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Data science is an exciting and rapidly growing field that involves extracting insights and knowledge from data. To land a top data science job, it is important to have a solid foundation in key data science skills, including programming, statistics, data manipulation and machine learning.

Fortunately, there are many free online learning resources available that can help you develop these skills and prepare for a career in data science. These resources include online learning platforms such as Coursera, edX and DataCamp, which offer a wide range of courses in data science and related fields.

Data science and related subjects are covered in a variety of courses on the online learning platform Coursera. These courses frequently involve subjects such as machine learning, data analysis and statistics and are instructed by academics from prestigious universities.

Here are some examples of data science courses on Coursera:

One can apply for financial aid to earn these certifications for free. However, doing a course just for certification may not land a dream job in data science.

Kaggle is a platform for data science competitions that provides a wealth of resources for learning and practicing data science skills. One can refine their skills in data analysis, machine learning and other branches of data science by participating in the platforms challenges and host of datasets.

Here are some examples of free courses available on Kaggle:

Related:9 data science project ideas for beginners

EdX is another online learning platform that offers courses in data science and related fields. Many of the courses on edX are taught by professors from top universities, and the platform offers both free and paid options for learning.

Some of the free courses on data science available on edX include:

All of these courses are free to audit, meaning that you can access all the course materials and lectures without paying a fee. Nevertheless, there will be a cost if you wish to access further course features or receive a certificate of completion. A comprehensive selection of paid courses and programs in data science, machine learning and related topics are also available on edX in addition to these courses.

DataCamp is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers interactive coding challenges and projects that can help you build real-world skills in data science.

The following courses are available for free on DataCamp:

All of these courses are free and can be accessed through DataCamps online learning platform. In addition to these courses, DataCamp also offers a wide range of paid courses and projects that cover topics such as data visualization, machine learning and data engineering.

Udacity is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers both free and paid courses, and many of the courses are taught by industry professionals.

Here are some examples of free courses on data science available on Udacity:

Related:5 high-paying careers in data science

MIT OpenCourseWare is an online repository of course materials from courses taught at the Massachusetts Institute of Technology. The platform offers a variety of courses in data science and related fields, and all of the materials are available for free.

Here are some of the free courses on data science available on MIT OpenCourseWare:

GitHub is a platform for sharing and collaborating on code, and it can be a valuable resource for learning data science skills. However, GitHub itself does not offer free courses. Instead, one can explore the many open-source data science projects that are hosted on GitHub to find out more about how data science is used in practical situations.

Scikit-learn is a popular Python library for machine learning, which provides a range of algorithms for tasks such as classification, regression and clustering, along with tools for data preprocessing, model selection and evaluation.The project is open-source and available on GitHub.

Jupyter is an open-source web application for creating and sharing interactive notebooks. Jupyter notebooks provide a way to combine code, text and multimedia content in a single document, making it easy to explore and communicate data science results.

These are just a few examples of the many open-source data science projects available on GitHub. By exploring these projects and contributing to them, one can gain valuable experience with data science tools and techniques, while also building their portfolio and demonstrating their skills to potential employers.

Read this article:
7 free learning resources to land top data science jobs - Cointelegraph

Summary: Humans and computers can interact via multiple modes and channels to respectively gain wisdom and deepen intelligence.

Source: Intelligent Computing

Deyi Li from the Chinese Association for Artificial Intelligence believes that humans and machines have a mutually beneficial relationship.

His paper on machine intelligence, which was published inIntelligent Computing builds on five groundbreaking works by Schrdinger, the father of quantum mechanics, Turing, the father of artificial intelligence, and Wiener, the father of cybernetics.

Schrdinger and beyond: Machines can think and interact with the world as time goes by.

Inspired by Schrdingers book What is Life? The Physical Aspect of the Living Cell, Li believes that machines can be considered living things. That is, like humans, they decrease the amount of entropy or disorder in their environment through their interactions with the world.

The machines of the agricultural age and the industrial age existed only at the physical level, but now, in the age of intelligence, machines consist of four elements at two different levels: matter and energy at the physical level, and structure and time at the cognitive level. The machine can be the carrier of thought, and time is the foundation of machine cognition, Li explained.

Turing and beyond: Machines can think, but can they learn?

In 1936, Turing published what has been called the most influential mathematics paper, establishing the idea of a universal computing machine able to perform any conceivable computation. Such hypothetical computers are called Turing machines.

His 1950 paper Computing Machinery and Intelligence introduced what is now known as the Turing test for measuring machine intelligence, sparking a debate over whether machines can think. A proponent of thinking machines, Turing believed that a child machine could be educated and eventually achieve an adult level of intelligence.

However, given that cognition is only one part of the learning process, Li pointed out two limitations of Turings model in achieving better machine intelligence: First, the machines cognition is disconnected from its environment rather than connected to it.

This shortcoming has also been highlighted in a paper by Michael Woodridge titledWhat Is Missing from Contemporary AI? The World.Second, the machines cognition is disconnected from memory and thus cannot draw on memories of past experiences.

As a result, Li defines intelligence as the ability to engage in learning, the goal of which is to be able to explain and solve actual problems.

Wiener and beyond: Machines have behavioral intelligence.

In 1948, Wiener published a book that served as the foundation of the field of cybernetics, the study of control and communication within and between living organisms, machines and organizations.

In the wake of the success of the book, he published another, focusing on the problems of cybernetics from the perspective of sociology, suggesting ways for humans and machines to communicate and interact harmoniously.

According to Li, machines follow a control pattern similar to the human nervous system. Humans provide missions and behavioral features to machines, which must then run a complex behavior cycle regulated by a reward and punishment function to improve their abilities of perception, cognition, behavior, interaction, learning and growth.

Through iteration and interaction, the short-term memory, working memory and long-term memory of the machines change, embodying intelligence through automatic control.

In essence, control is the use of negative feedback to reduce entropy and ensure the stability of the embodied behavioral intelligence of a machine, Li concluded.

The strength of contemporary machines is deep learning, which still requires human input, but leverages the ability of devices to use brute force methods of solving problems with insights gleaned directly from big data.

A joint future: from learning to creating

Machine intelligence cannot work in isolation; it requires human interaction. Furthermore, machine intelligence is inseparable from language, because humans use programming languages to control machine behavior.

The impressive performance of ChatGPT, a chatbot showcasing recent advances in natural language processing, proves that machines are now capable of internalizing human language patterns and producing appropriate example texts, given the appropriate context and goal.

Since AI-generated texts are increasingly indistinguishable from human-written texts, some are saying that AI writing tools have passed the Turing test. Such declarations provoke both admiration and alarm.

Li is among the optimists who envision artificial intelligence in a natural balance with human civilization. He believes, from a physics perspective, that cognition is based on a combination of matter, energy, structure and time, which he calls hard-structured ware, and expressed through information, which he calls soft-structured ware.

He concludes that humans and machines can interact through multiple channels and modes to gain wisdom and intelligence, respectively. Despite their different endowments in thinking and creativity, this interaction allows humans and machines to benefit from each others strengths.

Author: Xuwen LiuSource: Intelligent ComputingContact: Xuwen Liu Intelligent ComputingImage: The image is credited to Deyi Li

Original Research: Open access.Cognitive PhysicsThe Enlightenment by Schrdinger, Turing, and Wiener and Beyond by Deyi Li. Intelligent Computing

Abstract

Cognitive PhysicsThe Enlightenment by Schrdinger, Turing, and Wiener and Beyond

In the first half of the 20th century, 5 classic articles were written by 3 outstanding scholars, namely, Wiener (1894 to 1964), the father of cybernetics, Schrdinger (1887 to 1961), the father of quantum mechanics, and Turing (1912 to 1954), the father of artificial intelligence.

The articles discuss the concepts such as computability, life, machine, control, and artificial intelligence, establishing a solid foundation for the intelligence of machines (how machines can recognize as humans do?) and its future development.

Read more from the original source:
Machine Intelligence and Humanity Benefit From "Spiral" of Mutual ... - Neuroscience News

Autonomous transport company Aurrigo has improved its driverless vehicles capabilities in a project with Aston University.

Aurrigos airport Auto-Dolly is now able to differentiate between many different objects

The two-year Knowledge Transfer Partnership (KTP) with the university developed a new machine vision solution, using machine learning and artificial intelligence that means the Coventry-based companys driverless vehicles are now able to see and recognise objects in greater detail. This results in improved performance across a wider spectrum of test situations.

Previously the companys driverless vehicles were only capable of detecting that there was an object in their path, not the type of object, so would just stop when they encountered something in their way.

The new computer vision systems, coupled with machine learning and artificial intelligence, are now able to differentiate between different objects, enabling Aurrigos airport Auto-Dolly to differentiate between many different objects airside.

Professor David Keene, CEO of Aurrigo, said: This partnership has allowed us to produce a system which has resulted in our vehicles becoming smarter and more capable and enabled us to expand our operations, particularly with baggage handling in airports worldwide.

Dr George Vogiatzis, senior lecturer in computer science at Aston University, added: This KTP has been a great way for us to work with a new industrial partner whilst applying our expertise in deep learning and robotics to the exciting field of autonomous vehicles.

It is very rewarding to see the success of this collaboration.

The project findings will also be applied to other vehicles in the Aurrigo product range.

Read more from the original source:
Autonomous shuttle gets new capabilities through machine learning ... - Fleet World

Spreadsheets are often used by businesses of all sizes to complete both simple and complex tasks. Machine learning technology advancements have the potential to revolutionise different industries. Spreadsheet usage is meant to be accessible to all types of users, whereas machine learning is usually perceived as being too complex to use. Google is currently attempting to shift that paradigm for its online spreadsheet application Google Sheets. Explore more about the new machine learning add on for Google Sheets right below.

The operation of Google Sheets works in these three steps given below.

Check out the benefits of simple ML or new machine learning addon in Google sheets right below.

The beta version of Simple ML for Sheets is now accessible. A team of TensorFlow developers developed the Google Sheets add-on to make machine learning available to Sheets users with no prior experience with machine learning. Pretrained machine learning models and other no-code features are primarily used to achieve this.

Predicting missing values and identifying abnormal values are the two main ML tasks that this machine learning add-on is intended to support. Nevertheless, Simple ML for Sheets can also be used for more complex use cases like developing, testing, and analyzing machine learning models. It is likely that Simple MLs Advanced Tasks will need to be used, especially for data scientists and more experienced users who want to use Simple ML to make predictions.

For installing Simple ML for Sheets, users should go to the Extensions tab, get over the Add-ons options, and get add-ons. From there, finding and installing Simple ML is a fairly simple process.

Bottom Lines

Even though Simple ML is quick and reasonably accurate, users still need to know how to set up their data and read the newly created model to be successful. This new machine learning addon is very beneficial for the users of Google sheets. Hence, explore this wonderful addon of Google sheets and enjoy the best features to grab success in your business. You can find your business operating smoothly with simple ML.

Read the original:
Google introduces new machine learning add on for Google Sheets - TechiExpert.com

By Subramanyam Reddy, CEO and Founder, KnowledgeHut upGrad

The global Machine Learning market was valued at US$ 6.9 billion in 2018 and is projected to grow at a CAGR of over 43% between 2019 to 2025, as per a Bloomberg report. Against this, ML has also emerged as one of the fastest-growing fields for career seekers, boasting a year-on-year growth rate of 300%, enjoying unprecedented levels of popularity among young professionals. Machine Learnings growth and popularity are rooted in the growing digitization of all sectors across the world, significantly, in education.

Particularly during the pandemic and after, the education sector has had to fast-track the adoption of tech in delivery. AI and ML applications have found their way into revolutionizing the education and EdTech sectors with the technology driving delivery, assessment, and enhanced retention amongst learners. After the USA, India is one of the biggest markets for e-learning solutions in the world.

The autonomous way in which computers learn is in turn creating an impact in how learning happens in classrooms and beyond. Machine Learning (ML)s giant strides in rapidly transforming the field of education in India are expected to continue in 2023 and beyond.

Lets look at some of the trends emerging in the sector this year and beyond:

Personalised learning is emerging as one of the forerunners in the impact areas of ML in education. Across schools and universities in India, personalised learning is gaining traction and it is driven by AI & ML. Analyzing patterns and behaviors, ML aids instructors and teachers in customising learning for different learners needs. The effectiveness of these interventions is also analyzed by ML.

Another emerging trend driven by AI & ML in education is the development of AI-powered tools to aid learning. The shockwaves created by Chat GPT and other AI-powered platforms are making way for curiosity in how these tools will help people learn be it coding, writing better, developing creative concepts, and more. The access to the vast quantum of data and superfast processing capabilities of such platforms generate accurate answers to questions posed. While several may argue that humans cannot match supercomputers in terms of access or processing, the aim of these technologies is not to one-up humans. The approach to learning changes in a fundamental manner with the advent of such tools. What are the outcomes we seek through learning, and how can tech aid those outcomes, becomes a focal point here.

India has sixteen official languages and hundreds of unofficial languages and dialects spoken across the country. Effective communication is often one of the biggest challenges in the public works domain. For effective reach and improved access to information, AI & ML tools and technologies such as NLP play a significant role in helping people learn languages and improve communication and collaboration across geographies. With MLs aid, learning languages can become simpler and more accessible to a larger audience.

When it comes to assessment and evaluation in learning, the human perspective is more often than not, rooted in personal prejudices and biases. The objective perspective is lost in such scenarios, making evaluations a tool to deter rather than advance. The way ML steps into these areas of assessment and evaluation completely change the game. The same assessments then become a path for advancement, through the identification of areas of improvement and existing strengths of the learner.

Overall, the use of ML in education in India is expected to continue to grow in 2023, with more educators and institutions turning to these technologies to improve the learning experience for students.

Read more:
Machine Learning for education: Trends to expect in 2023 - Express Computer