Unit 1 - Introduction To Machine Learning.pdf

Unit 1 : Introduction to Machine Learning What is Machine Learning? History - - Alan Turing, in his 1950 paper, “Computing Machinery and Intelligence”, asked, “Can machines think?” - The term Machine Learning first introduced by Arthur Samuel in 1959. He defines machine le...

Unit 1 : Introduction to Machine Learning What is Machine Learning? History - - Alan Turing, in his 1950 paper, “Computing Machinery and Intelligence”, asked, “Can machines think?” - The term Machine Learning first introduced by Arthur Samuel in 1959. He defines machine learning as, ” Field of study that gives computers the ability to learn without being explicitly programmed.” Definitions - - Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. - According to Jason Brownlee, “Machine learning is the training of a model from data that generalizes a decision against a performance measure.” - Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. - We can define it in a summarized way as: “Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.” - A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks T, as measured by P, improves with experience E. Examples - Handwriting recognition learning problem o Task T: Recognizing and classifying handwritten words within images o Performance P: Percent of words correctly classified o Training experience E: A dataset of handwritten words with given classifications - A robot driving learning problem o Task T: Driving on highways using vision sensors o Performance P: Average distance traveled before an error o Training experience E: A sequence of images and steering commands recorded while observing a human driver - Machine learning brings computer science and statistics together. - With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. - Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. - A machine has the ability to learn if it can improve its performance by gaining more data. How does Machine Learning work? - A Machine Learning system learns from historical data, builds the logical models, and whenever it receives new data, predicts the output for it. - The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately. - Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. - Block diagram Features of Machine Learning – - Machine learning uses data to detect various patterns in a given dataset. - It can learn from past data and improve automatically. - It is a data-driven technology. - Machine learning is much similar to data mining as it also deals with the huge amount of the data. Need for Machine Learning – - The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. - As a human, we have some limitations as we cannot access the huge amount of data manually, so for this here comes machine learning. - With the help of machine learning, we can save both time and money. - The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. - Following are some key points which show the importance of Machine Learning: o Rapid increment in the production of data o Solving complex problems, which are difficult for a human o Decision making in various sector including finance o Finding hidden patterns and extracting useful information from data. When should you use Machine Learning? - Hand-written rules and equations are too complex—as in face recognition and speech recognition - The rules of a task are constantly changing—as in fraud detection from transaction records. - The nature of the data keeps changing, and the program needs to adapt—as in automated trading, energy demand forecasting, and predicting shopping trends. Terminologies of Machine Learning -  Model - A model is a specific representation learned from data by applying some machine learning algorithm. A model is also called a hypothesis. (Computer program used to recognize patterns in data or make predictions)  Feature - A feature is an individual measurable property of our data. A set of numeric features can be conveniently described by a feature vector. Feature vectors are fed as input to the model. For example, in order to predict a fruit, there may be features like color, smell, taste, etc.  Target (Label) - A target variable or label is the value to be predicted by our model. For the fruit example discussed in the features section, the label with each set of input would be the name of the fruit like apple, orange, banana, etc.  Training - The idea is to give a set of inputs(features) and its expected outputs(labels), so after training, we will have a model (hypothesis) that will then map new data to one of the categories trained on.  Prediction - Once our model is ready, it can be fed a set of inputs to which it will provide a predicted output(label). But make sure if the machine performs well on unseen data, then only we can say the machine performs well. The figure shown below clears the above concepts- Types of learning / Classification of Machine Learning – At a broad level, machine learning can be classified into three types: 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning 1) Supervised Learning - - Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output. - The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not. - The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as human learning in presence of Supervisor or Teacher. - Supervised learning can be grouped further in two categories of algorithms: o Classification o Regression - Example - Consider the following data regarding patients entering a clinic. The data consists of the gender and age of the patients and each patient is labeled as “healthy” or “sick”. Gender Age Label M 48 sick M 67 sick F 53 healthy M 49 sick F 32 healthy M 34 healthy M 21 healthy 2) Unsupervised learning – - Unsupervised learning is a learning method in which a machine learns without any supervision. - The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. - The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. - In unsupervised learning, we don't have a predetermined result. The machine tries to find useful insights from the huge amount of data. - It can be further classifieds into two categories of algorithms: o Clustering o Association 3) Reinforcement learning – - Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. - The agent learns automatically with these feedbacks and improves its performance. - In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance. - The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning. Machine Learning Problem Categories – To solve a problem using machine learning or AI it is important to know how to categorize the problem. Categorizing the problem helps us to understand which tools we have available to help us solve problem. There are two main types of machine learning problems: supervised and unsupervised. - Supervised machine learning problems are problems where we want to make predictions based on a set of examples. (supervised machine learning problems have a set of historic data which we want to use to predict the future) - Unsupervised machine learning problems are problems where our data does not have a set of defined set of categories, but instead we are looking for the machine learning algorithms to help us organize the data. (unsupervised machine learning problems have a set of data which we are looking for machine learning to help us organize or understand.) Supervised – Within supervised machine learning we further categorize problems into the following categories: classification and regression Classification – A classification problem is a problem where we use data to predict in which category something falls into. In other words, we are trying to use data to make a prediction about a discrete set of values or categorizes. e.g. 1) analyzing an image to determine if it contains a car or a person, 2) analyzing medical data to determine if a certain person is in a high risk group for a certain disease or not. Examples of algorithms used for supervised classification problems are – 1) Naïve Bayes classifier 2) Support Vector Machines 3) Logistic Regression 4) Neural Networks Regression – Regression problems are the problems where we try to make a prediction on a continuous scale. e.g. 1) Predicting the stock price of a company based on historical data, 2) Predicting the tomorrow temperature based on historical data. Examples of algorithms used for supervised regression problems are – 1) Linear Regression 2) Nonlinear Regression 3) Bayesian Linear Regression Unsupervised – Unsupervised machine learning problems are problems where we have little or no idea about the results. We are basically providing the machine learning algorithms with data and asking it to look for hidden features of data and cluster the data in a way that makes sense based on the data. e.g. 1) Genomics – In this, we provide an algorithm with thousands of different genes and the algorithm will then cluster the genes into groups of related genes. This could be genes related to lifespan, hair color etc. 2) Isolation of sounds in audio file - In this, we provide an algorithm with audio files and asking the algorithm to identify features within these audio files. These types of algorithms are able to isolate voices, music and other distinct features Examples of algorithms used for unsupervised problems are – 1) K-means clustering 2) Neural Networks Applications of Machine Learning – We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning: 1) Image Recognition – It is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. Popular use case - Automatic friend tagging suggestion. 2) Speech Recognition - While using Google, we get an option of “Search by voice”, it comes under speech recognition and it is a popular application of machine learning. Speech recognition is a process of converting voice instructions into text. 3) Traffic Prediction – If we want to visit a new place, we take help of Google Maps, which shows path with shortest route and predicts the traffic conditions. It predicts the traffic conditions such as whether traffic is cleared, slow moving or heavily congested with the help of two ways – a. Real time location of vehicle from Google Map and sensors b. Average time has taken on past days at same time Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends back to its database to improve the performance. 4) 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. 5) Self-driving cars – One of the most exciting applications of ML is self-driving cars. Ml plays a significant role in self-driving cars. e.g. Tesla car – it is using unsupervised learning method to train the car models to detect people and objects while driving. 6) Medical Diagnosis – In medical field, machine learning is used for diseases diagnosis. e.g. Tumor detection. 7) Stock Market Trading – Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in share prices, so for this machine learning’s long short term memory (LSTM) neural network is used for the prediction of stock market trends. Machine Learning Life Cycle – Machine learning has given the computer systems the abilities to automatically learn without being explicitly programmed. But how does it work? It can be described using the lifecycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose is to find a solution to the problem. Machine learning life cycle involves seven major steps, which are given below – o Gathering Data o Data preparation o Data Wrangling o Analyze Data o Train the model o Test the model o Deployment The most important thing in the complete process is to understand the problem and to know the purpose of the problem. Therefore, before starting the life cycle, we need to understand the problem because the good result depends on the better understanding of the problem. In the complete life cycle process, to solve a problem, we create a machine learning system called "model", and this model is created by providing "training". But to train a model, we need data, hence, life cycle starts by collecting data. 1. Gathering Data: Data Gathering is the first step of the machine learning life cycle. The goal of this step is to identify and obtain all data-related problems. In this step, we need to identify the different data sources, as data can be collected from various sources such as files, database, internet, or mobile devices. The quantity and quality of the collected data will determine the efficiency of the output. The more will be the data, the more accurate will be the prediction. This step includes the below tasks:  Identify various data sources  Collect data  Integrate the data obtained from different sources By performing the above task, we get a coherent set of data, also called as a dataset. It will be used in further steps. 2. Data Preparation: After collecting the data, we need to prepare it for further steps. Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training. In this step, first, we put all data together, and then randomize the ordering of data. This step can be further divided into two processes: Data exploration: It is used to understand the nature of data that we have to work with. We need to understand the characteristics, format, and quality of data. In this, we find Correlations, general trends, and outliers. Data pre-processing: In this step, process the raw data for its analysis. 3. Data Wrangling: Data wrangling is the process of cleaning and converting raw data into a useable format. It is the process of cleaning the data, selecting the variable to use, and transforming the data in a proper format to make it more suitable for analysis in the next step. It is one of the most important steps of the complete process. Cleaning of data is required to address the quality issues. It is not necessary that data we have collected is always of our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including: o Missing Values o Duplicate data o Invalid data o Noise  So, we use various filtering techniques to clean the data. It is mandatory to detect and remove the above issues because it can negatively affect the quality of the outcome. 4. Data Analysis: Now the cleaned and prepared data is passed on to the analysis step. This step involves: o Selection of analytical techniques o Building models o Review the result The aim of this step is to build a machine learning model to analyze the data using various analytical techniques and review the outcome. It starts with the determination of the type of the problems, where we select the machine learning techniques such as Classification, Regression, Cluster analysis, Association, etc. then build the model using prepared data, and evaluate the model. Hence, in this step, we take the data and use machine learning algorithms to build the model. 5. Train Model: In this step we train our model to improve its performance for better outcome of the problem. We use datasets to train the model using various machine learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features. 6. Test Model: Once the machine learning model has been trained on a given dataset, then we test the model. In this step, we check for the accuracy of our model by providing a test dataset to it. Testing the model determines the percentage accuracy of the model as per the requirement of project or problem. 7. Deployment: The last step of machine learning life cycle is deployment, where we deploy the model in the real-world system. If the above-prepared model is producing an accurate result as per our requirement with acceptable speed, then we deploy the model in the real system. But before deploying the project, we will check whether it is improving its performance using available data or not. Performance Measures in Machine Learning – Evaluating the performance of a Machine learning model is one of the important steps while building an effective ML model. To evaluate the performance or quality of the model, different metrics are used, and these metrics are known as performance metrics or evaluation metrics. These performance metrics help us understand how well our model has performed for the given data. In this way, we can improve the model's performance by tuning the hyper-parameters. Each ML model aims to generalize well on unseen/new data, and performance metrics help determine how well the model generalizes on the new dataset. In machine learning, each task or problem is divided into classification and Regression. Not all metrics can be used for all types of problems; hence, it is important to know and understand which metrics should be used. Different evaluation metrics are used for both Regression and Classification tasks. 1. Performance Metrics for Classification- In a classification problem, the category of data is identified based on training data. The model learns from the given dataset and then classifies the new data into classes or groups based on the training. The most commonly used Performance metrics for classification problem are as follows, o Accuracy o Confusion Matrix o Precision o Recall o F-Score o AUC (Area Under the Curve)-ROC I) Accuracy - The accuracy is the simple ratio between the number of correct predictions to the total number of predictions. It can be formulated as: It is simple to use; it is suitable only for cases where an equal number of samples belong to each class. When to use accuracy? It is good to use the Accuracy metric when the target variable classes in data are approximately balanced. For example, if 60% of classes in a fruit image dataset are of Apple, 40% are Mango. In this case, if the model is asked to predict whether the image is of Apple or Mango, it will give a prediction with 97% of accuracy. When not to use accuracy? It is recommended not to use the Accuracy measure when the target variable majorly belongs to one class. For example, suppose there is a model for a disease prediction in which, out of 100 people, only five people have a disease, and 95 people don't have one. In this case, if our model predicts every person with no disease (which means a bad prediction), the Accuracy measure will be 95%, which is not correct. II) Confusion Matrix - A confusion matrix is a tabular representation of prediction outcomes of any binary classifier, which is used to describe the performance of the classification model on a set of test data when true values are known. A typical confusion matrix for a binary classifier looks like the below image – We can determine the following from the above matrix:  In the matrix, columns are for the prediction values, and rows specify the Actual values. Here Actual and prediction give two possible classes, Yes or No. So, if we are predicting the presence of a disease in a patient, the Prediction column with Yes means, Patient has the disease, and for NO, the Patient doesn't have the disease.  In this example, the total number of predictions are 165, out of which 110 time predicted yes, whereas 55 times predicted No.  However, in reality, 60 cases in which patients don't have the disease, whereas 105 cases in which patients have the disease. In general, the table is divided into four terminologies, which are as follows: 1. True Positive(TP): In this case, the prediction outcome is true, and it is true in reality, also. 2. True Negative(TN): in this case, the prediction outcome is false, and it is false in reality, also. 3. False Positive(FP): In this case, prediction outcomes are true, but they are false in actuality. 4. False Negative(FN): In this case, predictions are false, and they are true in actuality. III) Precision - The precision determines the proportion of positive prediction that was actually correct. It can be calculated as the True Positive or predictions that are actually true to the total positive predictions (True Positive and False Positive). IV) Recall or Sensitivity - It aims to calculate the proportion of actual positive that was identified incorrectly. It can be calculated as True Positive or predictions that are actually true to the total number of positives, either correctly predicted as positive or incorrectly predicted as negative (true Positive and false negative). The formula for calculating Recall is given below: When to use Precision and Recall? The recall determines the performance of a classifier with respect to a false negative, whereas precision gives information about the performance of a classifier with respect to a false positive. So, if we want to minimize the false negative, then, Recall should be as near to 100%, and if we want to minimize the false positive, then precision should be close to 100% as possible. In simple words, if we maximize precision, it will minimize the FP errors, and if we maximize recall, it will minimize the FN error. V) F-scores - F-score or F1 Score is a metric to evaluate a binary classification model on the basis of predictions that are made for the positive class. It is calculated with the help of Precision and Recall. It is a type of single score that represents both Precision and Recall. So, the F1 Score can be calculated as the harmonic mean of both precision and Recall, assigning equal weight to each of them. The formula for calculating the F1 score is given below: When to use F-score? F-score make use of both precision and recall, so it should be used if both of them are important for evaluation, but one (precision or recall) is slightly more important to consider than the other. For example, when False negatives are comparatively more important than false positives, or vice versa. VI) AUC-ROC - Sometimes we need to visualize the performance of the classification model on charts; then, we can use the AUC-ROC curve. It is one of the popular and important metrics for evaluating the performance of the classification model. ROC (Receiver Operating Characteristic curve) represents a graph to show the performance of a classification model at different threshold levels. The curve is plotted between two parameters, which are: o True Positive Rate o False Positive Rate TPR or true Positive rate is a synonym for Recall, hence can be calculated as: TPR = TP / (TP +FN) FPR or False Positive Rate can be calculated as: FPR = FP / (FP +TN) To calculate value at any point in a ROC curve, we can evaluate a logistic regression model multiple times with different classification thresholds, but this would not be much efficient. So, for this, one efficient method is used, which is known as AUC. AUC: Area Under the ROC curve – AUC stands for Area Under the ROC curve. AUC calculates the two-dimensional area under the entire ROC curve, as shown below image: AUC calculates the performance across all the thresholds and provides an aggregate measure. The value of AUC ranges from 0 to 1. It means a model with 100% wrong prediction will have an AUC of 0.0, whereas models with 100% correct predictions will have an AUC of 1.0.  When to use AUC – AUC should be used to measure how well the predictions are ranked rather than their absolute values. Moreover, it measures the quality of predictions of the model without considering the classification threshold. When not to use AUC - As AUC is scale-invariant, which is not always desirable, and we need calibrating probability outputs, then AUC is not preferable. Further, AUC is not a useful metric when there are wide disparities in the cost of false negatives vs. false positives, and it is difficult to minimize one type of classification error. 2. Performance Metrics for Regression- Regression is a supervised learning technique that aims to find the relationships between the dependent and independent variables. A predictive regression model predicts a numeric or discrete value. The metrics used for regression are different from the classification metrics. It means we cannot use the Accuracy metric (explained above) to evaluate a regression model; instead, the performance of a Regression model is reported as errors in the prediction. Following are the popular metrics that are used to evaluate the performance of Regression models. o Mean Absolute Error o Mean Squared Error o R-squared Score o Adjusted R- squared I) Mean Absolute Error- Mean Absolute Error or MAE is one of the simplest metrics, which measures the absolute difference between actual and predicted values, where absolute means taking a number as Positive. The below formula is used to calculate MAE: Here, Y is the Actual outcome, Y' is the predicted outcome, and N is the total number of data points.  II) Mean Squared Error - Mean Squared error or MSE is one of the most suitable metrics for Regression evaluation. It measures the average of the Squared difference between predicted values and the actual value given by the model. Since in MSE, errors are squared, therefore it only assumes non-negative values, and it is usually positive and non-zero. The formula for calculating MSE is given below: Here, Y is the Actual outcome, Y' is the predicted outcome, and N is the total number of data points. III) R-squared Score – It is also known as Coefficient of Determination. It compares the model with constant baseline to determine performance of model. To select constant baseline, take mean of data and draw the line at mean. It always be less than or equal to 1 without concerning if values are too large or small. It is calculated as – IV) Adjusted R-squared – It is the improved version of R-squared error. The problem with R-square is that R-Square value always increases with an increase in independent variables irrespective of whether the independent variable is contributing to the model or not. Adjusted R-Square overcome this issue by adjusting values of independent variable. It is calculated as follows – Here, n is the number of observations k denotes the number of independent variables and Ra2 denotes the adjusted R2 Machine Learning Tools and Frameworks – There are different tools, software, and platform available for machine learning, and also new software and tools are evolving day by day. Although there are many options and availability of Machine learning tools, choosing the best tool for model is a challenging task. If we choose the right tool for our model, we can make it faster and more efficient. Some of the popular and commonly used tools are – TensorFlow - TensorFlow is one of the most popular open-source libraries used to train and build both machine learning and deep learning models. It provides a JS library and was developed by Google Brain Team. It offers a powerful library, tools, and resources for numerical computation, specifically for large scale machine learning and deep learning projects. For training and building the ML models, TensorFlow provides a high-level Keras API. Features –  TensorFlow enables us to build and train our ML models easily.  It also enables you to run the existing models using the TensorFlow.js  It helps in building a neural network.  Provides support of distributed computing.  It also enables the developers to perform numerical computations using data flow graphs.  It enables to easily deploy and training the model in the cloud. PyTorch - PyTorch is an open-source machine learning framework, which is based on the Torch library. This framework is free and open-source and developed by FAIR (Facebook's AI Research lab). It is one of the popular ML frameworks, which can be used for various applications, including computer vision and natural language processing. Features –  It enables the developers to create neural networks using Autograde Module.  It is more suitable for deep learning researches with good speed and flexibility.  It can also be used on cloud platforms.  It also provides a dynamic computational graph Google Cloud ML Engine - It is a hosted platform where ML developers and data scientists build and run optimum quality machine, learning models. It provides a managed service that allows developers to easily create ML models with any type of data and of any size. Features –  Provides machine learning model training, building, deep learning and predictive modelling.  It can be widely used to train a complex model.  Amazon Machine Learning - Amazon Machine Learning (AML) is a cloud-based and robust machine learning software application, which is widely used for building machine learning models and making predictions. Moreover, it integrates data from multiple sources, including Redshift, Amazon S3, or RDS. Features –  AML offers visualization tools and wizards.  Enables the users to identify the patterns, build mathematical models, and make predictions.  It enables the user to retrieve predictions with the help of batch APIs for bulk requests or real- time APIs for individual requests. Apache Mahout - Apache Mahout is an open-source project, which is used for developing machine learning applications mainly focused on Linear Algebra. It provides libraries to perform Mathematical operations mainly based on linear algebra and statistics. Features –  It enables developers to implement machine learning techniques, including recommendation, clustering, and classification.  It consists of matrix and vector libraries.  It provides support for multiple distributed backends.  It runs on top of Apache Hadoop using the MapReduce paradigm. Data Visualization – Data Visualization helps to understand data patterns, relationships and trends. Through data visualization, insights and patterns in data can be easily interpreted. Data visualization helps machine learning analysts to better understand and analyze complex data sets by presenting them in an easily understandable format. Data visualization is an essential step in data preparation and analysis as it helps to identify outliers, trends, and patterns in the data that may be missed by other forms of analysis. Machine learning algorithms work best when they have high-quality and clean data, and data visualization can help to identify and remove any inconsistencies or anomalies in the data. Types of Data Visualization Approaches – Machine learning may make use of a wide variety of data visualization approaches. That include: Line Charts - In a line chart, each data point is represented by a point on the graph, and these points are connected by a line. We may find patterns and trends in the data across time by using line charts. Time-series data is frequently displayed using line charts. Scatter Plots - A quick and efficient method of displaying the relationship between two variables is to use scatter plots. With one variable plotted on the x-axis and the other variable drawn on the y-axis, each data point in a scatter plot is represented by a point on the graph. We may use scatter plots to visualize data to find patterns, clusters, and outliers. Bar Charts - Bar charts are a common way of displaying categorical data. In a bar chart, each category is represented by a bar, with the height of the bar indicating the frequency or proportion of that category in the data. Bar graphs are useful for comparing several categories and seeing patterns over time. Heat Maps - Heat maps are graphical representation that displays data in a matrix format. The value of the data point that each matrix cell represents determines its hue. Heatmaps are often used to visualize the correlation between variables or to identify patterns in time-series data. Tree Maps - Tree maps are used to display hierarchical data in a compact format and are useful in showing the relationship between different levels of a hierarchy. Box Plots - Box plots are a graphical representation of the distribution of a set of data. In a box plot, the median is shown by a line inside the box, while the center box depicts the range of the data. The whiskers extend from the box to the highest and lowest values in the data, excluding outliers. Box plots can help us to identify the spread and skewness of the data. Uses of Data Visualization in Machine Learning – Data visualization has several uses in machine learning. It can be used to:  Identify trends and patterns in data: It may be challenging to spot trends and patterns in data using conventional approaches, but data visualization tools may be utilized to do so.  Communicate insights to stakeholders: Data visualization can be used to communicate insights to stakeholders in a format that is easily understandable and can help to support decision-making processes.  Monitor machine learning models: Data visualization can be used to monitor machine learning models in real time and to identify any issues or anomalies in the data.  Improve data quality: Data visualization can be used to identify outliers and inconsistencies in the data and to improve data quality by removing them. Challenges in Data Visualization– While data visualization is a powerful tool for machine learning, there are several challenges that must be addressed. The following list of critical challenges is provided.  Choosing the Right Visualization: One of the biggest challenges in data visualization is selecting the appropriate visualization technique to represent the data effectively. There are numerous visualization techniques available, and selecting the right one requires an understanding of the data and the message that needs to be conveyed.  Data Quality: Data visualization requires high-quality data. Inaccurate, incomplete, or inconsistent data can lead to misleading or incorrect visualizations. When displaying the data, it is crucial to make sure it is accurate, consistent, and comprehensive.  Data Overload: Another challenge in data visualization is handling large and complex datasets. When dealing with large amounts of data, it can be difficult to find meaningful insights, and visualizations can quickly become cluttered and difficult to read.  Audience Understanding: Another challenge in data visualization is ensuring that the target audience can interpret and understand the visualizations. Visualizations should be designed with the audience in mind and should be clear and concise.  Technical Expertise: Creating effective data visualizations often requires technical expertise in programming and statistical analysis. Data analysts and data scientists need to be familiar with programming languages, visualization tools, and statistical concepts to create effective visualizations.