Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects and Deployment throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, excited, and robust projects to better understand each concept under each topic.

Learn how to use the latest version of the Robot Operating System (ROS 2) in Python to program robots. Learn how to use the Robot Operating System (ROS) version 2 platform (ROS2) which is used to program real world robotics. The course is designed for beginners with little, to no experience using ROS2. The coding in this course is done in Python 3 using ROS2 Foxy. We will go over installation of ROS2, how to develop code that uses the framework, and even go over simulation tools so you can make your own robot and control it in simulation, with code that can easily be ported to a real robotic system.

Demystify the world of Machine Learning and build core Data Science & predictive analytics skills, without writing code! Created by Maven Analytics, Joshua McCarty 2.5 hours on-demand video course If you're excited to explore data science & machine learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes", "logistic regression", "KNN" and "decision trees", you're in the right place. This course makes data science approachable to everyday people, and is designed to demystify powerful machine learning tools & techniques without trying to teach you a coding language at the same time.

Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online. In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models. Who is this course for? You've given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables.

Learn the approaches and tools to tune hyperparameters and improve the performance of your machine learning models. Created by Soledad Galli 7.5 hours on-demand video course Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models. If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning.

Description Artificial Intelligence usage is exploding across the global and jobs are booming for this profession. There is a severe shortage of AI professionals globally as everyone from startups to tech giants to governments is jumping onboard the AI revolution. If you answered YES then this course is for you! This course is specifically designed to take away the complexity and mystery surrounding AI and Machine Learning and make it accessible for average IT guy who does not know advanced programming or data science. It will teach you the core concepts of AI / Machine Learning and then make you actually implement them using freely available services so that you get actual practical experience!

Blog - New features of Python 3.8 Sat, 30 Nov 2019 In this blog, I show how to use new features of Python 3.8. Video Tutorial - Upcasting and Downcasting in Java Wed, 27 Nov 2019 In this video, I explain upcasting and downcasting in Java Video Tutorial - How to use Lamdba Expressions in Java Sun, 24 Nov 2019 In this video, I demonstrate how to use Lambda Expression, Lambda Blocks, and Method Reference in Java 8 and above. Video Tutorial - Top-N Analysis in Oracle Database Tue, 19 Nov 2019 In this video, I show how to perform Top-N Analysis in Oracle Database 11g and 18c. Video Tutorial - Why to override equals(), hashCode() and toString() methods of Object class in Java. Sat, 16 Nov 2019 In this video, I explain why to overriding equals(), hashCode() and toString() methods of Object class in Java.

Recently, Andrew Ng took to the professional networking platform to announce that the Specialization on Machine Learning Engineering for Production (MLOps) by DeepLearning.AI is now available on Coursera. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. NG posted, "I'm thrilled DeepLearning.AI's Machine Learning Engineering for Production (MLOps) specialization is now available on Coursera!" "Being able to train ML models is essential. And, to build an effective AI career, you need production engineering skills as well, to build and deploy ML systems. With this specialization, you can grow your knowledge of ML into production-ready skills" the AI expert added.

Mixture of experts is an ensemble learning technique developed in the field of neural networks. It involves decomposing predictive modeling tasks into sub-tasks, training an expert model on each, developing a gating model that learns which expert to trust based on the input to be predicted, and combines the predictions. Although the technique was initially described using neural network experts and gating models, it can be generalized to use models of any type. As such, it shows a strong similarity to stacked generalization and belongs to the class of ensemble learning methods referred to as meta-learning. In this tutorial, you will discover the mixture of experts approach to ensemble learning.

Data scientists spend only 20 percent of their time on building machine learning algorithms and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data. That mostly happen because many use graphical tools such as Excel to process their data. However, if you use a programming language such as Python you can drastically reduce the time it takes for processing your data and make them ready for use in your project. This course will show how Python can be used to manage, clean, and organize huge amounts of data. Data scientist is one of the hottest skill of 21st century and many organization are switching their project from Excel to Pandas the advanced Data analysis tool .