Data is eating the world so Andrew Ng wants to make sure we radically improve its quality. "Data is food for AI," says Ng, and he is launching a campaign to shift the focus of AI practitioners from model/algorithm development to the quality of the data they use to train the models. Landing AI, the startup Ng founded to bring AI to traditional industries, today announced a competition to get the best performance out of a fixed model by improving the quality of the data. The top three winners will be invited to a private roundtable event with Andrew Ng to share ideas and explore how to grow the data-centric movement. In addition, DeepLearning.AI, an education startup Ng also founded, is launching an online course to teach his data-centric approach to a worldwide audience on Coursera (which Ng co-founded in 2012).
Many guides give you advice on how to get started in data science: which online courses to take, which projects to implement for your portfolio, and which skills to acquire. But what if you got started with your learning journey, and now you are somewhere in the middle and don't know where to go next? After finishing my Data Scientist nanodegree at Udacity, I was at that middle point. I had built a foundation in various data science topics -- ML, deep neural networks, NLP, recommendation systems, and more -- and my learning curve had been very steep. So I felt that simply taking another online course wouldn't yield as many "things learned per day."
Are you a working professional and looking for the best advanced data science courses? If yes, then you are in the right place. In this article, you will find the 7 Best Data Science Courses for Working Professionals. To gain data science skills, there are numerous courses available. So, without wasting your time, let's start finding the Best Data Science Courses for Working Professionals– This is a Nano-Degree Program offered by Udacity.
This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays.
YouTube is a great platform for learners and has some best channels for learning data science. That's why I thought to share with you the 15 Best YouTube Channels to Learn Data Science. So if you are planning to learn data science, then these data science YouTube channels will help you to understand the fundamentals of data science. Now without any further ado, let's start finding the best youtube channels to learn data science- Math is essential for data science and machine learning to understand how machine learning algorithms work. So if you want to learn math concepts, then you should check this YouTube channel.
Python is one of the most widely used programming languages in the data science field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you are looking for the Best Books on Data Science with Python, then you should check these books. In this article, you will find 8 Best Books on Data Science with Python. These books will give you in-depth knowledge starting from basics to advanced level.
Published Tuesday, May. 4, 2021, 9:11 am With tremendous data being generated every second, it is not difficult to imagine the potential of the many vital insights hiding in the data. Today, organizations focus on analyzing this collected data to discover insights into crucial business-related questions: How did the sales perform against estimated target sales in the last quarter? Are older customers contributing more to sales? Which customers should be given coupons? Let us understand how data science is helping organizations answer questions like these.
What is the difference between the Data Analyst, Machine Learning Engineer, and the Data Scientist Nanodegree programs? The Data Analyst program is designed for people with some data analysis experience and little-to-no programming experience. Students will learn to analyze data using Python and SQL, to wrangle and clean messy data, to use applied statistics to test hypotheses, and to create data visualizations. Graduates of this program will be prepared for data analyst positions. The Data Scientist Nanodegree program is designed for students with strong programming and data analysis skills, as it is the next step for graduates of the Data Analyst Nanodegree program.
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.
So you want to learn Data Science with R? Good Decision! Because R programming has various statistical and graphical capabilities. R has a huge variety of libraries to perform statistical analysis. Some most powerful visualization packages in R are ggplot2, ggvis, googleVis, and rCharts. So if you are looking for the Best Online Courses for Data Science with R, then this article will help you.