Then you arrived at the right place, this course is designed for you! Quantum Computing is the intersection of computer science, mathematics and quantum physics which utilizes the phenomena of quantum mechanics to perform computations which classical computers cannot perform. Quantum computers are faster than classical computers and provides significant speedup in different kinds of algorithms such as searching data elements or breaking RSA encryption systems! It is expected that the Quantum Computing industry is going to grow at a rapid rate from around USD 500 million in 2021 to nearly USD 1800 million (1.8 billion!) by 2026. Various industries such as banking, finance, space technology, defense, healthcare, pharmaceuticals, chemicals, energy, power, transportation, logistics, academia and government are going to do well out of this cutting-edge technology.

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng's experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

Get in touch with the prominent experts within Industry 4.0 and Digital Transformation! Industry 4.0 is closely associated with more automation (compared to the Third Industrial Revolution), bridging the physical and digital worlds enabled by IIoT, Big Data, IoT, cloud computing, cognitive computing and smart factories. Investing in innovative Industry 4.0 technologies has many advantages. It boosts collaboration between departments, increases efficiency, fuels growth as well as trims costs. Real-time data and intelligence, predictive analytics and IoT machinery helps companies be proactive when it comes to solving and addressing potential supply chain management and maintenance issues. In addition, it makes it easy to optimise and manage all aspects of manufacturing processes and supply chain.

Today, artificial intelligence and machine learning are two popular terms that have been often used interchangeably to describe an intelligent software or system. Even though both AI and ML are based on statistics and mathematics, they are not the same thing. Many people have been confused by these two terms. In this article, you will learn the distinctions between AI and ML with vivid examples. Artificial intelligence, or AI, is the ability of a computer or machine to mimic or imitate human intelligent behavior and perform human-like tasks.

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. About the Deep Learning Specialization The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

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.