex algorithms AI vs ML vs DL Ability of machines to imitate intelligent human behavior Applications of AI that allows a system to automatically learn and improve from experience Application of ML that uses complex algorithms and deep neural networks to train a model  What is DL? Deep learning is a subset of machine learning that uses Artificial Neural Network (ANN) to learn from data. Deep learning algorithms can work with an enormous amount of both structured and unstructured data. It deals with algorithms inspired by the structure and function of the human brain. Structured data is typically organized in a tabular format, such as a database with rows and columns Applications of Deep learning Cancer tumor detection Unstructured data lacks a predefined data model or Music generation structure. Examples include text, images, audio, and video Image coloring Object detection  Artificial Neural Network (ANN) Biological Neural Network Artificial Neural Network Biological Neural Network Artificial Neural Network Dendrites Inputs Relationship between Biological Cell nucleus Nodes neural network and artificial neural network Synapse Weights Axon Output  Input layer Hidden layer1 Hidden layer2 Output layer Fig. Architecture of ANN Input Layer: This layer receives the initial input data. Hidden Layers: These layers come between the input and output layers and are responsible for learning patterns in the data. Output Layer: This layer produces the final output of the neural network. Types of Machine Learning Machine learning Supervised Unsupervised/semisupervised Reinforcement  Supervised learning based on supervision we train the machines using the "labelled" dataset, and based on the training, the machine predicts the output The main goal of the supervised learning technique is to map the input variable(x) with the output variable(y) Classification Classification ƒ( , ) = CAR  Supervised learning 1) Classification: >> Goal is to categorize input data into predefined classes or categories; >> The algorithm learns from labeled training data; >> Classification algorithms are used to solve the classification problems in which the output variable is categorical, such as "Yes" or No, Male or Female, Red or Blue, etc. Example: A common example is email spam detection. Given a set of emails labeled as spam or not spam, a classification algorithm can learn to predict whether a new, unseen email is spam or not based on features such as the content, sender, and subject. Classification is about predicting a class/discrete values  Supervised learning : Classification Problem?? Classification ƒ( , ) = CAR  Supervised learning : Classification Elephant Elephant Classifier Tiger Identify this animal? Dataset  Supervised learning : Regression 2) Prediction / Regression: making an inference about a target variable based on input features; a linear relationship between input and output variables. These are used to predict continuous output variables, such as market trends, weather prediction, etc. ƒ( , ) = 20500.50 Regression is about predicting a quantity or continuous values  Unsupervised learning As its name suggests, there is no need for supervision The machine is trained using the unlabeled dataset, and the machine predicts the output without any supervision The main aim of the unsupervised learning algorithm is to group or categories the unsorted dataset according to the similarities, patterns, and differences 1. Clustering meaningful patterns 2. Associations Unsupervised learning : Clustering Clustering It is a way to group the objects into a cluster such that the objects with the most similarities remain in one group and have fewer or no similarities with the objects of other groups. Unsupervised learning : Associations Associations Finds interesting relations among variables within a large dataset