Free How-To Tutorials - Collection of technical and non technical, Free tutorials and reference manuals with examples for Java, Cloud Computing, Artificial Intelligence, Machine Learning.
Free How-To Tutorials - Cloud Computing
Cloud computing is a method of computing where a shared group of resources such as file storage, web servers, data processing services and applications are accessed via the internet. ... Example of cloud computing services include Amazon Web Services, Microsoft Azure, Google Cloud Platform and IBM Cloud.
Free How-To Tutorials: Cloud Computing Tutorial – Basic
How do I get certified in cloud computing?
- AWS Certified Solutions Architect.
- Certificate of Cloud Security Knowledge.
- Certified OpenStack Administrator (COA)
- Certified System Administrator in Red Hat OpenStack.
- Cisco CCNA-Cloud.
- Cloud Certified Professional.
- Cloud Credential Council.
- Cloud Genius.
Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Of course, these topics are closely related with each other.
Free How-To Tutorials: Artificial Intelligence
What subjects are required for artificial intelligence?
- Various level of math, including probability, statistics, algebra, calculus, logic and algorithms.
- Bayesian networking or graphical modeling, including neural nets.
- Physics, engineering and robotics.
- Computer science, programming languages and coding.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Free How-To Tutorials of Machine Learning
Free How-To Tutorials - Data science
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
What are the subjects in data science?
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