I like to keep regular tabs on the state of data science education in America. As one of the hottest fields in the economy, I find that the quality of data science courses is a leading indicator of the state of online education innovation. So, when I see significant new develops in online education technology, I'll dedicate some time to taking the course (and, selfishly, I like to learn new data skills). One website that I've used before, Udacity, recently launched several new courses on artificial intelligence, machine learning and statistics. I've had mixed experiences with Udacity in the past.

For any executive who pursued a traditional path into the finance organization--business school, perhaps an accounting degree or an MBA in finance--the incessant media chatter about how machine learning will transform your business must be a little disconcerting. Most finance executives have little to no experience with the subject. And while many now have an idea of what machine learning is, few have a concrete understanding of how or where they can implement it. And yet, machine learning's influence on finance operations will inevitably grow, accelerating the long transition of the finance function from accounting steward and risk manager to valued business partner who helps drive strategy and identify and exploit business opportunities. A branch of artificial intelligence, machine learning refers to the use of algorithms, or computing models, that allow computers to become better at performing a given task through experience rather than programming.

This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python. You can also go through the Slides here: https://goo.gl/aNmKbQ Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-... #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people's digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

The world has witnessed some serious data breach issues and the cybersecurity has been in question multiple times during the last few years. As per a report by Cisco, it's harder for the professionals to detect cyber threats due to the huge volume of malicious as well as legitimate encrypted web traffic. Therefore, machine learning is now being proposed to be used for cybersecurity. Some people think that machine learning is similar to artificial intelligence that is able to understand language and solve problems. Rather, machine learning is a part of artificial intelligence.

Developers are not the kingmakers of the fastest growing area of technology -- machine learning (ML) and artificial intelligence (AI). Although TensorFlow, Sckit-Learn and Spark MLLib are popular on GitHub, they are at their core libraries used by data scientists to build models that enable machine learning and types of artificial intelligence functionality. In fact, 51 percent those doing ML said their models are created by internal data science teams, according to a recent O'Reilly survey. From the perspective of professionals that are creating data products, using ML services from a cloud provider is almost never even a consideration. Yet, AI/ML as a service, perhaps accessed via an API, is often pitched as the primary way developers will get involved in this field.

In Nigeria (and Africa), the proliferation of artificial intelligence is very much still in its infancy thanks to several problems ranging from a lack of adequate infrastructure to cultural and socio-economic barriers to adoption. But that doesn't mean Africa is taking a backseat and'unlooking'. As far back as 2008, South African company BrandsEye was already using a proprietary mix of search algorithms, crowdsourcing and machine learning to mine online conversations for sentiment and provide that data to enterprise customers. Also, Egyptian startup Affectiva, launched in 2009 by Rana El Kaliouby, uses emotion recognition to detect moods and make decisions based on facial expressions and has raised $34 million in venture capital till date. In Nigeria, the space is heating up, slowly but surely.

Facebook's AI researchers are working with New York University to make magnetic resonance imaging (MRI) up to 10 times faster. The goal: Through a project called fastMRI, Facebook's team will develop algorithms capable of filling in the gaps in low-resolution MRI scan, effectively turning them into higher-res ones. This could be important because currently it can take an hour or more to produce a good MRI scan. That is a long time to lie motionless inside an MRI machine, especially for children or patients who are already unwell. The hope is to cut the time down to a few minutes.

Artificial intelligence (AI) has emerged as one of the key technologies in today's digital age, revolutionizing many industries, from customer service to autonomous vehicles. The technology is also becoming a key weapon in the fight against cybercriminals. The rise of data breaches has become a critical and concerning issue. In recent years we have seen a tsunami of cyber attacks around the globe, inflicting great costs to both businesses and customers. According to a report titled "Economic Impact of Cybercrime – No Slowing Down," by McAfee and the Center for Strategic and International Studies (CSIS), cybercrime cost the world between US$445 and US$608 billion in 2017.

"I do think there is a lot of potential if you have a compelling product and people are willing to pay a premium for that. I think that is what Apple has shown. You can buy a much cheaper cell phone or laptop, but Apple's product is so much better than the alternative, and people are willing to pay that premium." "The first step is to establish that something is possible; then probability will occur." "Don't delude yourself into thinking something's working when it's not, or you're gonna get fixated on a bad solution."