If you are in Pythoneer/Pythonista or at least familiar with python, you will encounter the word PIP as this is a vital tool that we will use to install a package to import any package in the program. Have you ever thought of creating your package or wanted to know how this process works then you came to the perfect place? This article demonstrates your step to step process so that right away you can publish your package after reading this article.

Steps for publishing a python package in PIP:-

Creating a Machine Learning Model is not enough!. Deploying it in a web/android application will fulfill the real-world application. To create a web application we can use Django or Flask as a backend framework. Flask is a micro backend framework that is perfectly suitable for creating a web application with Machine Learning Models. After creating a web application we need to deploy it on the cloud to make it available for all. For deploying easily on the cloud we can use AWS EC2 Instance. …

Running ML Models in Android using Tensorflow Lite


Generally, after we train a model we need to test it. In the Development phase, it can be done using CLI (Command Line Interface). But when we need to proceed to the deployment phase, We need to make our model run on Web and Android. For Web, We can run our models using Tensorflow.js or using Flask / Django Frameworks. When it comes to android, It would be difficult to run the models in android as it generally requires more RAM and many constraints. For this Google comes up with a mini API known as TensorFlow-Lite. By using Tensorflow-Lite API…

Exploratory Data Analysis using SweetViz Library


Exploratory Data Analysis refers to the critical process of performing initial investigations on data to discover patterns, to spot anomalies, to test hypotheses, and to check assumptions with the help of summary statistics and graphical representations. When you want to build any model in Machine Learning you first need to understand the dataset. You need to get a sense of data before making your hands dirty.

What is HummingBird?

Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks to accelerate traditional ML models.

Benefits of Hummingbird Library:-

1. All the current and future optimizations implemented in neural network frameworks;

2. Native hardware acceleration;

3. Having a unique platform to support for both traditional and neural network models;

4. Users benefit without having to re-engineer their models.

5. In general, Hummingbird syntax is quite minimal and intuitive.

This means Hummingbird Library converts the traditional ML models to neural network models and giving the privilege to accelerate the time taken for predicting…

Not Bigdata….

Illustration of large Datasets

Datasets are a collection of instances that all share a common attribute. Machine learning models will generally contain a few different datasets, each used to fulfill various roles in the system.

When any experienced data scientist is dealing with a project related to ML, 60 percent of work is done for analyzing the dataset, which we call as Exploratory Data Analysis(EDA). That means data plays a major role in machine learning. In the real world, we have huge data to work on, which makes computation and reading data with normal pandas is not feasible as if it will…

Color Separation in an Image using Machine Learning(KMeans Clustering)

Color Separation in an image is a process of separating colors in the image. This process is done through the KMeans Clustering Algorithm.K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.K-means algorithms identify k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.

Image Color Separation:-

We will be clustering the pixel intensities of an RGB image. Given an MXN size image, we thus have MxN pixels, each consisting of three…

When we want to build any deep learning model we need to process more image data, but when we have a limited amount of images then Image Augmentation comes into the race. Image Augmentation is a method of expanding the image dataset artificially by the use of multiple image processing techniques like rotation, brightness, shifting the pixels of images, flipping of images horizontally and vertically, etc.

Image Augmentation Performed on a lion image

In Deep learning, when we have very less amount of data and if we train the model then we can achieve high training accuracy but we will achieve very low out-sample accuracy which may…

Google Search Engine

PageRank (PR) is an algorithm used by Google Search to rank websites in their search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages. According to Google:

PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.

It is not the only algorithm used by Google to order search engine results, but it…

Sai Durga Kamesh Kota

Google DSC ML Lead | AI Practitioner🤖 | 3️⃣ rd year undergrad 🎓 in CSE 💻 at NIT Patna | Tech Blogger📝

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