With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks.

Auto insurance fraud costs companies billions of dollars every year. Those losses trickle down to policyholders who absorb some of that risk in policy rate increases. Thélem assurances, a French property and casualty insurer whose motto is "Thélem innovates for you", has launched an artificial intelligence program, prioritizing a fraud detection use case as its initial project. Fraud detection is a model that lends itself well to online machine modeling and is a project that would allow us to enter into artificial intelligence starting with the analytical field that we have prioritized. A successful fraud detection project would deliver immediate, significant financial gains for the company.

Google Colab is one of the best tools online to utilize for the construction of machine learning projects. With the help of the wonderful tools that are available to us within the Google Colab environment, we are granted access and freedom to manipulate the Colab notebooks to achieve the best possible results for a particular task. Colaboratory (also known as Colab) is a free Jupyter notebook environment that runs in the cloud and stores its notebooks on Google Drive. Colab was originally an internal Google project; an attempt was made to open-source all the code and work more directly upstream, leading to the development of the "Open in Colab" Google Chrome extension, but this eventually ended, and Colab development continued internally. With just the help of your Gmail account, you are granted access to this free service for making the best utility of the Notebooks.

AutoAI, a powerful, automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award yesterday at the AIconics AI Summit in San Francisco. Chosen by a panel of 13 independent judges, the AIconics awards recognize breakthroughs in AI for business. To share what went behind the development of AutoAI and how it accelerates time to value with data science projects, I interviewed one of our principal inventors: Jean-Francois Puget, PhD, a distinguished engineer for machine learning and optimization at IBM and a two-time Kaggle Grandmaster. What challenge led you to start developing AutoAI? Jean-Francois Puget: As data scientists, our work is a mix of applying general-purpose recipes and creating domain-specific insights.

This video shows how you can use the Keras applications to classify images. It also talks about the use of the image processing layers of an existing model as the base for a new classification model. This approach saves times and resources to get you to a solution faster. In this video, we are using a notebook in the IBM Watson Studio. You can then download it and import the file to create a notebook.

To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.

Machine learning is expanding its scope to get the title of the trendiest job market across the globe. Techno-experts and various establishments are investing billions into this fleshly coming up industry. As per statista the chief reason for the adoption of machine learning technology according to 33% of individuals is its use in business analysis. Offering a handful of opportunities, freshers of IT as well as experienced individuals are willing to know more about the different programming coding and language tool to establish themselves wholeheartedly in the machine learning software. Among all this, there are various non-programmers who don't possess to have any kind of knowledge about coding and yet desires to walk in the vicinity of machine language and remain functioning in the industry.

Customer experience management (CXM) programs are necessarily a quantitative endeavor, requiring CX professionals to decipher insights from a sea of customer data. In this post, I will illustrate how you can use IBM Watson Studio to analyze one source of customer data, customer survey responses, to answer two important questions about the health of your customer relationship: 1) what is the current level of satisfaction across the CX touch points and 2) which of these touch points is responsible for ensuring customers are loyal? Customer Experience Management (CXM) programs rely on different types of data that come from a variety of sources. The most popular source of customer feedback is surveys. These two questions will help you understand how well you are meeting the needs of your customers and, more importantly, understand what you need to do to improve customer loyalty.

IBM THINK is the premier gathering of technologists, marketers, and subject matter experts for all things IT, security, compliance, and AI/machine learning. I'm honored to be speaking again at THINK, this year in San Francisco, February 11-15. What will I be presenting? I'll be showing some brand new applications of machine learning for marketing, five applications that provide tangible benefits to marketers who want to get more results out of their data. What are these applications of machine learning for marketers?