In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting.

Alexa Joyce, future skills director at Microsoft, discusses how the education sector can go about unlocking its'black box' of data As the UK education system recovers from the effects of the global pandemic, there has been a significant rise in attention paid to the increased use of technology in teaching and learning. Two billion learners will use digital learning services by 2050, while the education technology (edtech) market is projected to triple by 2025, with global spending reaching $404bn. As investment in edtech continues to grow, students, parents, and teachers face an array of solutions -- from digital personalised learning platforms, devices and accessories, through to multiple online courses. Alongside the opportunities technology provides for increasing accessibility, the wealth of data that edtech offers is unsurmountable. But, how can technology allow policymakers, school leaders and sector experts to unlock this'black box' of education data and use it for improved learning outcomes?

Since as a beginner in machine learning it would be a great opportunity to try some techniques to predict the outcome of the drugs that might be accurate for the patient. The main problem here is not just the feature sets and target sets but also the approach that is taken in solving these types of problems as a beginner. Welcome to this project on Drug Classification in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

Amazon India is inviting applications from students for the second edition of its online Machine Learning (ML) summer school. The course aims to teach key machine learning technologies by scientists at Amazon and to make the candidates industry-ready for careers in science. The free course, which was launched in 2021, is open to students enrolled in any recognised institute in India, who are expected to graduate in 2023 or 2024. Rajeev Rastogi, vice president of applied science in ML, says, "The tutorial sessions covering the right mix of theoretical and practical knowledge will be delivered by our ML scientists who are experts in their field. This programme will be a platform to help foster ML excellence and strive towards developing applied science skills in young talent."

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course.

Expand your NLP portfolio using BERT and Haystack to answer all your questions! If you're trying to learn Natural Language Processing (NLP), make a Discord Bot, or are just interested to play around with Transformers for a bit, this is the project for you! In this example, we will create a Chatbot that knows everything about Dragon Ball, but you can do about anything you want! It can be a chatbot that answers questions about another series, a university course, the laws of a country, etc. Firstly, let's see how that is possible with BERT. BERT is a Machine Learning technique for NLP created and published by Google in 2018.