HTML to create websites CSS and Bootstrap to style your websites JavaScript - one of the most in demand coding languages in the market for web development jQuery - a simplified way of applying Javascript Python, an extremely valuable, versatile and powerful coding language Django - the python framework for creating dynamic websites that can even integrate machine learning and AI Create dynamic websites using the Model-View-Controller software design pattern Data science - the ability to handle, clean, visualise and analyse big data. Some of the biggest salaries and investments go into Data Scientists (NumPy, Pandas, Sklearn, Matplotlib, Seaborn) Full training in entry mathematics and statistics with a heavy emphasis on machine learning How to develop machine learning from scratch - training algorithms using big data that can then be used in production for making predictions Deep learning / AI - learn to create your own AI solutions, such as image classifiers, AI capable of creating art, and much more Create a range of cutting edge neural network architectures Document your code at a UK industry standard Use AWS tools such as EC2 to host your websites Integrate web server tools such as Nginx and Gunicorn Master essential developer tools such as GIT, Jupyter notebook, Google Colab, GPUs, Putty, Browser Developer Tools Gain experience in digital security - the DOs and DONTs of developing and scaling online websites and services Harness the power of Linux Create Application Programming Interfaces (APIs) in Python Gain the ability to access machines (e.g. The combination of being able to create full-stack websites AND machine learning and AI models is very rare - something referred to as a unAIcorn. This is exactly what you will be able to do by the end of this course. Why you need this course Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your own tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unAIcorn.

Good day, The biggest AI bootcamp in Nigeria is here! Will you be part of the best of the best who will make it to the all-expense paid residential Artificial Intelligence Bootcamp?... This was a mail I received on September 24 from Data Science Nigeria. And below is a snippet of what I got on Data Science Nigeria's website today. I started my programming journey back in September, 2018 with the most highly rated course on Udemy courtesy of my mentor, Fakorede Abiola.

The robots are coming – for jobs. This is the plain, cold, hard fact we now face as we head towards the third decade of the 21st Century. The technology-driven world in which we now live is one filled with promise – cars that drive themselves, algorithms that respond to customer service inquiries, automated business intelligence on tap. Yet, this brave new world is also filled with challenges. For even as AI and automation increase productivity and improve our lives, their widespread adoption means that many work activities humans currently perform will soon be displaced – if they haven't been already. What this doesn't mean, however, is that there will be a shortage of jobs in the future.

Get your team access to 3,500 top Udemy courses anytime, anywhere. Get your team access to 3,500 top Udemy courses anytime, anywhere. From beginner to advanced Learn how to select most important features and build simpler and more robust machine learning models. The course covers various ways of Feature Selection in complete Detail, Below are the Major categories of Methods covered:- 1. Filter Methods 2. Wrapper Methods 3. Embedded Methods 4. Genetic Algorithm 5. Other Advance Methods The videos include full code written in Python 3 (Jupyter notebook) that you can directly apply to your own data sets. So what are you waiting for?

Get your team access to 3,500 top Udemy courses anytime, anywhere. Get your team access to 3,500 top Udemy courses anytime, anywhere. Maths: Calculus, Linear Algebra, Statistics, Naive Bayes Methods: Neural Networks, Deep Learning, PCA, Scikit-learn, Tensorflow, Keras Machine: Python, Cloud Computing, Colab Insights into real life projects and how to apply the concepts Do you want to master Machine Learning (ML) - the key field of the future? ML is the core of artificial intelligence and will transform all industries and all areas of life. This comprehensive course covers the three M's Maths, Methods and Machine, and is easy to understand.

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.

November 8 is National STEM Day, a day celebrating science, technology, engineering, and math. The STEM field of 20 years ago looks very different than the one of today and will look very different in even just five years. Inspired by the air shows he saw growing up, Bill Marx went into the aerospace field in the mid-90s. Now, he is the chief technology officer at Intuitive Research and Technology Corporation. "A lot of the stuff you used to use a supercomputer for 30 years ago," Marx said.

Today, machine learning influences research and consumer products and is leading to breakthroughs across industries like healthcare, manufacturing, finance, and retail. In the field of reinforcement learning, machine learning meets the real world when applied to robotics. Knowing this, how can we ensure students are skilled and prepared to leverage the power of this technology? Intermind Co. is an education group bringing academic programs from leading universities on subjects like machine learning and artificial intelligence to international college students. We recently created a project-based learning experience around the use of Robot Operating System (ROS), the leading open-source framework for writing robot software, and AWS RoboMaker, a service that helps develop, test, and deploy intelligent robotics applications at scale.

Since this phenomenon was first observed, researchers have attempted to develop methods which produce models that are robust to adversarial perturbations under specific attack models (Wong and Kolter (2018); Sinha et al. (2018); Raghunathan et al. (2018); Mirman et al. (2018); Madry et al. (2018); Zhang et al. (2019)). As machine learning proliferates into society, including security-critical settings like health care (Esteva et al. (2017)) or autonomous vehicles (Codevilla et al. (2018)), it is crucial to develop methods that allow us to understand the vulnerability of our models and design appropriate countermeasures. Additionally there is a growing literature on the theory of adversarial examples. Many of these results attempt to understand adversarial examples by constructing examples of learning problems for which it is difficult to construct a classifier that is robust to adversarial perturbations. This difficultly may arise due to sample complexity (Schmidt et al. (2018)), computational constraints (Bubeck et al. (2019); Degwekar et al. (2019)), or the high-dimensional geometry of the initial feature space (Shafahi et al. (2019); Khoury and Hadfield-Menell (2018)).