Study group for algorithms in Ruby, hosted at App Academy

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Resources and notes for the algorithms study group with Learn Teach Code (Created by @ThuyNT13, @mtroiani, @LearningNerd, @nurarenke and @loorinm)

lustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm

Recent developments in quantum information systems and technologies offer the possibility to address some of the most challenging large-scale problems in science, whether they are represented by complicated interacting quantum mechanical systems or classical systems. The last years have seen a rapid and exciting development in algorithms and quantum hardware. The emphasis of this summer school is to highlight, through a series of lectures and hands-on exercises and practice sessions, how quantum computing algorithms can be used to study nuclear few- and many-body problems of relevance for low-energy nuclear physics. And how quantum computing algorithms can aid in studying systems with increasingly many more degrees of freedom compared with more classical few- and many-body methods. Several quantum algorithms for solving quantum-mechanical few- and many-particle problems with be discussed. The lectures will start with the basic ideas of quantum computing. Thereafter, through examples from nuclear physics, we will elucidate how different quantum algorithms can be used to study these systems. The results from various quantum computing algorithms will be compared to standard methods like full configuration interaction theory, field theories on the lattice, in-medium similarity renormalization group and coupled cluster theories.

This group aims to study pysyft tutorial parts and The Algorithmic Foundationsof Differential Privacy book chapters.

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

Diabetes Mellitus (DM), commonly known as diabetes, is a group of metabolic disorders characterized by high blood sugar levels over a prolonged period. Artificial Intelligence in Medical Science refers to real-world medical domains, considered and discussed at the proper depth, from both the technical and the medical points of view. Data Science and Machine Learning is helping medical professionals make diagnosis easier by bridging the gap between huge data sets and human knowledge. We can begin to apply Machine L earning techniques for classification in a dataset that describes a population that is under a high risk of the onset of diabetes. Given the medical data we can gather about people, we should be able to make better predictions on how likely a person is to suffer the onset of diabetes, and therefore act appropriately to help. We can start analyzing data and experimenting with algorithms that will help us study the onset of diabetes.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

Group sparse Canonical Correlation Analysis (group sparse CCA) is a method designed to study the mutual relationship between two different types of data (i.e. SNP and gene expression). More information about method and algorithm can be seen from: Dongdong Lin, Ji-Gang Zhang, Jingyao Li, Vince D. Calhoun, Hong-Wen Deng, Yu-Ping Wang: Group sparse canonical correlation analysis for genomic data integration. BMC Bioinformatics 14: 245 (2013)

Algorithms Study Group

do you need to get your site on page 1 of Search engines, but you're confident how? Well, you've come to the correct spot! I have ranked dozens of websites on page 1 of Google, and I'm going to show you how you can do the same. Very first, let's start with a quick primer about how Yahoo and other engines like google work. How Google (And Other Search Engines) Job The first thing you should realize about Google is that Google is NOT NECESSARILY the Internet. only because of your site doesn't show up on the Google, it does not mean it is not on the Internet. Your site will be published go on the World wide web without displaying in Google's search results for a particular keyword. The way Google and some other search engines present websites is: Google has got automated computers working around the clock that arbitrarily visit sites all over the Internet and take pictures of each page, and record them away in a massive database. As part of having a snapshot of each web page, Google's system reads every term and other content on a web page (like images, videos, audio tracks files, etc) to be able to determine the specific subject of the page. Google uses this collected data to find out which web pages to show, and in what order, for a particular keyword search. Google's computer {algorithms|methods} {dynamically|effectively} determine which {page|webpage} on the Internet is the most relevant for a specific key word or key phrase and displays it first. It then displays the 2nd most relevant web page, and so on. The cake you produced list of web pages is known as a Search engine Page (SERP). every web site owner on the world desires to be in very first page of Yahoo search engine. mainly because honestly, that is where action happens, by being top on Search engines you can make your fortunes. there are only 10 organic results Google brings for each search term executed, so how could you make positive that it is your site Google displays in page one? Preferably in the first natural ranking position, here is: How you will can easily Be top on the search engines Search engine Results for FREE OF CHARGE You can rank in Google’s first page (at times in just 24 hours) if you learn choosing keywords and create articles around these keywords. The process is straightforward as soon as you learn how people search on Google. The process to follow is: Use Search Box and find keywords that are performed of 3 or more words as shown in the next image: Google search for Just how to Rank on the search engines Search Results for How to position on Google features quick search recommendations for search conditions “how to get top put on Google” Related Search conditions Provides Additional Keyword Ranking Possibilities Search Results for The way to get on the First Webpage of Yahoo Search Outcomes for How to Get on the First Webpage of Google comes with a screenshot of related research words in line with the sample keywords researched When you begin to make use of keywords found in related keyphrases, you can get higher Google rankings not simply for the initial keywords you are targeting, but others as well. This specific will obviously get you on first page of Yahoo for diverse group of keywords and not merely the people you in the beginning created. Website landing page Optimization Simplified Looking at the below, all you now have to do is create your website pages in line with the initial keywords you have selected. Basically, think of each keyword/s you are targeting as “Questions” people are asking search motors. As well as your job as the website owner is to “Answer” their questions on your web pages. The way to Rank First in Google The way to Rank First in Google particulars on page lookup engine ranking factors. How to create optimized web pages is outlined through name, meta description, navigation menu, h1 and h2 labels and internet site image marketing Study the contents well and make them part of your web webpage optimization knowledge. Because before long, your web pages can be first on Google for free without having to pay. Inside fact, you will get top rankings instantly ( within 24 hours), or as soon as Google becomes informed about your new landing pages that are completely optimised. Get On the First Page of Google Through Google Ppc (Paid Version) You are able to pay Google Adwords for Ppc Advertising. It is a straightforward process and I’ve got video tutorials that shows you how to create a Adwords Campaigns here. Facebook PPC advertising will allow you to focus on keywords and advertise these to be shown in Google search engine results. However, SEO for organic and natural rankings should be part of your web success for lasting results. Due to the fact once you achieve first position rankings, it is going to usually stay there on the first page all the time (especially by using RankYa Search Powerplant Optimization techniques).

For Algorithms and Django Study groups

A program made in Java that visualizes common data structures and some common sorting and graph algorithms. This was a group project in 2nd year of my Undergraduate studies

Saxo Bank Review Info Update 2022 otoxs July 03, 2022 Saxo bank is considered one of the most relied on names inside the world of foreign exchange buying and selling. The dealer provides a huge collection of belongings for margin-based totally trading, low spreads, and lightning speedy execution of trades. The broker holds a couple of policies global. In this article, I review some functions that Saxo Bank has and why buyers and nontraders could make money with the broker’s provision.Overview Saxo Bank A/S is privately owned; Geely Financials Denmark A/S, a subsidiary of Zhejiang Geely Holding Group Co., Ltd, owns fifty two percentage of the stocks of its shares. The founder and CEO of Saxo Bank, Kim Fournals, owns 25.71% of the Bank shares. Shampoo Plc, a main Nordic economic offerings enterprise, additionally owns 19.9% of the financial institution shares. Minority shareholders, such as numerous modern and former personnel of the Bank, preserve the ultimate stocks. The Bank turned into the primary broking in Denmark to gain approval from the European Investment Directive in 1992. Saxo Group presently operates international, taking part carefully with supervisory government in each jurisdiction it exists in. The broking is regulated through the Australian Securities and Investments in Australia, the Securities and Futures Commission in Hong Kong, the Japanese Financial Services Agency in Japan, Financial Conduct Authority inside the UK, Bank of Netherlands within the Netherland. It is also regulated in Italy, Czech, Singapore, Switzerland, U.A.E., Denmark, and many others. Aside from having a extensive agency jurisdiction, it additionally has a completely extensive variety of markets reachable to traders. The markets furnished encompass foreign exchange, stocks, ETFs, Bonds, Mutual Funds, Futures, Listed Options, and more. It also gives controlled portfolios wherein professionals navigate and manipulate your investments without your input. The controlled portfolio additionally gives you the liberty to withdraw at any time. The Bank has provisions for retail traders and institutional buyers.ProsConsStrongly regulated Wide variety of markets Useful studies toolsHigh minimum deposit Only to be had for six days a week High bond, alternatives and destiny costs Who is Saxo Bank for? The broking offers a extensive variety of offerings, and it has one of a kind aspects, which makes it beneficial for one of a kind forms of humans. The SaxoSelect program affords a controlled portfolio that's beneficial for individuals who do not have any form of history in trading. Through the program, specialists carry out buying and selling and manipulate portfolio for your behalf. The SaxoSelect is not just beneficial for amateur investors; other buyers who do no longer have time to exchange or desire to construct a separate portfolio other than their regular trades also can make earnings through the program. Beginners and advanced buyers also can make money through trading the lots of gadgets which are to be had at an cheap rate. The broker additionally provides a wholesome ground for institutional buyers and people who're inquisitive about partnering with the corporation. Rating: four.five/5Is Saxo Bank Safe? Saxo Bank A/S is integrated in Denmark as a certified bank and is regulated along side Saxo Bank A/S Italy, Saxo Bank AS the Czech Republic, Saxo Bank Netherlands via the Danish Financial Supervisory Authority (F.S.A.). As a member of the European Union, the E.U. Banking and Investment Directives were incorporated through Denmark into the Danish Law. Saxo financial institution A/C and its branches are also regulated in one of a kind jurisdictions. It is regulated and licensed in the U.K. by using the Financial Conduct Authority; BG SAXO Societal did. Intermediazione Mobiliaire S.p.A. is certified by way of the Italian Market Authority. It is licensed in the Czech Republic with the aid of Czech National Bank. Saxo Bank A/S Netherlands is registered by means of the Bank of Netherlands. Saxo Bank Pte. Ltd. Singapore is supervised through the Monetary Authority of Singapore. It is a capital markets offerings license holder and an exempt financial marketing consultant. Other rules and licenses include the Central Bank of U.A.E. as a consultant workplace, Japanese Financial Services Agency, Securities and Futures Commission in Hong Kong, Australia Securities and Investments Commission in Australia.[2] So a long way, what I will say is that the bank is duly certified and controlled by way of the top and trusted agencies and businesses. For more statistics approximately the licenses and guidelines, please click right here. Rating: 4.5/5Year founded:1992Publicly traded: NoLicenses: ASIC, FCA, FMS (Swiss), SFC, JFSA (Japanese), MAS (Singapore), and moreBacklist: NoOffering of InvestmentsStock Saxo financial institution provides get admission to to more than 19,000 stocks across several core and rising markets on extra than forty exchanges worldwide. I find this thoughts-blowing because it is not commonplace to look the range of shares furnished with the aid of the broker on many other brokerage systems. These exchanges encompass NASDAQ, NYS, Singapore Exchange, Hong Kong Exchange, Australian Securities Exchange, Tokyo Stock Exchange.ETFs It also has more than 3000 ETFs from extra than 30 exchanges across the worldBonds Mutual Funds there are extra than 250 top-rated mutual finances to be had on the platform. The mutual price range are from the sector’s largest investors.Futures They are also to be had of their thousand Listed Products Traders and traders have get admission to to extra than 1,two hundred listed options from 23 exchanges global. These listed alternatives are throughout equities, hobby rates, energy, metals, indices, and extra. Others Asides from all that have been listed above, you could additionally alternate cryptocurrency CFD and ETPs. The ETPs help to take a long-time period function without leverage. Forex trading and lots of extra. The dealer provides eighty two FX spot pairs and 140 forwards throughout fundamental, minor and distinguished pairs. Metals are also available. Rating: 4.5/5Forex: Spot TradingYesCurrency Pairs (Total the Forex market pairs)182Social Trading / Copy-TradingYesCFDs instrumentsforex, stocks, crypto, ETFs, Bonds, Mutual fund, futures, indexed products, and so on.Commissions & Fees The dealer gives enterprise-leading costs. Traders and traders get competitive spreads and commissions throughout all asset training. The rates additionally grow to be better as change volume will increase. The dealer offers commissions from $1 on US stocks and the USA-indexed ETFs. It also costs commissions as low as $1.25 futures and indexed options; there aren't any expenses for making an investment in mutual funds. Many of the options, bonds, and futures expenses are a little high as compared to some other agents. For more facts on the expenses Rating: 4/5Minimum Initial DepositUSD 10,00, 500(GBP) for UK, USD3000 for Australia and SGD three hundred for Singapore Average Spread EUR/USD – Standard0.8All-in Cost EUR/USD – Active0.6Minimum withdrawal Platforms & Tools SaxoTraderGO It has improved trade tickets, fundamental and technical evaluation equipment, an in depth charting package, performance analysis, and some other essential capabilities. It helps a one-screen setup even though the chart can be dragged to a 2nd display screen. I located that the platform is obtainable through mobile and exclusive variations of windows.SaxoTraderPRO it's miles completely customizable and is a expert-grade platform. It provides functions like algorithmic orders, one-click buying and selling, and different charting applications. I found that it's miles customizable throughout six screens, with superior workspace and more functions thru my check. This platform isn't reachable on mobile gadgets. It is restrained to PC and MAC. The two platforms are well constructed and are consumer-friendly. Connectivity and APIs: this device allows you hook up with Saxo’s capital markets infrastructure through your selected interface. Third-celebration equipment These tools assist to execute trades from one in all Saxo’s companion’s structures. This enables you join your Saxo account to a 3rd-birthday celebration platform to access custom tools that healthy your unique buying and selling requirement. These equipment, although useful, is probably too technical for beginners to understand how to use them. Such novices could need to undergo a getting to know procedure to master them. The broking also gives a demo account in which traders can alternate a selected marketplace earlier than trading it on a stay account. Rating: 4/5Virtual Trading (Demo)YesProprietary PlatformYesDesktop Platform (Windows)YesWeb PlatformYesMetaTrader four (MT4)YesMetaTrader 5 (MT5)NoCharting – Indicators / Studies (Total)58Charting – Drawing Tools (Total)19Charting – Trade From ChartYesOrder Type – Trailing StopYesResearch The studies tools cover all of the assets furnished. Saxo Bank affords day by day commentary and in-intensity analysis across its markets. There are also articles written through monetary experts from special fields that can be beneficial for buyers to get statistics from. The studies data helps you understand how investors view the markets and notice what the experts are seeing.

Study group and meeting contents for introduction to algorithm

Soongsil University's Algorithm Study Group : Soongsil Computing Contest Club's Algorithm github for ACM-ICPC

Algorithm + CS Study Group

Algorithm Study Group

A collaborative study group at QMUL, following the NeetCode 250 roadmap to master Data Structures and Algorithms through LeetCode. We aim to solve 3 problems per week.

Lithofacies classification using well log data from the Hugoton and Panoma Fields dataset. This project implements various machine learning algorithms including Support Vector Machines, Random Forest, Neural Networks, and others to predict facies groups. The study focuses on improving facies classification accuracy using well log data from 9 well.

Phishing emails are a type of online scam where criminals send an email that appears to be from a legitimate company and ask you to provide sensitive information. This is usually done by including a link that will appear to take you to the company’s website to fill in your information – but the website is a clever fake and the information you provide goes straight to the person behind the scam. In this research paper, unsupervised machine learning and the K-means Clustering technique is applied to identify the theme of phishing on a phishing-email dataset which is scraped from various sources spanning from 2010 till 2019. Unsupervised machine learning uncovers previously unknown patterns in data and is best applied when there is no data on desired outcomes or for problems that the business has not seen before. K-means clustering is a popular unsupervised machine learning algorithm that groups similar data points together based on certain similarities to discover underlying patterns. This research paper will explain: The purpose of the study The analysis of the data set The process of cleaning the data Identification of a phishing theme using clustering Our conclusions

In time-domain astronomy, data gathered from the telescopes is usually represented in the form of light-curves. These are time series that show the brightness variation of an object through a period of time (for a visual representation see video below). Based on the variability characteristics of the light-curves, celestial objects can be classified into different groups (quasars, long period variables, eclipsing binaries, etc.) and consequently be studied in depth independently. In order to characterize this variability, some of the existing methods use machine learning algorithms that build their decision on the light-curves features. Features, the topic of the following work, are numerical descriptors that aim to characterize and distinguish the different variability classes. They can go from basic statistical measures such as the mean or the standard deviation, to complex time-series characteristics such as the autocorrelation function. In this document we present a library with a compilation of some of the existing light-curve features. The main goal is to create a collaborative and open tool where every user can characterize or analyze an astronomical photometric database while also contributing to the library by adding new features. However, it is important to highlight that this library is not restricted to the astronomical field and could also be applied to any kind of time series. Our vision is to be capable of analyzing and comparing light-curves from all the available astronomical catalogs in a standard and universal way. This would facilitate and make more efficient tasks as modeling, classification, data cleaning, outlier detection and data analysis in general. Consequently, when studying light-curves, astronomers and data analysts would be on the same wavelength and would not have the necessity to find a way of comparing or matching different features. In order to achieve this goal, the library should be run in every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, etc) and future surveys (LSST) and the results should be ideally shared in the same open way as this library.

Notebooks used for presentation in Algorithms Meetup with the Fastai Study Group.

A collaborative, week-by-week journey through Aditya Bhargava’s “Grokking Algorithms.

Collection of algorithms problems and solutions for study group.

r30 study group for data structures and algorithms

LikeSeal : Algorithm Club's Fourth Study Group.

This is a study group for comp 580 - Probabilistic Algorithms and Data Structures