In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project.

In this hands-on project, we will train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers. Machine learning help companies analyze customer churn rate based on several factors such as services subscribed by customers, tenure rate, and payment method. Predicting churn rate is crucial for these companies because the cost of retaining an existing customer is far less than acquiring a new one. Note: This course works best for learners who are based in the North America region.

With the advent of DevOps and Continuous Delivery, businesses are now looking for real-time risk assessment throughout the various stages of the software delivery cycle. Although Artificial Intelligence (AI) is not really new as a concept, applying AI techniques to software testing has started to become a reality just the past couple years. Some development teams are turning to online and e-learning resources like Udemy. Down the line, AI is bound to become part of our day-to-day quality engineering process, however, prior to that, let us take a look at how AI can help us achieve our quality objectives. Day after day, QA Engineers face a plethora of difficulties and waste a lot of time to find a proper solution.

… from coding to artificial intelligence, machine learning and deep learning, said Dr Raja M, head of the campus’ Department of Computer Science.

This tutorial is a step-by-step, beginner-friendly explanation of how you can integrate PyCaret and Gradio, the two powerful open-source libraries in Python, and supercharge your machine learning experimentation within minutes. This tutorial is a "hello world" example, I have used Iris Dataset from UCI, which is a multiclassification problem where the goal is to predict the class of iris plants. The code given in this example can be reproduced on any other dataset, without any major modifications. PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.

Machine learning is a subfield of computer science where machines are trained to make decisions with the help of data provided without any human interference. For example, if we could teach a computer to tell if a person is lying about something, then the computer might be using machine learning as software. There are huge applications of machine learning such as Face recognition, image classification, stock market prediction, Emotion detection, self-driving cars, etc. More details about all these are covered in the training course videos. Machine learning uses knowledge from mathematics, statistics, computer science, and programming to build and deploy algorithms that can do one of those tasks mentioned above.

In this latest Data Science Central webinar, Dan Chaney, (VP of Enterprise AI & Data Science Solutions, Future Tech Enterprise, Inc.) and Lenny Isler, (Business Development, Advanced Computing Solutions, for HPi), discuss the key internal challenges that companies face when trying to manage their AI programs and align various stakeholders - business leadership, IT, and data science team members - around common objectives and business goals.

How to create an personalized artificial intelligence assistant How to create JARVIS AI How to create AI assistant How to build chatbots? How to transform your Computer into JARVIS How to build an AI Assistant? How to build an AI Assistant? Note: 100% OFF Udemy coupon codes are valid for maximum 3 days only. Look for "ENROLL NOW" button at the end of the post.

When it comes to the education landscape, the entire industry has undergone a rapid transformation in recent times. The next-gen technologies, including Artificial Intelligence and Machine learning, have portrayed their true potential in all industry verticals globally. As per a recent report, the global AI software market is forecast to reach 126 billion U.S. dollars by 2025. When every other industry is reaping the benefits from AI & ML, the education industry is no different. Artificial intelligence and machine learning in the education sector are acting as the driving force and have added value as never before.

There are many situations where using a depth camera as input into a machine learning system can offer additional benefits and features – as we previously discussed in a prior blog post "What does Depth Bring to Machine Learning." Depth cameras have a number of advantages for a variety of machine learning problems, including lighting invariance. Depth cameras can easily adapt to a wide variety of lighting conditions, something that 2D models must be trained to compensate for. A depth camera also allows differentiation between items of different size as well as easier background segmentation, allowing the separation of individual items regardless of how complicated the background is. One of the main problems facing those wishing to use depth cameras for machine learning projects at the current time is the lack of significant training datasets, when compared with the existing libraries and models based on many thousands of 2d images. Given how complex machine learning is, it's useful to have frameworks that assist with acquiring datasets, building models and implementing them.