Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. This course on Microsoft Clarity will help you learn how to leverage this new FREE tool by Microsoft – that makes you understand the actual user experience and gain actionable insights for your website – some insights that are currently only offered by Clarity – like Recordings, Heatmaps, dead clicks and more! Most Importantly, You will not only learn the Software, but also learn how to understand user behavior and take actions to improve user engagement thus improving your website performance and ranking.

This article on a complete tutorial to learn Data Science in R from scratch, was posted by Manish Saraswat. Manish who works in marketing and Data Science at Analytics Vidhya believes that education can change this world. R, Data Science and Machine Learning keep him busy. R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s.

In machine learning and data science, there is a huge use of probability distribution.

In this article, I will show how to build Neural Networks with Python and how to explain Deep Learning to the Business using visualization and creating an explainer for model predictions. Deep Learning is a type of machine learning that imitates the way humans gain certain types of knowledge, and it got more popular over the years compared to standard models. While traditional algorithms are linear, Deep Learning models, generally Neural Networks, are stacked in a hierarchy of increasing complexity and abstraction (therefore the "deep" in Deep Learning). Neural Networks are based on a collection of connected units (neurons), which, just like the synapses in a brain, can transmit a signal to other neurons, so that, acting like interconnected brain cells, they can learn and make decisions in a more human-like manner. Today, Deep Learning is so popular that many companies want to use it even though they don't fully understand it.

In any data science project life cycle, cleaning and preprocessing data is the most important performance aspect. Say if you are dealing with unstructured text data, which is complex among all the data, and you carried the same for modeling two things will happen. Either you come up with a big error, or your model will not perform as you expected. You might have wondered how the modern voice assistance system such as Google Assistance, Alexa, Siri can understand, process and respond to human language, so here comes the heavy lifter. Natural language processing, NLP, is a technique that comes from the semantic analysis of data with the help of computer science and artificial intelligence.

There are various dog breeds, and most of them are similar to each other. As a beginner, you can build a Dog's breed identification model to identify the dog's breed. For this project, you can use the dog breeds dataset to classify various dog breeds from an image. I also found this complete tutorial for Dog Breed Classification using Deep Learning by Kirill Panarin. This is also a good deep learning project for beginners. In this project, you have to build a deep learning model that detects the human faces from the image.

This course on Microsoft Clarity will help you learn how to leverage this new FREE tool by Microsoft – that makes you understand the actual user experience and gain actionable insights for your website – some insights that are currently only offered by Clarity – like Recordings, Heatmaps, dead clicks and more! Most Importantly, You will not only learn the Software, but also learn how to understand user behavior and take actions to improve user engagement thus improving your website performance and ranking. Microsoft Clarity is a free-to-use analytics product built to help website owners and managers improve their website experiences by better understanding site visitor behavior using real evidence in form of recordings and heatmaps. So if you are a website owner, or you manage your company's / client's websites, then knowledge of this tool will be a great new addition to your skill set, and you can get certain insights that currently no other tool offers! A Verifiable Certificate of Completion is presented to all students who undertake this course on Microsoft Clarity.

Logistic regression is a predictive modelling algorithm that is used when the Y variable is binary categorical. That is, it can take only two values like 1 or 0. The goal is to determine a mathematical equation that can be used to predict the probability of event 1. Once the equation is established, it can be used to predict the Y when only the X--s are known. Earlier you saw what is linear regression and how to use it to predict continuous Y variables. In linear regression the Y variable is always a continuous variable.

In an effort to increase standardization across the PyTorch ecosystem Facebook AI in a recent blog post told that they would be leveraging Facebook's open-source Hydra framework to handle configs, and also offer an integration with PyTorch Lightning. This post is about Hydra. If you are reading this post then I assume you are familiar with what are config files, why are they useful, and how they increase reproducibility. And you also know what a nightmare is argparse. In general, with config files you can pass all the hyperparameters to your model, you can define all the global constants, define dataset splits, and … without touching the core code of your project.

If you're a programmer, you want to explore deep learning, and need a platform to help you do it – this tutorial is exactly for you. Google Colab is a great platform for deep learning enthusiasts, and it can also be used to test basic machine learning models, gain experience, and develop an intuition about deep learning aspects such as hyperparameter tuning, preprocessing data, model complexity, overfitting and more. Colaboratory by Google (Google Colab in short) is a Jupyter notebook based runtime environment which allows you to run code entirely on the cloud. This is necessary because it means that you can train large scale ML and DL models even if you don't have access to a powerful machine or a high speed internet access. Google Colab supports both GPU and TPU instances, which makes it a perfect tool for deep learning and data analytics enthusiasts because of computational limitations on local machines.