01_Machine Learning.pptx and made by students
- 2. 31/12/2024 2 Machine Learning (ML) • The term ML was coined by Arthur Samuel in 1959. • ML is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed. • ML is the is a core sub-area of Artificial Intelligence (AI). • ML applications learn from experience like humans without direct programming. • Machine learning algorithms are trained with training data. When new data comes in, they can make predictions and decisions accurately based on past data.
- 4. 31/12/2024 4 1. Image Recognition • It is used to identify objects, persons, places, digital images, etc. • The popular use case of image recognition and face detection is, Automatic friend tagging suggestion: • Facebook provides us a feature of auto friend tagging suggestion. • Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's face detection and recognition algorithm. • It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture.
- 5. 31/12/2024 5 2. Speech Recognition • While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning. • Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." • Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions.
- 6. 31/12/2024 6 3. Product recommendations: • Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. • Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning. • Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest. • As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.
- 7. 31/12/2024 7 4. Self-driving cars: • One of the most exciting applications of machine learning is self-driving cars. • ML plays a significant role in self-driving cars. • Tesla, the most popular car manufacturing company is working on self-driving car.
- 8. 31/12/2024 8 5. Email Spam and Malware Filtering: • Whenever we receive a new email, it is filtered automatically as important, normal, and spam. • We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning.
- 9. 31/12/2024 9 6. Virtual Personal Assistant: • We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests, they help us in finding the information using our voice instruction. • These assistants can help us in various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc.
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- 11. 31/12/2024 11 Artificial Intelligence Machine learning Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data so that they can give accurate output. Machine learning and deep learning are the two main subsets of AI. Deep learning is a main subset of machine learning. AI has a very wide range of scope. ML has a limited scope. AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned about accuracy and patterns. The main applications of AI are Siri, customer support using chatboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc. On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI. Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.
- 12. 31/12/2024 12 What is a dataset? • A dataset is a collection of data in which data is arranged in some order. • A dataset can contain any data from a series of an array to a database table. Below table shows an example of the dataset: Country Age Salary Purchased India 38 48000 No France 43 45000 Yes Germany 30 54000 No France 48 65000 No Germany 40 74000 Yes India 35 58000 Yes
- 13. 31/12/2024 13 Iris Dataset S.No. Sepal Length Sepal Width Petal Length Petal Width Class 1 5.1 3.5 1.4 0.2 Iris-setosa 2 4.9 3.0 1.4 0.2 Iris-setosa . 50 . . Iris Setosa 51 6.3 2.5 4.9 1.5 Iris-versicolor 52 6.1 2.8 4.7 1.2 Iris-versicolor . 100 Iris-versicolor 101 6.4 3.1 5.5 1.8 Iris-virginica . 150 Iris-virginica Features:- Sepal Length, Sepal Width, Petal Length, Petal Width Classes- Iris-setosa, Iris-versicolor, Iris-virginica
- 14. 31/12/2024 14 Popular sources for Machine Learning datasets S.No. Name Link 1 Kaggle Datasets https://www.kaggle.com/datasets 2 UCI Machine Learning Repository https://archive.ics.uci.edu/ml/index.php 3 Datasets via AWS https://registry.opendata.aws/ 4 Google's Dataset Search Engine https://toolbox.google.com/datasetsearch 5 Microsoft Datasets https://msropendata.com/ 6 Awesome Public Dataset Collection https://github.com/awesomedata/awesome-public-datasets 7 Government Datasets 8 Computer Vision Datasets https://www.visualdata.io/ 9 Scikit-learn dataset https://scikit-learn.org/stable/datasets/index.html
- 15. 31/12/2024 15 Types of Machine Learning
- 16. 31/12/2024 16 Supervised Learning • In Supervised Learning, the machine learns under supervision. It contains a model that is able to predict with the help of labelled dataset. • A labelled dataset is one where you already know the target answer.
- 18. 31/12/2024 18 In this case, we have images which have labelled a spoon or a knife. This known data is fed to the machine, which analyses and learns the association of these images based on its features such as shape, size, sharpness, etc. Now when a new image is fed to the machine without any label, the machine is able to predict accurately that it is a spoon with the help of the past data.
- 19. 31/12/2024 19 Steps Involved in Supervised Learning: •First Determine the type of training dataset •Collect/Gather the labelled training data. •Split the training dataset into training dataset, test dataset, and validation dataset. •Determine the input features of the training dataset, which should have enough knowledge so that the model can accurately predict the output. •Determine the suitable algorithm for the model, such as support vector machine, decision tree, etc. •Execute the algorithm on the training dataset. Sometimes we need validation sets as the control parameters, which are the subset of training datasets. •Evaluate the accuracy of the model by providing the test set. If the model predicts the correct output, which means our model is accurate.
- 20. 31/12/2024 20 • Supervised learning can be further divided into two types – Classification Regression
- 21. 31/12/2024 21 Regression • Regression algorithms are used if there is a relationship between the input variable and the output variable, i.e. a change in one variable is associated with a change in the other variable. • For example, salary based on work experience or weight based on height, etc.
- 22. 31/12/2024 22 • Let’s consider two variables - humidity and temperature. • Here, ‘temperature’ is the independent variable and ‘humidity' is the dependent variable. • If the temperature increases, then the humidity decreases. • These two variables are fed to the model and the machine learns the relationship between them. • After the machine is trained, it can easily predict the humidity based on the given temperature.
- 23. 31/12/2024 23 Regression Algorithms • Linear Regression • Non-Linear Regression • Bayesian Linear Regression • Polynomial Regression
- 24. 31/12/2024 24 Classification • Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc. • Random Forest • Decision Trees • Logistic Regression • Support vector Machines •
- 25. 31/12/2024 25 • In order to predict whether a mail is spam or not, we need to first teach the machine what a spam mail is. • This is done based on a lot of spam filters - reviewing the content of the mail, reviewing the mail header and then searching if it contains any false information. Certain keywords and blacklist filters that are used from already blacklisted spammers. • All of these features are used to score the mail and give it a spam score. The lower the total spam score of the email, the more likely that it is not a scam. • Based on the content, label and the spam score of the new incoming mail, the algorithm decides whether it should land in the inbox or spam folder.
- 26. 31/12/2024 26 Real Life Applications of Supervised Learning • Risk assessment - Supervised learning is used to assess the risk in financial services or insurance domains in order to minimize the risk portfolio of the companies. • Image classification - Image classification is one of the key use cases of demonstrating supervised machine learning. For example, Facebook can recognize your friend in a picture from an album of tagged photos. • Fraud detection - To identify whether the transactions made by the user are authentic or not. • Visual recognition - The ability of a machine learning model to identify objects, places, people, actions and images.
- 27. 31/12/2024 27 Unsupervised Learning • In Unsupervised Learning, the machine uses unlabelled data and learns on itself without any supervision. The machine tries to find a pattern in the unlabelled data and gives a response. • Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision.
- 28. 31/12/2024 28 Unsupervised Learning • Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format. • Example: Suppose the unsupervised learning algorithm is given an input dataset containing images of different types of cats and dogs. The algorithm is never trained upon the given dataset, which means it does not have any idea about the features of the dataset. The task of the unsupervised learning algorithm is to identify the image features on their own. Unsupervised learning algorithm will perform this task by clustering the image dataset into the groups according to similarities between images.
- 29. 31/12/2024 29 Unsupervised Learning • Why use Unsupervised Learning? • Below are some main reasons which describe the importance of Unsupervised Learning: • Unsupervised learning is helpful for finding useful insights from the data. • Unsupervised learning is much similar as a human learns to think by their own experiences, which makes it closer to the real AI. • Unsupervised learning works on unlabeled and uncategorized data which make unsupervised learning more important. • In real-world, we do not always have input data with the corresponding output so to solve such cases, we need unsupervised learning.
- 30. 31/12/2024 30 Unsupervised Learning • Working of Unsupervised Learning • Working of unsupervised learning can be understood by the below diagram:
- 31. 31/12/2024 31 Unsupervised Learning • Here, we have taken an unlabeled input data, which means it is not categorized and corresponding outputs are also not given. Now, this unlabeled input data is fed to the machine learning model in order to train it. Firstly, it will interpret the raw data to find the hidden patterns from the data and then will apply suitable algorithms such as k-means clustering, Decision tree, etc. • Once it applies the suitable algorithm, the algorithm divides the data objects into groups according to the similarities and difference between the objects.
- 32. 31/12/2024 32 Let's take a similar example is before, but this time we do not tell the machine whether it's a spoon or a knife. The machine identifies patterns from the given set and groups them based on their patterns, similarities, etc. Unsupervised learning can be further grouped into clustering and association.
- 33. 31/12/2024 33 Types of Unsupervised Learning Algorithm:
- 34. 31/12/2024 34 Types of Unsupervised Learning Algorithm: •Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities. •Association: An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset. Association rule makes marketing strategy more effective. Such as people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.
- 35. 31/12/2024 35 Clustering - Unsupervised Learning • Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. • For example, finding out which customers made similar product purchases.
- 36. 31/12/2024 36 • Suppose a telecom company wants to reduce its customer churn rate by providing personalized call and data plans. • The behaviour of the customers are studied and the model segments the customers with similar traits. • Several strategies are adopted to minimize churn rate and maximize profit through suitable promotions and campaigns. • In the right side of the image, you can see a graph where customers are grouped. • Group A customers use more data and also have high call durations. • Group B customers are heavy Internet users, while Group C customers have high call duration. • So, Group B will be given more data benefit plants, while Group C will be given cheaper called call rate plans and group A will be given the benefit of both.
- 37. 31/12/2024 37 Association - Unsupervised Learning • Association is a rule-based machine learning to discover the probability of the co-occurrence of items in a collection. • For example, finding out which products were purchased together.
- 38. 31/12/2024 38 • Let’s say that a customer goes to a supermarket and buys bread, milk, fruits and wheat. • Another customer comes and buys bread, milk, rice and butter. • Now, when another customer comes, it is highly likely that if he buys bread, he will buy milk too. • Hence, a relationship is established based on customer behaviour and recommendations are made.
- 39. 31/12/2024 39 Unsupervised Learning algorithms: • Unsupervised Learning algorithms: • Below is the list of some popular unsupervised learning algorithms: • K-means clustering • KNN (k-nearest neighbors) • Hierarchal clustering • Anomaly detection • Neural Networks • Principle Component Analysis • Independent Component Analysis • Apriori algorithm • Singular value decomposition
- 40. 31/12/2024 40 Real Life Applications of Unsupervised Learning • Market Basket Analysis - It is a machine learning model based on the algorithm that if you buy a certain group of items, you are less or more likely to buy another group of items. • Identifying Accident Prone Areas - Unsupervised machine learning models can be used to identify accident-prone areas and introduce safety measures based on the intensity of those accidents.
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- 42. 31/12/2024 42 Supervised Learning Unsupervised Learning Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using unlabeled data. Supervised learning model predicts the output. Unsupervised learning model finds the hidden patterns in data. In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model. The goal of supervised learning is to train the model so that it can predict the output when it is given new data. The goal of unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset. Supervised learning needs supervision to train the model. Unsupervised learning does not need any supervision to train the model. Supervised learning can be categorized in Classification and Regression problems. Unsupervised Learning can be classified in Clustering and Associations problems. Supervised learning can be used for those cases where we know the input as well as corresponding outputs. Unsupervised learning can be used for those cases where we have only input data and no corresponding output data. Supervised learning model produces an accurate result. Unsupervised learning model may give less accurate result as compared to supervised learning. It includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. It includes various algorithms such as Clustering, KNN, and Apriori algorithm.
- 43. 31/12/2024 43 Regression Algorithm Classification Algorithm In Regression, the output variable must be of continuous nature or real value. In Classification, the output variable must be a discrete value. The task of the regression algorithm is to map the input value (x) with the continuous output variable(y). The task of the classification algorithm is to map the input value(x) with the discrete output variable(y). Regression Algorithms are used with continuous data. Classification Algorithms are used with discrete data. In Regression, we try to find the best fit line, which can predict the output more accurately. In Classification, we try to find the decision boundary, which can divide the dataset into different classes. Regression algorithms can be used to solve the regression problems such as Weather Prediction, House price prediction, etc. Classification Algorithms can be used to solve classification problems such as Identification of spam emails, Speech Recognition, Identification of cancer cells, etc. The regression Algorithm can be further divided into Linear and Non-linear Regression. The Classification algorithms can be divided into Binary Classifier and Multi-class Classifier.
- 44. 31/12/2024 44 Confusion Matrix • A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It is often used to measure the performance of classification models, which aim to predict a categorical label for each input instance. The matrix displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data.
- 45. 31/12/2024 45 Confusion Matrix • A 2X2 Confusion matrix is shown below for the image reorganization having a Dog image or Not Dog image.
- 46. 31/12/2024 46 Confusion Matrix • True Positive (TP): It is the total counts having both predicted and actual values are Dog. • True Negative (TN): It is the total counts having both predicted and actual values are Not Dog. • False Positive (FP): It is the total counts having prediction is Dog while actually Not Dog. • False Negative (FN): It is the total counts having prediction is Not Dog while actually, it is Dog.
- 48. 31/12/2024 48 Confusion Matrix •Actual Dog Counts = 6 •Actual Not Dog Counts = 4 •True Positive Counts = 5 •False Positive Counts = 1 •True Negative Counts = 3 •False Negative Counts = 1