Machine Learning Blog Posts

BFS is a graph traversal technique to explore and analyse graphs. It systematically visits all the neighbouring vertices of a current vertex before moving on to the next level of vertices.   Read on to learn about BFS. Understanding Graph Traversal Algorithm in Data Structure Graph traversal algorithms in data structures are essential techniques for systematically visiting and exploring each node or vertex within a graph. They play a crucial role in understanding the relationships and connectivity present in complex data structures. Graph traversal algorithms facilitate the examination of graphs by navigating through their nodes in a specific order.  Graph traversal algorithms are widely used in network analysis, routing algorithms, and web crawling, among other fields. They empower efficient analysis and ease the decision-making processes. The two main approaches for graph traversal are:  DFS or the depth-first search algorithm (DFS)  BFS or the breadth first search algorithm  Unlike BFS, DFS explores a graph by traversing as far as possible along each branch before backtracking. What is Breadth First Search Algorithm? BFS algorithm starts from a given source vertex and explores the graph layer by layer, examining all the vertices at the same level before descending further. It uses a queue data structure to maintain the order of exploration, ensuring that vertices are visited in a breadth first manner. Understanding How BFS Algorithm Works  To implement BFS, a queue data structure is used to maintain the exploration order. The algorithm begins by enqueuing the source vertex and marking it as visited. Then, while the queue is not empty, it dequeues a vertex, visits its adjacent unvisited vertices, enqueues them, and marks them as visited. This process continues until all vertices have been visited or until the desired condition is met. Using this approach, BFS guarantees that vertices in the BFS graph are visited in order of their distance from the source vertex. Need for the Breadth First Search Algorithm There are several reasons why using the BFS algorithm is essential: Shortest Path Finding: BFS guarantees the shortest path between two vertices in an unweighted graph, making it an ideal choice for route planning or navigation systems. Completeness: BFS is complete for finite graphs, ensuring it explores the entire graph and visits all reachable vertices. Breadth-First Traversal: BFS explores vertices at the same level before moving to deeper levels, providing a breadth-first exploration of the graph. Minimal Memory Usage: BFS uses minimal memory compared to other graph traversal algorithms like DFS, as it only needs to store vertices in the queue. Optimal Solution and Accuracy: In unweighted graphs, BFS ensures that the first occurrence of a target vertex will yield the shortest path, making it efficient for searching tasks. Programmers often use breadth first search Python for various applications in graph theory and data structures.  Gain an in-depth understanding of graph traversal algorithms and their structures with an MS in Full Stack AI and ML course. BFS Algorithm Rules Some important rules to remember when implementing the breadth first search algorithm for graph traversal: Queue: Use a queue data structure to keep track of the vertices to be visited in the breadth first traversal. Visited Marking: Maintain a visited array or set to keep track of the vertices that have been visited during the traversal. Enqueue and Mark: Enqueue the starting vertex into the queue and mark it as visited. Dequeue and Visit Neighbors: While the queue is not empty, dequeue a vertex, visit it, and enqueue its unvisited neighbouring vertices. Order of Visit: Visit the vertices in the order they were enqueued, ensuring a breadth-first exploration. Avoid Revisiting: Check if a vertex has already been visited before enqueueing it to avoid revisiting vertices. Termination: Terminate the algorithm when the queue becomes empty, indicating that all reachable vertices have been visited. Shortest Path: If finding the shortest path, track the parent of each vertex to reconstruct the path once the destination is reached. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification BFS Algorithm Architecture The architecture of the BFS algorithm is broken down below: Queue: The BFS algorithm uses a queue data structure to maintain an orderly process when exploring vertex positions. The queue follows the First-In, First-Out (FIFO) principle to ensure that vertices are processed in their order of arrival in the queue. Visited Array or Set: A visited array or set tracks the vertices visited during BFS traversal and ensures that each vertex is processed only once. It ensures no revisited vertices occur while processing each vertex correctly only once. BFS Tree: As the BFS algorithm explores the graph, it constructs a BFS tree. The BFS tree represents the traversal path and reveals the hierarchical relationships between vertices. Each vertex in the tree has its parent, the vertex discovered during the traversal. Enqueue and Mark: The BFS algorithm starts by enqueueing the source vertex into the queue and marking it as visited. Dequeue and Visit Neighbours: While the queue is not empty, the algorithm dequeues a vertex, visits it, and explores its neighbouring vertices. Each unvisited neighbour is enqueued into the queue and marked as visited. Termination: The BFS algorithm terminates when the queue becomes empty. This indicates that all reachable vertices have been visited and processed. Enrol for the Machine Learning Course from the World’s top Universities. Earn Master, Executive PGP, or Advanced Certificate Programs to fast-track your career. Pseudocode Of Breadth First Search Algorithm BFS(graph, start_vertex):     queue = create an empty queue     visited = create an empty set or array to track visited vertices     enqueue start_vertex into the queue     add start_vertex to visited     while queue is not empty:         current_vertex = dequeue from the queue         process(current_vertex) // e.g., print or perform operations on the current_vertex         for each neighbour in graph[current_vertex]:             if neighbour is not in visited:                 enqueue neighbour into the queue                 add neighbour to visited The BFS algorithm starts by initialising an empty queue and an empty set/array to track visited vertices. It begins the traversal from the start_vertex, which is enqueued into the queue and marked as visited. The algorithm then enters a while loop that continues as long as its queue remains nonempty. At each iteration, a vertex at the front of its queue (denoted by current_vertex) is removed and processed, either through operations on it or printing it out. Next, the algorithm explores all the neighbours of the current_vertex. For each unvisited neighbour, it enqueues the neighbour into the queue and marks it as visited. The process continues until the queue becomes empty, indicating that all reachable vertices from the start_vertex have been visited. You can also check out our free courses offered by upGrad in Management, Data Science, Machine Learning, Digital Marketing, and Technology. All of these courses have top-notch learning resources, weekly live lectures, industry assignments, and a certificate of course completion – all free of cost! BFS Algorithm Example Let’s consider the following graph to understand a BFS example:   A––B   |       |   C––D We want to perform a breadth first search (BFS) traversal from vertex A to visit all the vertices. Step 1: Enqueue vertex A into the queue. Mark A as visited. Queue: [A] Visited: {A} Step 2: Dequeue A from the queue and process it (e.g., print or perform operations). Enqueue its neighbours, B and C, into the queue (as they have yet to be visited). Mark B and C as visited. Queue: [B, C] Visited: {A, B, C} Step 3: Dequeue B from the queue and process it. Enqueue its neighbour D into the queue (as it is not visited yet). Mark D as visited. Queue: [C, D] Visited: {A, B, C, D} Step 4: Dequeue C from the queue and process it. There are no unvisited neighbours of C, so no vertices are enqueued. Queue: [D] Visited: {A, B, C, D} Step 5: Dequeue D from the queue and process it. There are no unvisited neighbours of D, so no vertices are enqueued. Queue: [] Visited: {A, B, C, D} The BFS algorithm has now traversed all vertices reachable from the starting vertex A in a breadth-first manner. The order of traversal is A, B, C, and D. Complexities Associated With Breadth First Search Algorithm  The complexity of the breadth first search algorithm can be analysed in terms of time and space complexity. 1.Time Complexity BFS has an O(V + E) time complexity, where V represents the number of nodes (vertices) in the graph, and E represents its edges. When dequeuing from its queue or exploring its adjacent vertices, BFS attempts to visit all nodes and edges once. Thus its time complexity increases linearly as more nodes and edges enter or exit it. 2. Space Complexity Space complexity for BFS grows linearly with the number of vertices in a graph (V), represented as an integer value. This is because even under extreme conditions, the BFS queue can simultaneously contain all vertices in its maximum level traversal. Furthermore, its visited array or set requires O(V) space to store visited vertices visited during traversal; hence BFS grows linearly in space complexity with each increase in graph vertex count. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Breadth First Search Algorithm Code Applications Here are a few examples of the numerous applications of the BFS algorithm in various domains, showcasing its versatility and usefulness in graph exploration and analysis: Shortest Path: BFS can be used to find the shortest path between two vertices in an unweighted graph. The shortest path can be reconstructed by tracking the parent nodes during traversal. Connectivity: BFS can determine whether a graph is connected or not. The graph is connected if BFS reaches all vertices from a given source vertex. Web Crawling: BFS is widely used in web crawling or web scraping. It helps explore web pages systematically by following links and discovering new pages at each level. Social Network Analysis: BFS can help analyse social networks, identify clusters, find the shortest path between users, or calculate measures like degrees of separation. Bipartite Graphs: BFS can determine whether a graph is bipartite or not. It can colour the vertices with two different colours such that no two adjacent vertices have the same colour. Minimum Spanning Tree: BFS can be used to find the minimum spanning tree (MST) of a connected, weighted graph when all edge weights are equal. Puzzle Solving: BFS can solve puzzles like the sliding tile puzzle or the maze problem, finding the shortest path to the goal state. Network Routing: BFS is used in network routing algorithms, such as finding the shortest path between routers or determining the best route for data packets. Conclusion The Breadth First Search (BFS) algorithm is an invaluable tool for exploring and analysing graphs in a breadth-first manner. Its efficiency, accuracy in finding the shortest path, and versatility in various applications make it a fundamental technique in data structures and algorithms.  Aspiring IT professionals or software developers looking to enhance their skills and master graph traversal algorithms like DFS and BFS can check out upGrad’s Advanced Certificate Programme in Machine Learning & NLP from IIITB. Put yourself at the forefront of a technologically evolving landscape with upGrad.

For those who are familiar with the technologies, the difference between CPU and GPU is relatively simple. However, to better understand the differences, we must enumerate them to appreciate their applications fully. Generally, GPUs are used to take on added functions to what the CPUs already execute. In reality, though, often, it is the GPU that is the driving force behind machine learning and Artificial Intelligence. Let us now look at the core differences between CPU vs GPU in machine learning. Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. CPU vs GPU CPU stands for Central Processing Unit. It functions much like the human brain does in our bodies. It takes the form of a microchip which is placed on the motherboard. It receives data, executes commands, and processes information that other computers, devices, and software components send. In how they are created, CPUs are best for sequential processing and scalar processing, which allows multiple different operations on the same data set.  GPU is short for Graphics Processing Unit. In most computer models, the GPU is integrated into the CPU. Its role is to take care of processes that the CPU cannot i.e., intense graphics processing. While the CPU can only execute a limited number of commands, GPU can manage thousands of commands in parallel. This happens because it is processing the same operation on multiple sets of data. GPUs are built on Single Instruction Multiple Data (SIMD) architecture, and they employ vector processing to arrange inputs into data-streams so that all of them can be processed at once.  Thus, having established the core difference between CPU and GPU, we have learned that they process different data pieces, and now we can look at CPU vs GPU in machine learning. While CPUs can handle graphic functions, GPUs are ideal for them as they are optimized for the required fast-paced computation. For the rendering of 3D figures in games, GPUs were primarily used till very recently. However, due to new research into them, the application area has significantly broadened.  Check out upGrad’s Advanced Certification in DevOps  The Application of Graphics in Machine Learning  Machine learning and artificial intelligence often invoke images from science fiction in us. We dream of the robots of Terminator or the supercomputers of Asimov. However, the reality is slightly more prosaic. It involves things like business intelligence and analytics shortcuts. They are in the line of steady progression that began from supercomputers like Deep Blue. Deep Blue was a computer that beat Gary Kasparov, the then chess champion. It was called a supercomputer because it had 75 teraflops of processing power, which took up the equivalent of several racks over a large floorspace. Today, a graphics card holds around 70 teraflops of processing power. When used on a computer, it uses 2000-3000 cores. By way of comparison, this single GPU chip can handle up to 1000 times more data than a traditional CPU chip.  It is also important to note that CPUs and GPUs add to our existing capabilities. We could do all the functions they do without having to resort to them. But the benefit they bring is that they make everything easier and faster. Think about physical mail versus actual mail. Both can be done, but the latter is undoubtedly quicker and easier. Therefore, machine learning is nothing but doing the same work we are doing but in an augmented setting. Machines can do tasks and calculations over a matter of days that would take us a lifetime or more otherwise.  Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Machine Learning Cases Concerning GPUs Machine learning borrows heavily from the Darwinian Theory of Evolution. It takes into account any analysis on big data what the previously leanest and fastest solution was. It saves this iteration for future analysis. As an example, a local business wants to analyze a data set for local customers. When it begins the first set, it will not know what any of the data means. But based on the continued purchases, each simulation can be compared to keep the best and discard the rest. Online sites such as Google and YouTube use this feature often. It takes historical data and creates a trend based on that for recommended pages and videos. For example, if you watch a “cute cat video”, the machine has learned from the experience of site patterns and user behavior what it should recommend next to you. Similarly, once you establish your trends based on continuous usage, that also gets factored into what they learn. This same principle is at work on e-commerce sites like Amazon and Facebook. If you search for football-related products, the next ads you will see are similar in nature to it. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Choosing the correct GPU GPUs, as we have established, work better for machine learning. But even when selecting a GPU, we must choose the best option available for our needs. The determinant factor when selecting a GPU is primarily on the type of calculations that need to be done. There are two types of precision calculations a GPU can do depending on the number of places it can make calculations up to. These are known as Single Floating Point and Dual Floating Point precision types. Single Precision Floating Points take up 32 bits of computer memory compared to the Dual Precision Floating Points, which occupy 64 bits. Intuitively, it shows that the Dual Precision Floating Points can undertake more complex calculations and therefore have an increased range. However, because of the same reason, they require a higher grade of a card to run, and they also take more time because often, the data being computed is based on higher-level mathematics. If you are not a developer yourself, one should reconsider before going in for these high-end technologies. No one size fits all requirements. Each computer needs to be customized based on the data set that needs to be analyzed. Furthermore, hardware requirements like power and cooling are also important considerations and can use up between 200-300 watts. Sufficient cooling racks and air-coolers need to be present to balance the heat generated because the heat can end up affecting your other devices. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? 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Data is also called the new ‘oil’ of this century. Meaning data is as precious for the functioning of a business in the 21st century as crude oil was at the start of the 20th. Much as oil has become an essential part of human civilization, data is also proving to become one. Activities related to its collection, manipulation, and presentation are gaining more and more prominence.  Since businesses are increasingly being more and more dependent on data, new techniques to handle the data above have evolved. Data Science, Data Analytics, Machine Learning, Data Engineering and others are some fields of studies. These train an individual in specific data handling techniques for a specific role in the data handling process.  Machine Learning and Data Analytics are two such related but different fields, and before exploring the question – machine learning vs data analytics, a basic understanding of the terms is necessary. Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Data Analytics – What is it? Inferring by its name, one would think that data analytics must be related to the act of ‘analyzing’ data, and he would be correct. Data Analytics is the ‘analyzing’ of data, but analyzing is a very broad term, so let’s briefly get an overview of what this ‘analyzing’ involves and how it works. Collection of data – A set of figures and associated parameters are collected. Data analytics does not cover the collection of actual data but rather complies with the collected data from various sources. For example, four companies have conducted a similar survey in 4 different regions; data analytics compile all four similar datasets into one file in the database for processing. Processing of data – Data processing is how data related to particular specified parameters are extracted from the raw database file. This extraction is performed by utilizing certain functions embedded in data processing software or by running a script (program) on the data entries. E.g., if one wants to find the age of the people who participated in the four surveys, he would process the data solely on the parameters of age. Data cleaning – The next step is to clear the duplication of entries, errors or incomplete data from the ‘data pool’ related to those parameters. To achieve these certain limits, benchmarks and formats are present in the system. For example, the applicant’s previous survey age limit should be positive and below 120;  the algorithm would eliminate any negative entry or entry exceeding 120. Application Statistical and modelling techniques – The calculation of  KSI (The key statistical Indicators) of the data, and modelling of certain graphs, charts, tables etc., visual communicators and others. E.g. For the above survey the respondents average age in the survey for the region, 1,2,3,4 can be depicted in the form of a chart. Moving on to the other half of the question, machine learning vs data analytics. Check out upGrad’s Advanced Certification in DevOps  Machine learning – What is it? Again, as evident from the name, it involves how the machine learns by itself. The problem is that machines are not as sentient as humans; thus, machine learning involves the algorithms or codes that would amend themselves according to the feedback requested and input/data received. One such example of machine learning in everyday use is E-mail clients, which classify some of the received e-mails as ‘spams’; here, the input is the content of the e-mail. For feedback, the algorithm may scan the document for certain parameters such as ‘sale’, ‘offer’, etc. and combine it with the information whether the sender is in the receiver’s contact list. Other factors such as the mail being cc (carbon copy) or bcc to many people would decide the feedback as being ‘spam’ or ‘not spam. Over time, the algorithm may include more words to scan for in its database by analyzing the receiver’s e-mails manually marked as ‘being spam’ and moving the e-mails from frequent ‘spammers’ directly into the ‘trash bin’. Several models are available for implementing machine learning, with new models experimented on and released each year. Part of it has to do with rapid advancements in the hardware types of equipment and digitization processes. Some of the popular models are – Artificial Neural Networks – A collection of various Machine Learning programs interacting with each other. Decision tree model – A logical progression of tasks. With several branches of outcomes for several different inputs or logical conditions. Regression analysis – Developing a relationship between input and output and tailoring the output to match their averages. This ability of a program/algorithm to apply its learned knowledge is very beneficial to the industry. Some of its applications are automated chat boxes on websites, automating the user’s routine tasks, prediction based on data, checking receipts, theorem proving, optimization of the process based on feedback. Now that both the terms are clear, comparing them. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Machine Learning vs Data Analytics  A quick comparison between machine learning vs data analytics is done on the following parameters –  Modification in the algorithm/ program For any modification in the algorithm of Data Analytics, the changes have to be entered manually. Whereas for machine learning, the changes are made by the algorithm without any external intervention. Handling raw data  One thing that Data analytics does phenomenally better is data handling. All sorts of data handling are possible – It can prune data by removing faulty, repeated, empty data sets and arranged in a neat table, graphs and whatnot. Moreover –  Data can be filtered by a certain parameter or variable. It can make certain variables correlated with each other. Statistical functions such as – moving averages, skewness, medians, modes, etc., can also be obtained from the data. On the other hand, Machine learning cannot handle raw data. It makes sense, because Data analytics has been around far longer than Machine Learning, so instead of designing Data Analytics algorithms into machine learning, one can separately use a data analytics tool. However, several softwares provide the functionalities of both into one package. Feedback There is no such concept of ‘feedback’ in Data Analysis; it more or less operates on the ‘input-output basis. One enters the input (data), selects a suitable modifier (function) and gets an appropriate output (result). There is no modification in the modifier (function) based on the result. On the other hand, Machine learning follows the same routine. After generating the output, the algorithm can make changes by analyzing the relationship between the input and the user’s interactions. Predicting Data Analytics cannot make predictions based on a data set. It may model the data establishing various correlations between variables and represent them but cannot estimate the next set of variables based on the trends in a number of the previous set of variables.  Machine learning, on the other hand, can do it effortlessly. All it needs is a large enough collection of previous datasets for analysis. Machine Learning finds application in data analytics for this specific purpose only. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Applications Data analytics has a highly specific purpose – to collect, clean, process and model the data.  As such, it has comparatively limited applications. Some applications include providing information to help in the management’s decision-making, Serving as a proof of opinion, delivering facts to the public, and compiling the financial statements and others. On the other hand, a machine’s ability to adapt without any external help has tremendous applicability. Machine learning is applicable in any field where there is a need for ‘customization’ of the process according to an individual or the elimination of manual processes favouring an automated one. One such example of its usage is in data analytics itself. That being said, Machine learning is a comparatively new field of study. As such, there is a lot more to be done in terms of innovation, applicability and marketability of the machine learning techniques. SO, for a common task, the industry is biased towards data analytics than machine learning. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? 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There are hosts of software suites for machine learning, the most common of them are – Amazon Machine Learning Kit, Azure Machine Learning, Google Prediction API, MATLAB, RCASE, IBM Watson Studio and KNIME, to name a few. After a brief study of the answer to the question machine learning vs data analytics, written above, one can easily observe that machine learning is a much more potent tool and flexible tool with diverse applications. However, one can also conclude that they both have a specific role in the business industry. There are some functions, such as processing raw data, that only data analytics can perform and then there is a certain function such as Prediction that only machine learning can perform. So, each one has its importance and applications, and although sometimes one may work better than the other for a specific task, they both are much needed by the industries. At upGrad, our Advanced Certificate in Machine Learning and Deep Learning, offered in collaboration with IIIT-B, is an 8-month course taught by industry experts to give you a real-world idea of how deep learning and machine learning work. In this course, you’ll get a chance to learn important concepts around machine learning, deep learning, computer vision, cloud, neural networks, and more.  Check out the course page and get yourself enrolled soon! 

Is machine learning possible without maths? Absolutely Not. Machine learning is entirely about maths. It is an application of artificial intelligence that uses raw data, processes it, and further builds a model or conclusion.  As imagining what an item would look like three-dimensionally just by looking at a picture. It is all about understanding and reasoning. How is machine learning possible? Well, that’s because a lot of data is transmitted and generated every second of the day. Even Right now, when you’re reading this, some information is being developed. This data is further used for analysis, and at the end, conclusions are drawn. It is Fun, and one can relate it in our daily life by wanting to know why something works and how. There are very few who have not been impacted by artificial intelligence in today’s world. Because we encounter it in some or the other way, be it in healthcare, screen lock, photo tagging, Online Shopping etc. Each concept learnt in this field is in some or the other way related to mathematics, either directly or indirectly. Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Maths For Machine Learning To understand maths for machine learning, you must Excel in the following topics- 1)   Statistics 2)   Multivariate Calculus 3)   Linear algebra 4)   Probability These are the four pillars. Let’s understand each of them in detail, as all these are equally essential to building an algorithm and solving real-life problems. Machine Learning is all about working with data. For every modification performed on data, there is one bridge that helps us reach our goals through computation, and that is math. Check out upGrad’s Advanced Certification in DevOps  1)   Statistics- This topic is more familiar to us than the others, which we will be covered because we have been learning this since high school, and it is the most critically important component of maths for machine learning. It is the application of probability theory and is used for drawing conclusions from the data which has been collected. It is playing with the raw data to get the findings from it.   The first step is the collection of data. It is possible through 2 sources- Primary source and Secondary source. This is the foundation for our further steps.   The data collected is raw, and it needs some processing to make it meaningful and valuable. The data is processed, and information is extracted from it.   The processed data should be represented in a manner that is easy to read and understand.   Lastly, conclusions are drawn from the data collected because just numbers are not enough! There are two types of statistics used in machine learning- A)  Descriptive statistics- Descriptive statistics is a measure that summarises the processed data for ease of visualization, and it can be presented in a manner that is meaningful and understandable. B)   Inferential statistics- It allows you to make conclusions based on the data taken from the population and also give reasoning. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 2)   Probability- To start from scratch, the probability is the chance or likelihood of the occurrence of a particular event to happen. In machine learning, it is used in predicting the possibility of a specific event happening. The probability of an event is calculated as-. P(event)= favourable outcomes/ total number of possible outcomes Some basic concepts of probability are-   Joint probability- It is a measure that shows how much are the chances of two different events taking place simultaneously. It is denoted by P(A∩B )-   Conditional probability- Conditional probability means the chances of some event occurring given that another event has already happened. It is denoted by P(A|B)   Bayes theorem- It gives results on the probability of an event based on new information. It renews a set of old chances with the new one ( after adding additional information) to derive a new set of possibilities. Bayes theorem helps us to understand the Confusion Matrix. It is also known as the error matrix in the field of machine. It is a method used for extracting the results of the performance of a classification model. A comparison is made between the actual and predicted classes. It has four outcomes- True Positive (TP): predicted values = predicted actual positive      False-positive (FP): Negative values predicted as positive      False-negative (FN): Positive values predicted as negative      True negative (TN): Predicted values = predicted actual negative Machine learning professionals use this concept to note down inputs and predict possible outcomes. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau 3)   Multivariate Calculus- Multivariate calculus is also known as multivariable calculus. It is an intrinsic field of maths in machine learning algorithms, and without understanding this, you cannot think of going any further. It is the branch that tells us how to learn and optimize our models or algorithms. Without apprehending this concept, it is difficult to predict the outcomes from the data that has been collected. Multivariate Calculus is divided into two types which are-  Differential calculus- Differential calculus breaks the data into small pieces to know how it works individually. Inferential calculus- Inferential calculus glues the broken pieces to find how much there is. Some other types are Vector Values Function, Partial Derivatives, Hessian, Directional Gradient, Laplacian, Lagragian distribution. Multivariate Calculus is mainly used in enhancing the machine learning process. 4)   Linear algebra- Linear algebra is the backbone of machine learning. It makes running the algorithms feasible on substantial data sets. It also makes us understand the working of algorithms which we use in our daily life and help us make a better choice. There are quite a few tasks which cannot be done without the use of linear algebra. Which are-   Development of machine learning models.   Operation of complex data structures. Machine learning professionals use linear algebra to build their algorithms. Linear algebra is widely known as the mathematics of the 21st century, as many believe it will transform every industry in the future. It is a platform on which all the algorithms come together and lead to a result. Some machine learning algorithms are fundamental and should be applied to any data problem. They are as follows- 1)   Logistic regression 2)   Linear regression 3)   SVM (Support Vector Machine) 4)   Naïve Bayes 5)   Decision Tree 6)   KNN (K- Nearest Neighbour) 7)   K- means 8)   Dimensionality Reduction Algorithms 9)   Gradient Boosting Algorithms 10) Random Forest We need a plan for building a model because direct implementation will lead to a lot of errors. We need a high-level programming language such as Python to test our strategies and get better results than using the trial and error method, which is a very time-consuming process. Python is one of the best languages used for programming and software development. Importance of machine learning- Let’s think of one day without the use of artificial intelligence. Difficult, right? The applications provided have become part and parcel of our lives because of their ability to provide quick solutions to our problems and answering tedious questions effectively, efficiently and quickly. It is convenient and works as a saviour when a person is short on time. It also saves time, money and provides security. Tasks get done quickly and efficiently with not much physical movement. Our life cannot get easier. Making payments is just a few fingertips away. Privacy is protected through face lock and fingerprint lock. Features with which we play from day to night are all because of the gift of artificial learning. Every question in the world can be answered by Siri or Google assistant. It helps us to buy the best for ourselves. For instance, while purchasing a phone, one can compare one device better than the other and the algorithm behind it. The applications of it are never-ending like, use in google maps where it uses location data from smartphones, in riding apps like ola, uber in which we fix the price of our ride and minimize the waiting time, in commercial flights to use auto-pilot, in spam filters whenever we receive an email from an unknown address while giving smart replies in gmail- it automatically suggests replies to us, and most importantly in the bank to prevent fraud and check deposits on mobile. They are widely used in the healthcare department in machine learning; not only this, but we need maths right from sunrise to sundown because we make several transactions during a day. Our learning maths journey starts when we are in 11th and 12th grades, and when we start realizing that life is so unfair. At that time of life, you might wonder where I am going to use this math. Well, we use it here, and all the theoretical knowledge comes into practicality. The best way to get yourself fascinated in this field is by taking a machine learning algorithm and understanding why and how it works. Not everything which is helpful comes to you quickly. You have to make efforts to achieve it. Though maths for machine learning can be complex, once you excel in it, you can not only use it for work but also implement it in your daily life to understand the working of certain things. Many people still aren’t aware of how important it is to learn maths for machine learning as we saw some pointers on why and where we require mathematics not only in this field but also in our day-to-day life.  At upGrad, our Advanced Certificate in Machine Learning and Deep Learning, offered in collaboration with IIIT-B, is an 8-month course taught by industry experts to give you a real-world idea of how deep learning and machine learning work. In this course, you’ll get a chance to learn important concepts around machine learning, deep learning, computer vision, cloud, neural networks, and more.  Check out the course page and get yourself enrolled soon!   

Machine Learning in Java: Machine learning has taken over the industry and is rising at a rapid rate. Machine learning gives algorithms a chance to learn and grow without being further programmed. It sets its own parameters by using sample data so that it can perform a specific task on similar data. Machine learning is a trained algorithm that is used for a particular problem. However, we are still in the first wave of machine learning because the theory is still a lot more to come. From the face recognition software that we use on our phones to self-driving cars, google maps, google translate, and voice-controlled technologies are all part of machine learning. Over the next few years, new products with next-generation technology will be ruling the world.  Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What is machine learning exactly? We are just at the beginning of machine learning. Day by day, computing and machine learning is getting more powerful. As we speak, new algorithms are being formed to take over the world. We are surrounded by machine learning devices. For example, Siri or Alexa are devices that work on voice generation. We just need to ask them something, and they search the web and answer it for us. We do not have to take the trouble of opening a search engine and typing in the information we need, and looking for a correct answer. Another example of machine learning could be Netflix or Amazon; once we watch a specific movie genre or series, these websites come up with a list of recommendations of a similar genre.  Email classification is the most suitable way to explain how machine learning works? The main task is to determine if an email is spam or not. Spam mails cannot be easily identified just by looking at the subject or message. There are other things that need to be taken into account. The algorithm reads the data, classifies it into different categories, and looks for patterns. But with the help of Machine Learning, we do not have to manually separate the spam emails. It is already done for us.  Promotional emails are the same. It is directly sent into the promotional section of our mailbox. It saves us the trouble of going through a ton of mail and then by mistake scrolling through important mail. It helps us answer the important mail first as it is first shown in our inbox. Machine learning has made our daily life much easier. Now we have Robots that vacuum our floors while we can do some other work. It has taken technology to another level by coming up with self-driving cars and trains as it is the next big thing for the upcoming generation.  Machine learning is a branch of Artificial Intelligence, which is focused on building applications that learn from examples and experiences. Over time this software application learns from data and improves its accuracy without being programmed further. Algorithms are trained to find similar kinds of patterns in enormous amounts of data and make predictions accordingly. As the algorithm processes more data, the decisions and predictions become more accurate. Most of the algorithms that we come across today are based on Machine Learning in Java.  Check out upGrad’s Advanced Certification in DevOps  How does it work? A regular algorithm has been developed to form a machine learning algorithm. As it is made to learn and grow from data provided automatically. It has been categorized into three types: Supervised Learning: Supervised learning is the training process. It is the part where the algorithm has been trained to respond to various types of questions. It labels and classifies Data as it is received. For example, when we are kids just learning how to write, our teacher or parent used to guide our hands to make the proper shape of the alphabet. Similarly, this algorithm gets a set of training data and maps out the input and output variables of it. Once it has been trained, it can make decisions, respond and make predictions automatically.  Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Unsupervised Machine Learning: Machine learning gets a lot of unlabeled data. It then uses algorithms to cluster the data in different classes. It tries to take out meaningful features or patterns from this Data so that it can classify, label, and sort it without the help of a human. When we talk of Unsupervised Learning, the first thing that comes to our mind is making automatic predictions and decisions. But this is not the case, and here Unsupervised Machine Learning means identifying patterns and relationships among data that an average person would miss.  Reinforcement Learning: This type of learning is done by interacting with a particular environment. It follows the concept of trial and error. For example, a child during his/her early childhood years cannot differentiate between which objects are hot and which things are cold. If a child’s favorite dish is kept in a hot container and you tell the child that it is hot, but the child cannot understand what it means, on touching the container, they get burnt. It is then they realize that this means hot. In a similar way, the Reinforcement machine learning technique learns from the consequences of its actions. To find out the best possible outcome. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Why Machine Learning in Java: Java is one of the senior and most popular languages used in the world of programming. It is used for software development and for the development of Big Data ecosystems. It is easy to use and high in demand. If calculated roughly around the world, more than nine million developers use Java. Private and public sector enterprises have a codebase that uses JVM as a primary computing environment. Since Java is everywhere, it has a massive demand in the programming world. Python, R, etc., are other machine learning programming languages used. Even though they may be good but Java is not lagging behind. With the aid of a  third-party open-source library, any Java developer can apply machine learning and get into Data Science. Apache Spark and Apache Kafka use Java as their core programming language to deal with big data. Because of security and reliability reasons Java has been used by these platforms for the development of their data system.  Java applications have a ton of resources and community support. It is an object-oriented programming language that is portable and versatile. The first part of a machine learning process is a collection of Data. Therefore adequate machine learning tools are required. By choosing the proper machine learning tool and making careful decisions, the business will be able to make a profit.  Significant platforms and open resource machine learning libraries in Java:  Mahout: Apache Mahout is a distributed framework. It provides machine algorithms for a platform known as Apache Hadoop. With this framework, one can work with built-in algorithms. It allows Mathematicians, Data analysts, statisticians, and data scientists to use their custom made algorithms. Along with offering high performance, scalability, and flexibility, Mahout also focuses on clustering, classification, and recommendation systems. It also includes reference implemented algorithms that run on a single node. Mahout was majorly designed for the purpose of entertainment.   Java ML Java ML, also known as Java Machine Learning, is a collection of machine learning algorithms. It has a standard interface for algorithms of the same type. It has plenty of codes and tutorials directed for programmers and software engineers. Algorithms that are written clearly have proper documentation processes and can be used for references in the future. Java ML has many features, some of them being: Data manipulation, clustering, classification, documentation, and feature selection.  ADAMS ADAMS, also known as Advanced Data Mining and Machine Learning Systems. The main aim of ADAMS is to build and maintain processing, data-driven, mining, and visualization of data. It has a comprehensive collection of operators, also known as actors, that can retrieve information and process data. It provides the users with various unique features such as machine learning, visualization, data processing, streaming, scripting, and many more. By using a tree-like structure and following a philosophy of less is more, ADAMS is a powerful platform and Machine Learning in Java.  Deeplearning4j: Deeplearning4j is written in Java and is suitable for Java Virtual Machine Language such as Kotlin, Scala, etc.  Apache Spark and Hadoop, the latest computing frameworks, are a part of Deeplearning4j’s library. It brings Artificial Intelligence into business environments and has a Commercial-grade as well as an open-source library.  WEKA WEKA, also known as Waikato Environment for Knowledge Analysis. WEKA is a machine learning library that has an open-source which was developed in New Zealand. The name of this Machine learning library was inspired by a flightless bird that is found in New Zealand. It is by far the best and ongoing project. Currently, it is the best place to start machine learning. WEKA has a collection of algorithms and supports the deep learning technique. It has a number of machine learning tools for regression, classification, visualization, and Data mining. ELKI ELKI also stands for Environment for DeveLoping KDD Applications Supported for Index Structures. It was developed by the Ludwig Maximilian University of Munich, Germany.  It is a Java-based data mining framework used for the expansion of KDD applications. ELKI focuses on algorithm research that emphasizes outlier detection and cluster analysis. It provides data index structures such as R*- tree. This Java Machine Learning Library is famous among students and researchers who gain insights from data.  RapidMiner: RapidMiner used to be called Yet Another Learning Environment (YALE). It was developed in Germany at the Technical University of Dortmund. It is a platform that provides an environment for text ming, data preparation, deep learning machine learning, as well as predictive analytics. RapidMiner is used for business application, education, and training. It is easy to use and maintains workflow.  It is used for learning real-world related tasks and for research purposes. It offers a data processing system.  Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Stanford CoreNLP Stanford CoreNLP is one of the machine learning tools sounded by Stanford University. It is a Java-based framework that can perform various NLP related tasks. It has a base of words, identifying text, parts of speech, etc. Stanford CoreNLP has many features, some of which are; for pipeline production, a fast and efficient text annotator is provided. It has a well-maintained text analytics that regularly updates and has a vast database. Many machine learning tools do not offer their users with a multi-language system. But Stanford CoreNLP supports multi human languages such as English, Arabic, Chinese, etc. One of the most important features of Stanford CoreNLP is that it uses Java as its primary tool, which makes it easy to use. It also provides AIP’s for major programming languages in the world. . It can also be used as a simple web- service.  JSTAT JSTAT also stands for Java Statistical Analysis tool. It is used under the GPL3 license. It has an extensive collection of Machine Learning algorithms amongst any framework that has a high performance rate in comparison to any other Java Library. It had been developed as a self education exercise. This framework is recommended in academic and research areas. Some of JSTAT’s primary features include clustering, classification, and feature  selection methods.  Neuroph: Neuroph is an Artificial Neural Network (ANN) that is object oriented and written in Java. GUI tool is used for creating Neural Networks. Java helps developers to develop and train a set of Neural Networks. The latest update of Neuroph 2.96 has many updated features that can be used for standard machine tasks as it contains API improvements.  Machine Learning in Java provides programmers, mathematicians, data scientists, and software engineers a platform with proper techniques and tools. Complex data allows them to gain insight. It is very important to process data and understand it by starting at the basic step, which is applying machine learning methods on basic tasks like clustering, classification, documentation, data analyzing, data mining, etc. By using Mahout, Deeplearning4J, ELKI, RapidMiner, and other tools, the use of Machine Learning becomes easier.  At upGrad, our Advanced Certificate in Machine Learning and Deep Learning, offered in collaboration with IIIT-B, is an 8-month course taught by industry experts to give you a real-world idea of how deep learning and machine learning work. In this course, you’ll get a chance to learn important concepts around machine learning, deep learning, computer vision, cloud, neural networks, and more.  Check out the course page and get yourself enrolled soon! 

A high-level, multi-paradigm programming language with an object-oriented approach, Python is designed to be highly extensible, with multiple compact modules, including a multi-functional math module.  Here, we explore the different ways in which Python can calculate a very specific mathematical functionality – square roots – with or without the use of its math and cmath modules. Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What is a Square Root? While most of us are familiar with this mathematical concept, it’s worth refreshing our memory with a simple definition: The value ‘y’ is x’s square root because ‘y’, when multiplied by itself, yields the original number x. In mathematical terms this may be expressed as follows: If x = y x y or x = y2 then √x = y Square Root Functionality in Python A number’s square root may be extricated using Python in a variety of different ways: 1. Using the Python math module: A. With the inbuilt math.sqrt( ) function: Step 1: Import the math module Step 2: Use the sqrt( ) function Input code: Import math Print(“54’s square root is” ,math.sqrt(49)) Output: 54’s square root is 7.348469228349534 B. With the inbuilt math.pow( ) function: Step 1: Import the math module Step 2: Use the pow( ) function This operates on the simple mathematical principle: √x = x1/2  or  √x = x0.5. The function requires the input of two parameters – the base number and its exponent. Input code: Import math number = float(input(” Please Enter any numeric Value : “)) squareRoot = math.pow(number, 0.5) print(“The given number {0}’s square root = {1}”.format(number, squareRoot)) Output: Please Enter any numeric Value: 54 The Given Number 54.0’s square root = 7.348469228349534 Check out upGrad’s Advanced Certification in DevOps  Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 2. Using the Python cmath module Step 1: Import the complex math (cmath) module Step 2: Use the cmath.sqrt( ) function The cmath module helps compute the  square root of real or complex numbers. Input code: import cmath num = 1+2j sqrt = cmath.sqrt(num) print(‘{0}’s square root is {1:0.2f} + {2:0.2f}’ .format(num,sqrt.real,sqrt.imag)) Output: (1+2j)’s square root is 1.27+0.79 In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 3. Using the exponent ** operator: Operates on the same principle as the pow( ) function, i.e. √x = x1/2  or  √x = x0.5 but does not require users to import the math module Input code: def sqrt(n): if n < 0: Return Else: return n**0.5 print(sqrt(54)) Output: 7.348469228349534 Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau At upGrad, our Advanced Certificate in Machine Learning and Deep Learning, offered in collaboration with IIIT-B, is an 8-month course taught by industry experts to give you a real-world idea of how deep learning and machine learning work. In this course, you’ll get a chance to learn important concepts around machine learning, deep learning, computer vision, cloud, neural networks, and more.  Check out the course page and get yourself enrolled soon!