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Machine learning is a set of methods, tools, and computer algorithms used to train machines to analyse, understand, and find hidden patterns in data and make predictions. The eventual goal of machine learning is to utilize data for self-learning, eliminating the need to program machines in an explicit manner. Once trained on datasets, machines can apply memorized patterns on new data and as such make better predictions.

Linear Regression

A set of input variables (x) that are used to determine an output variable (y). A relationship exists between the input variables and the output variable. The goal of ML is to quantify this relationship.

Logistic Regression

Logistic regression is best suited for binary classification: data sets where y = 0 or 1, where 1 denotes the default class. For example, in predicting whether an event will occur or not, there are only two possibilities: that it occurs (which we denote as 1) or that it does not (0). So, if we were predicting whether a patient was sick, we would label sick patients using the value of 1 in our data set.

Logistic regression is named after the transformation function it uses, which is called the logistic function h(x)= 1/ (1 + ex). This forms an S-shaped curve.

K Nearest Neighbour

The K-Nearest Neighbours algorithm uses the entire data set as the training set, rather than splitting the data set into a training set and test set.

When an outcome is required for a new data instance, the KNN algorithm goes through the entire data set to find the k-nearest instances to the new instance, or the k number of instances most similar to the new record, and then outputs the mean of the outcomes (for a regression problem) or the mode (most frequent class) for a classification problem. The value of k is user-specified.

The similarity between instances is calculated using measures such as Euclidean distance and Hamming distance.

Decision Tree

Decision tree algorithm can be used for solving regression and classification problems. Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data. There are two types in decision tree. They are categorical variable decision tree and continuous decision tree.

Neural Network

A Neural Network is basically an organization of numerical conditions. It takes at least one info factors, and by going through an organization of conditions, brings about at least one yield factors.

The blue circles represent the input layer, the black circles represent the hidden layers, and the green circles represent the output layer. Each node in the hidden layers represents both a linear function and an activation function that the nodes in the previous layer go through, ultimately leading to an output in the green circles.


Clustering is an unsupervised technique that involves the grouping, or clustering, of data points. It’s frequently used for customer segmentation, fraud detection, and document classification.

Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. While each technique has a different method in finding clusters, they all aim to achieve the same thing.


K-means algorithm in data mining starts with a first group of randomly selected centroids, which are used as the beginning points for every cluster, and then performs iterative (repetitive) calculations to optimize the positions of the centroids

Support Vector Machine (SVM)

Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Support Vectors are simply the co-ordinates of individual observation. The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line).

Recognition Model

Pattern Recognition is defined as the process of identifying the trends (global or local) in the given pattern. A pattern can be defined as anything that follows a trend and exhibits some kind of regularity. The recognition of patterns can be done physically, mathematically or by the use of algorithms. When we talk about pattern recognition in machine learning, it indicates the use of powerful algorithms for identifying the regularities in the given data. Pattern recognition is widely used in the new age technical domains like computer vision, speech recognition, face recognition, etc.

Tools of pattern recognition
  • Amazon Lex- It is an open-source software/service provided by Amazon for building intelligent conversation agents such as chatbots by using text and speech recognition.
  • Google Cloud AutoML– This technology is used for building high-quality machine learning models with minimum requirements. It uses neural networks (RNN -recurrent neural networks) and reinforcement learning as a base for model construction.
  • R-Studio – It uses the R programming language for code development. It is an integrated development environment for developing and testing pattern recognition models.
  • IBM Watson Studio – IBM Watson Studio is an open-source tool provided by IBM for data analysis and machine learning. It is used for the building and deployment of machine learning models on a desktop.
  • Microsoft Azure Machine Learning Studio – Provided by Microsoft, this tool is using a drag and drop concept for building and deployment of the machine learning models. It offers a GUI (Graphical User Interface) based environment for model construction and usage.


REST APIs play a major role as a communication channel between different services. It has become the de facto standard of passing information across multiple systems in the JSON format. This is because it has a uniform interface to share messages across two different systems.

  • GET – Used by the client to select or retrieve data from the server
  • POST – Used by the client to send or write data to the server
  • PUT – Used by the client to update existing data on the server
  • DELETE – Used by the client to delete existing data on the server

CNN Classifier and feature extraction

Convolutional neural organization (CNN) is quite possibly the most well-known and utilized of DL networks. Due to CNN, DL is exceptionally well known these days. The primary benefit of CNN contrasted with its archetypes is that it consequently recognizes the huge highlights with no human management which made it the most utilized. Consequently, we have delved in profound with CNN by introducing its fundamental parts. Besides, CNN is beginning with the AlexNet organization and finishing with the High-Resolution organization (HR.Net).

CNN is employed for HGR where both alphabets and numerals of ASL are considered simultaneously. The pros and cons of CNNs used for HGR are also highlighted. The CNN architecture is based on modified AlexNet and modified VGG16 models for classification. Modified pre-trained AlexNet and modified pre-trained VGG16 based architectures are used for feature extraction followed by a multiclass support vector machine (SVM) classifier. The results are evaluated based on different layer features for best recognition performance. To examine the accuracy of the HGR schemes, both the leave-one-subject-out and a random 70–30 form of cross-validation approach were adopted. This work also highlights the recognition accuracy of each character, and their similarities with identical gestures. The experiments are performed in a simple CPU system instead of high-end GPU systems to demonstrate the cost-effectiveness of this work. The proposed system has achieved a recognition accuracy of 99.82%, which is better than some of the state-of-art methods.

Deep Learning including lack of training data, Imbalanced Data, Interpretability of data, Uncertainty scaling, Catastrophic forgetting, Model compression, Overfitting, vanishing gradient problem, Exploding Gradient Problem, and Under specification.

Jupyter Notebook Application

The Jupyter Notebook App is a server-client application that allows editing and running notebook documents via a web browser. The Jupyter Notebook App can be executed on a local desktop requiring no internet access (as described in this document) or can be installed on a remote server and accessed through the internet.

In addition to displaying/editing/running notebook documents, the Jupyter Notebook App has a “Dashboard” (Notebook Dashboard), a “control panel” showing local files and allowing to open notebook documents or shutting down their kernels.

Google Colab

Google Colab is basically a free Jupyter notebook environment running wholly in the cloud. Most importantly, Colab does not require a setup, plus the notebooks that you will create can be simultaneously edited by your team members – in a similar manner you edit documents in Google Docs. The greatest advantage is that Colab supports most popular machine learning libraries which can be easily loaded in your notebook.

To use Colaboratory, you must have a Google account and then access Colaboratory using your account. Otherwise, most of the Colaboratory features won’t work.

As with Jupyter Notebook, you can use Colaboratory to perform specific tasks in a cell-oriented paradigm. If you’ve used Jupyter Notebook before, you notice a strong resemblance between Notebook and Colaboratory. Of course, you also want to perform other sorts of tasks, such as creating various cell types and using them to create notebooks that look like those you create with Notebook.

Application which uses Machine Learning


If you have used an app to book a cab, you are already using Machine Learning to an extent. It provides a personalized application which is unique to you. Automatically detects your location and provides options to either go home or office or any other frequent place based on your History and Patterns.

Product Recommendation

Google tracks your search history, and recommends ads based on your search history. This is one of the coolest applications of Machine Learning. In fact, 35% of Amazon’s revenue is generated by Product Recommendations.

Traffic Alert

People currently using the service, Historic Data of that route collected over time and few tricks acquired from other companies. Everyone using maps is providing their location, average speed, the route in which they are traveling which in turn helps Google collect massive Data about the traffic, which makes them predict the upcoming traffic and adjust your route according to it.

data science immeasurably affects every one of the applications. A few enterprises like banking, transport, internet business, medical care and a lot more are utilizing data science to better their items.

Data Science is an immense field and hence, its applications are additionally tremendous and different. Businesses need data to push ahead and along these lines, it’s anything but a fundamental part of the multitude of ventures today. 

In the event that you have any inquiries identified with Data Science applications, ask openly through remarks. We will hit you up.

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