Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques.

Learn Machine Learning from scratch, this course for beginner who want to learn the fundamental of machine learning and artificial intelligence.

This course brings the software architecture skills required by an enterprise architect. In the lectures, we go through the engineering requirements and how to deal with gained information. Although the course is not about to show you how to build a web/desktop/mobile app with programming, but you have a great tool to create blueprint of your system. You will learn modern way to create your own design pattern or use common and useful architecture patterns. As it is said in the videos, by creating a blueprint of you system before starting to build it, you can then easily edit/modify/update/upgrade the system even after lot of years.

The pace of digital transformation in the education sector has accelerated immeasurably over the past two years. Every stage of education, from primary to higher education as well as professional and workplace training, has undergone a shift towards online and cloud-based delivery platforms. Beyond that, the changing needs of industry and workforces have prompted a dramatic change in the relationship between adult learners and providers of further education, such as colleges and universities. The value of the educational technology (EdTech) sector is forecast to grow to $680 million by 2027. Much of this will be due to mobile technology, cloud services and virtual reality creating new possibilities for accessible, immersive learning.

This is a course on Machine Learning, Deep Learning (Tensorflow PyTorch) and Bayesian Learning (yes all 3 topics in one place!!!). This is a course on Machine Learning, Deep Learning (Tensorflow PyTorch) and Bayesian Learning (yes all 3 topics in one place!!!). We start off by analysing data using pandas, and implementing some algorithms from scratch using Numpy. These algorithms include linear regression, Classification and Regression Trees (CART), Random Forest and Gradient Boosted Trees. We start off using TensorFlow for our Deep Learning lessons.

Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.

Evaluation metrics to analyze the performance of models Industry relevance of linear and logistic regression Mathematics behind KNN, SVM and Naive Bayes algorithms Implementation of KNN, SVM and Naive Bayes using sklearn Attribute selection methods- Gini Index and Entropy Mathematics behind Decision trees and random forest Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost Different Algorithms for Clustering Different methods to deal with imbalanced data Correlation Filtering Content and Collaborative based filtering Singular Value Decomposition Different algorithms used for Time Series forecasting Hands on Real-World examples. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language. This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning.

We have the pleasure of introducing Radiant Earth Foundation's first online course, Machine Learning for Earth Observations (ML4EO) Bootcamp. Available on Atingi, an open digital learning platform designed to improve training and employment opportunities, this self-paced course contains a mixture of lectures and hands-on exercises for novice data science or remote sensing practitioners. Atingi is implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). A discussion exchange forum has been set up for participants to post questions about the course content and get help from others taking the course. The Radiant MLHub LinkedIn community page and Slack channel can also be used to crowdsource answers to questions.

So what does it take to build a successful data science team? Whether you are the first "data person" at your organization or leading a team of hundreds, we know success is not based on just technology; it requires people to create a productive, effective, and collaborative data science team. Last month's webinar featured data science leaders from Caliber Home Loans, The Looma Project, Saturn Cloud, T-Mobile, and Warner Music Group to start to answer this question. You can view the recording of the webinar at Building Effective Data Science Teams. There were so many great follow-up questions that we'd like to keep this conversation going. We've also added links to an RStudio Community thread for each individual question if you'd like to continue the conversation there as well. We have paraphrased and distilled portions of the responses for brevity and narrative quality. What is a symptom that you have observed, during your time in this field, of a team being low on credibility within an organization or with stakeholders?

Neural networks, or deep learning, is a capability that is changing the way people live and work. From language translations to medical diagnosis to speech recognition to self-driving cars, deep learning is in the fabric of a technology revolution. But what is deep learning, and how much knowledge does a nontechnical or computer science stakeholder need to have to contribute to or run projects, or to spot opportunities for applications? How do healthcare executives know the potential data objectives faced can be addressed with deep learning? To add more complexity, the marketplace is filled with content and claims that will confuse even the most ardent expert.