In this course, you'll be learning various supervised ML algorithms and prediction tasks applied to different data. You'll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
This is another specialization program offered by Coursera. This specialization program is for both computer science professionals and healthcare professionals. In this specialization program, you will learn how to identify the healthcare professional's problems that can be solved by machine learning. You will also learn the fundamentals of the U.S. healthcare system, the framework for successful and ethical medical data mining, the fundamentals of machine learning as it applies to medicine and healthcare, and much more. This specialization program has 5 courses. Let's see the details of the courses-
By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you've completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization.
In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew's pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems.
Andrew Ng's DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The 3-course program is a new version of Ng's pioneering machine learning course, taken by over 4.8 million learners since 2012. The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. The new Machine Learning Specialization by @DeepLearningAI_ & @StanfordOnline is now available on @Coursera!
Do you want to learn Tensorflow and looking for the Best Resources to Learn Tensorflow?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Tensorflow including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Tensorflow. You can bookmark this article so that you can refer to this article later.
In this course, you will learn the basics of deep learning and how to build your first deep learning model using Keras. This course will teach supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library. The course material of this course is available free, but for a certificate, you have to pay. In this course, you will also learn how do neural networks learn and what are activation functions.
It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout.