This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Check out the entire series here: https://www.youtube.com/playlist?list... Support me! http://www.patreon.com/calebcurry

These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

About this course: Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools. The past decade has seen an explosion of new mechanisms for understanding and using location information in widely-accessible technologies. This Geospatial Revolution has resulted in the development of consumer GPS tools, interactive web maps, and location-aware mobile devices. These radical advances are making it possible for people from all walks of life to use, collect, and understand spatial information like never before. This course brings together core concepts in cartography, geographic information systems, and spatial thinking with real-world examples to provide the fundamentals necessary to engage with Geography beyond the surface-level.

About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts.

Getting up from the toilet after a satisfying bowel movement, you walk right over to the sink and start washing your hands. "Alexa, flush my toilet," you say while reaching for your toothbrush. Your mirror starts displaying your schedule for the day, the weather update and latest news. Suddenly, there's a ping -- your toilet has detected an anomaly in your stool and recommends you increase your fiber intake. A dispenser built into your medicine cabinet whirs and spits out a fiber supplement.

Well, if you are even remotely related to the technological field, chances are you may be aware of the world going gaga over these buzzwords. Though these different buzzwords are being used in a multitude of diverse application areas, at the core, they all mean the same thing -- making sense of vast amounts of data in a way that would give out some intelligence to act upon. Although Machine Learning has now gained prominence owing to the exponential rate of data generation and technological advancements to support it, its roots lie way back in 17th century. People have been attempting to make sense of data and processing it to gain quick insights since ages. Let me take you through an interesting journey down the history of Machine Learning -- how it all began and how did it come to what it is today.

We are living in interesting times, where digital assistants schedule meetings, chatbots work alongside humans as teaching assistants, and your suitcase can now become self driving luggage as showcased at CES, 2018. The implications are just starting to be felt in the workplace. In 2017, I wrote about how The Employee Experience is the Future of Work. Now, as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience. As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

Artificial Intelligence may seem like something from the future, with its inclusion in sci-fi thrillers and movies. While AI has yet to take over the world with destructive prone robots, it is becoming more and more prevalent in our everyday lives. Whether we know it or not, most of us probably use AI every day for simple tasks like taking a picture, parking our car, asking our phones what the weather looks like, or using our personal home assistants to turn on the lights. Our technology has yet to reach the level of self-awareness, but that doesn't mean it isn't …

This tutorial is an introduction to Artificial Intelligence which explains the need to study AI, AI growth, concept of AI, its use cases and various intelligence types in detail. If you've enjoyed this video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Got any questions about AI? Ask us in the comment section below. Are you looking for something more? Enroll in our Artificial Intelligence & Deep Learning training course and become a certified AI Expert (https://goo.gl/RdA17B).

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).