"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
This tutorial is part of the Getting started with Watson OpenScale learning path. In this tutorial, you'll see how IBM Watson OpenScale can be used to monitor your artificial intelligence (AI) models for fairness and accuracy. You'll get a hands-on look at how Watson OpenScale will automatically generate a debiased model endpoint to mitigate your fairness issues and provides an explainability view to help you understand how your model makes its predictions. In addition, you'll see how Watson OpenScale uses drift detection. Drift detection will tell you when runtime data is inconsistent with your training data or if there is an increase the data that is likely to lead to lower accuracy.
Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.
AWS's Machine Learning includes three techniques, binary classification, multiclass classification, and regression. What we will do in this course is to look at these three machine learning techniques with three different data sets. To keep things interesting, we will use Kaggle's data sets for two of our examples. If you are new to machine learning, don't worry, you'll learn machine learning concepts along the way and I'll walk you through the AWS console. We will work our way through the six Amazon Machine Learning steps.
Understanding deep learning technology Understand correlation between deep learning, machine learning and artificial intelligence History of deep learning Deep learning networks Intuition behind deep learning and artificial neural network A Powerful Skill at Your Fingertips Learning the fundamentals of deep learning puts a powerful and very useful tool at your fingertips. Jobs in deep learning area are plentiful, and being able to learn deep learning will give you a strong edge. Deep learning is becoming very popular. Tesla self-driving cars, Alexa, Siri, IBM Deep Blue and Watson are some famous example of deep learning application. Understanding deep learning is vital in information retrieval, image classification and autonomous car driving.
Artificial Intelligence is the big thing in the world of technology. From self-driving cars to automated robots to NPCs in our games to simple things like AI processing of images in our smartphones, Artificial Intelligence is now entrenched in our daily lives. And if you are looking to have a long future as a developer and get the highest paying jobs, you need to have the required skills to work in the AI field. Thankfully, there are tons of online courses that can not only get you started on AI but also help you become a professional AI developer. To make your life easier, we have listed the 10 best Artificial Intelligence courses online that can help you in your learning journey.
Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.
Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram. Master the basics: become an expert in Python and Java while learning core machine learning concepts Learn TensorFlow and how to build models of linear regression Machine learning goes mobile: learn how to incorporate machine learning models into Android apps Make an app with Python that uses data to predict the stock market. Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram. Make an app with Python that uses data to predict the stock market. Go through 3 ultimate levels of artificial intelligence for beginners Learn artificial intelligence, machine learning, and mobile dev with Java, Android, TensorFlow Estimator, PyCharm, and MNIST.
Basics of Machine Learning and its types Deep Learning and Neural Networks Learn about Tensorflow Lite Generate Tensorflow lite model from Keras model Generate Tensorflow lite model using saved model Generate Tensorflow lite model using concrete function Train and deploy classification and regression models Use datasets available in different formats for model training Learn Python Programming language Learn popular Machine Learning libraries like Numpy,Pandas and Matplotlib Learn Tensorflow 2.0 This course is designed for Android developers who want to learn Machine Learning and deploy machine learning models in their android apps using TensorFlow Lite. This course will get you started in building your FIRST deep learning model and android application using deep learning. We will learn about machine learning and deep learning and then train our first model and deploy it in android application using tenserflow lite . All the materials for this course are FREE. We will start by learning about basics of Python programming language.
Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2019, then you have come to the right place.
Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world.