Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. Value estimation--one of the most common types of machine learning algorithms--can automatically estimate values by looking at related information.

In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.

This course is designed to teach the student the concepts of supervised and unsupervised machine learning by experimenting on the toy datasets that are installed in Python's machine learning library, sklearn. The student will learn the basics of coding in the Python programming language and then will learn the basics of machine learning by studying a very small dataset and the code that has been used to make predictions on it. When the student has learned the basics of programming in Python and making predictions on a very small movie recommendation dataset, he will go on to study the eight toy datasets that are installed in sklearn, which is Python's machine learning library. The student will study the code of the above dataset and will learn the basics of supvervised machine learning, which involves making predictions on labeled datasets to answer either regression or classification problems. The students will also go over the code on an unsupervised learning technique, clustering.

I have worked with IBM, Cisco, EMC-RSA and others, and I have been an academics for a couple of years. I worked in four continents and travelled extensively. I have a PhD in Engineering, an MSc in AI and an MBA, i am also a Certified Blockchain Expert.

Lucas and his team are experienced Machine Learning engineers and trainers with over 10 years of industry experience in building machine learning and other engineering projects. Lucas enjoys learning and is interested in all things machine learning, data science and cryptocurrency related. He's a keen kaggler and when he's not writing courses or working he's probably to be found hacking an arduino.

Have you ever wondered how the Stock Market, Forex, Cryptocurrency and Online Trading works? Have you ever wanted to become a rich trader having your computers work and make money for you while you're away for a trip in the Maldives? Ever wanted to land a decent job in a brokerage, bank, or any other prestigious financial institution?We have compiled this course for you in order to seize your moment and land your dream job in financial sector. This course covers the advances in the techniques developed for algorithmic trading and financial analysis based on the recent breakthroughs in machine learning. We leverage the classic techniques widely used and applied by financial data scientists to equip you with the necessary concepts and modern tools to reach a common ground with financial professionals and conquer your next interview.By the end of the course, you will gain a solid understanding of financial terminology and methodology and a hands-on experience in designing and building financial machine learning models.

We help millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in--and for--the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Janani Ravi and the team and is specially produced in collaboration with Janani for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP funding scheme, terms and conditions apply. Note from the team ... This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data.

Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding. The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist.

Try here the demonstration tool for automatically classifying goods with their commercial descriptions and experience how AI could assist core Customs operations. As the awareness among Customs agencies about the importance and the interest in its application grows, the BACUDA expert team with the support of CCF-Korea continues to deliver state of the art methods and training material to meet the demands of Members. Complementing the development of the neural network model to support the classification of goods in Harmonized System, an online advanced Data Analytics course including a practical module on the HS recommendation algorithm was published on CLiKC!, the WCO e-learning platform. The BACUDA team of experts collaborated on the development of an AI model to recommend HS codes, which aims to support commodity classification for Customs officials by using historical data to predict HS codes upon the entry of the commercial descriptions of goods. An accompanying tool provides a demonstration on the functions which the model offers.