Along from curiosity, we are in the surprise mode too.

1. How the 5G gonna works?
2. What would be the downloadable speed?
3. May be concern about the rates (expenses).
4. How it will impact the work towards the fast pace in this fourth industrial revolution?

I too excited. But the real issue we need to think is the health problems for humans and other creatures. For instance the radiations and heat in the 5G.

1. How the human gonna manage it?
2. The most important question is the other living creatures around the earth. Birds etc.
3. How the birds gonna tolerate?
4. What are the health hazards we need to care in-order to use 5G?

If the 5G will gonna make us smarter or even faster the work do. That’s sounds very good.  Let’s know about the consequences too.

It took me a while to have a reason to write a bit about the 5G. I was started researching and reading about the most relevant article from livescience website regarding 5G and the health issues too. In this article, I would like to share the glimpse of expert views from this article.

I will paste the source link down below. I sincerely encourage you all to visit further to read the full article.

“That’s significant because it will enable new applications that are just not possible today,” said Harish Krishnaswamy, an associate professor of electrical engineering at Columbia University in New York. “Just for an example, at gigabits per second data rates, you could potentially download a movie to your phone or tablet in a matter of seconds. Those type of data rates could enable virtual reality applications or autonomous driving cars.”

“With a massive amount of antennas — tens to hundreds of antennas at each base station — you can serve many different users at the same, increasing the data rate,” Krishnaswamy said. At the Columbia high-Speed and Millimeter-wave IC (COSMIC) lab, Krishnaswamy and his team designed chips that enable both millimeter wave and  MIMO technologies. “Millimeter-wave and massive MIMO are the two biggest technologies 5G will use to deliver the higher data rates and lower latency we expect to see.”

“There’s often confusion between ionizing and non-ionizing radiation because the term radiation is used for both,” said Kenneth Foster, a professor of bioengineering at Pennsylvania State University. “All light is radiation because it is simply energy moving through space. It’s ionizing radiation that is dangerous because it can break chemical bonds.”

In 2018, the National Toxicology Program released a decade-long study that found some evidence of an increase in brain and adrenal gland tumors in male rats exposed to the RF radiation emitted by 2G and 3G cellphones, but not in mice or female rats. The animals were exposed to levels of radiation four times higher than the maximum level permitted for human exposure.

“Everyone I know, including me, is recommending more research on 5G because there’s not a lot of toxicology studies with this technology,” Foster said.

“I think 5G will have a transformational impact on our lives and enable fundamentally new things,” Krishnaswamy said. “What those types of applications will be and what that impact is, we can’t say for sure right now. It could be something that takes us by surprise and really changes something for society. If history has taught us anything, then 5G will be another example of what wireless can do for us.”

SOURCE: https://www.livescience.com/65959-5g-network.html

With respect.

The next generation of wireless connectivity is almost here.

To really care about 5G, we need to know the impacts and the consequences. Moreover, entire globe has started paying attention towards the 5G. Please correct me, if I’m wrong. We heard sometimes, 5G gonna be the superfast. To me personally, using 5G is ultimately secondary. Knowing more about the 5G is matters most. I would love to know, how the user have curiosity upon 5G and what researcher/experts are saying about the 5G’s pros and cons. Let’s look forward and more about 5G.

Here are 5 the facts, I would like to share from fool website. Please click the source link to read the full article.

1. 5G wireless will be available by 2020, or even a bit earlier. Verizon Communications (NYSE:VZ), Alphabet’s (NASDAQ:GOOG) (NASDAQ:GOOGL) Google, and AT&T (NYSE:T) are already testing 5G technologies right now. Google is testing solar-powered drones that can stay up in the sky for as long as five years and beam down 5G signals to users. AT&T and Verizon are taking a more traditional approach and are currently using 5G signals near their respective headquarters. Verizon says it will roll out tests in Boston, New York and San Francisco later this year.
2. But there aren’t any set standards for 5G yet. The international wireless standards body, 3GPP, is still determine the specifications, along with Ericsson, Samsung, Nokia, Cisco Systems, and Verizon. The next generation of wave radio transmissions standards are likely to be set by 2018.
3. 5G will be lightning fast. Verizon says that its 5G network will likely be 200 times faster than the 5Mbps speeds many of its users get on 4G LTE. That means 5G speeds will hit 1 Gbps, which is currently the fastest speed you can get from Google Fiber. At that rate, you’ll be able to download an HD movie in seven seconds. Speeds are expected to increase even higher than 1Gbps as well, as 5G evolves.
4. 5G will likely be the next major fight for wireless carriers, and no one wants to be left out. The major U.S. carriers are all closing the gap on their 4G LTE coverage and speeds, which means they’ll likely latch onto their 5G networks to differentiate themselves. AT&T was dismissive about any type of 5G talk just a few months ago, but is now very open about its 5G plans. The company’s about-face shows just how much carriers don’t want to be seen as falling behind. 
5. 5G will cost more than 4G LTE connections, but probably not much more. According to research by the University of Bridgeport, carriers will likely keep costs around the same as they are now, but you’ll get much faster speeds. That’s because carriers reduce the price of data by a little bit each year. Huawei and Nokia believe 5G will cost more than 4G LTE, but say that the carriers won’t be able to charge too much more than the current rates.

SOURCE: https://www.fool.com/investing/general/2016/02/16/5-facts-you-didnt-know-about-5g-wireless.aspx

With respect.

Here is the career comes in Machine Learning field. I could evaluate myself, the posts I had been shared are a very good one. So far, I covered the most notable and relevant one. When you had passion about the particular fields like Machine Learning. This is the time to know substantially about the overall career. To be quite chronological, this post has to my first post when I started writing about Machine Learning over the last just 4 days.

But when you keep stretching your mind with the precise motive. At some point, you could analyse, still what I can I write and deliver. I never say I made myself as a compulsion to write. It is all about my thought process with regards to it.

Even more I will to agree and quite ashamed to admit, there is a lot to write and which I could not able to think too. Let’s see in the future posts. I’m gonna test and challenge myself.

Overall, when I started reading and researching about this content, particularly, this is one of the article will give you the brighter steps to make a career in Machine Learning. If you had a passion to thrive in this field.

I’m will paste the source link down below. I sincerely encourage you all to read the whole story.

Finally, in this source link, you could able to see the Machine Learning tutorial. Every topic by topic. You can read.

Introduction to Careers in Machine Learning

Machine learning is the field of AI that provides the ability to the system to learn on its own without any human intervention at higher accuracy, due to which it is highly required in the area of Information Technology Industry and the developers working in these technologies are assigned the role of Machine Learning Engineer. Initially, it is followed by the Architect level position whose work is to design the prototype for the applications that needs to be developed, starting salary of the machine learning engineer as per the American website is 100,000 dollars annually.

Education Required for Machine Learning

Machine Learning needs a lot of basic computer science concepts and one should be strong in computer science concepts such as Mathematical, Data Structures and Algorithms subjects like computations, statistics etc. Strong knowledge of basic mathematics is also recommended. Machine Learning is the core component of Artificial Intelligence where one needs to show much interest and enthusiasm in learning these concepts.

• Machine Learning is evolving quite rapidly and gradually nowadays. A lot of technology professionals are required in the coming years in the area of Machine Learning.
• Machine Learning includes technology, mathematics, statistics, business knowledge and many technical and logical skills to excel in this area. Data analysis is one of the main elements of the Machine Learning area where this area mainly depends on data in which the machine learns on its own.
• This requires a lot of valuable data to be processed before a machine is learning itself. A Data Analyst can easily transform his/her career in Machine Learning. Python is the most used programming language in the area of Machine Learning. This is also included in most of the academic programs as well in most of the universities.

Career Path

• The career path initially starts as a Machine Learning Engineer, who will be developing applications that perform some common tasks done by human beings and this will be used for repeated things that will perform without any errors and produces effective results.
• A Machine Learning Engineer role will be followed by the Architect level position in. The next level of a career path in the Architect level will be of some role to design and develop the prototypes for the applications to be developed.
• Even a software engineer with some years of experience can switch their careers in the Machine Learning area. A Python Developer or a data scientist can also easily switch careers in Machine Learning.
• Persons even without any experience in software engineering can also start their careers in Machine Learning if they have some string knowledge in computer science, mathematics, statistics etc.

Job Positions or Application Areas

In the area of Machine Learning, there are different roles available in the information technology industry to pursue the career are such as Machine Learning Engineer, Senior Machine Learning Engineer, Lead Machine Learning Engineer, Machine Learning Engineer Front Office and Back office, Principal Engineer – Machine Learning, Machine Learning Software Engineer, Data Scientist, Senior Data Scientist, Data Scientist IT, Senior Data Scientist IT etc. The Machine Learning Engineer possesses some strong core knowledge of Computer Science concepts, a solid Mathematics background with Statistics as well.

Salary

The national average salary for a Machine Learning as mentioned in another top salary information website Glassdoor.com is $120,931 in the United States.

Career Outlook

• There are also multiple career paths to move after entering into the Machine Learning Engineer area like Artificial Intelligence, Data Science and Data Analytics etc.
• An IT professional with some good communication skills and strong technical skillset with a solid mathematics or statistics background can reach some top heights in their careers like Senior Architects or Senior Subject Matter Experts in the career of Machine Learning or Artificial Intelligence.
• The requirements for the job positions in the area of Machine Learning Engineer in the United States are increasing daily in large numbers. Because of the day to day routine activities or tasks in the large customer based companies, the job handling responsibilities need to be very accurate and error-free for successful business deliverers to the customers.
• Machine Learning Software applications or products are a great need for businesses to maintain the customers’ content data secure, Machine Learning Engineer is one of the best technological advancements available in the market to provide some high complexity business solutions.

Source: https://www.educba.com/careers-in-machine-learning/

With respect.

It will always be with you. That doesn’t mean you won’t succeed. Here, I would like to give of this article. I’m gonna paste the source link down below to read the full article.
In less than a year, I will be deemed worthy by my university of a Bachelors degree. In less than a year, I will be saying goodbye to the place I called home for the last 4 years. In less than a year, I will know where the next chapter of my life begins.

Unfortunately, I’m not there yet. I am really only left with one question that occupies my mind space as I write my cover letters and send off my applications:

So Many Others are Going Through This Too.

It really is as simple as that.

Machine Learning and Data Science are highly competitive fields and I mean highly competitive fields. It’s a typical day. You check out LinkedIn and see the newest opportunities available at well developed start-ups. One catches your eye in particular and you realize: it was only posted 30 minutes ago! You click on the listing to see what the job entails only to see “200+ applicants” listed below the company’s name.

It’s a frustrating cycle. You and I are going through it together, no matter how much it seems like a competition to get a job in the first place. Entrenching yourself in a competitive mindset can be good to push you forward through difficult times, however it may lead you to neglect how similar you are to everyone you are competing with. We are all on our paths to success and we will find it in due time. Securing a good job is not a zero-sum game, simply because there are so many good fits for you.

The more you apply, the quicker you will find that fit. Best of luck on your journey.

SOURCE: https://towardsdatascience.com/the-uncertainty-in-the-machine-learning-job-hunt-a0e785c03a65

With respect.

Jump-start your data science skills.

When I started researching this content. I really wonder, whether this content only to Data Scientist. I don’t think so. These ten steps are absolutely required to those who wanna become a statistician, those who would like to read Deep Learning and started learning Natural Language Processing in AI.

This article from towardsdatascience.com summarizes in the last solid start for further study for advanced algorithms and methods. The writer said there are few more to cover.

I’m gonna paste the source link down below. Please visit further to read the full article.

Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners.

To demystify machine learning and to offer a learning path for those who are new to the core concepts, let’s look at ten different methods, including simple descriptions, visualizations, and examples for each one.

A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a ­­­problem, often a business problem. The aim is to go from data to insight. For example, if an online retailer wants to anticipate sales for the next quarter, they might use a machine learning algorithm that predicts those sales based on past sales and other relevant data. Similarly, a windmill manufacturer might visually monitor important equipment and feed the video data through algorithms trained to identify dangerous cracks.

The ten methods described offer an overview — and a foundation you can build on as you hone your machine learning knowledge and skill:

1. Regression.
2. Classification.
3. Clustering.
4. Dimensionality Reduction.
5. Ensemble Methods.
6. Neural Nets and Deep Learning.
7. Transfer Learning.
8. Reinforcement Learning.
9. Natural Language Processing.
10. Word Embeddings.

All the visualizations of this blog were done using Watson Studio Desktop.

Special thanks to Steve Moore for his great feedback on this post.

Source: https://towardsdatascience.com/10-machine-learning-methods-that-every-data-scientist-should-know-3cc96e0eeee9

With respect.

We are using Machine Learning in our day to day life. We may or may not know, we you are using knowingly or unknowingly. If you start thinking over the last (just) 5 years, you will understand very well. What’s gone so far?

I think this is the right time to see and learn these kinds of apps and tools that we are using.

There is wise line. I would always say wise line. “You should learn to know what is going on across the globe too”.  

As I said, I’m not a tech-savvy at all. But I’m becoming. I would say, if you using or not, it’s would be better, if you know it in advance.

When I started reading this article from Medium website, I really wonder, is this Machine Learning apps?

My thoughts are roaming like a clock about Machine Learning. I just keep thinking. What can I start learning something new from Machine Learning?

I would like to share with purpose too.

Let’s look at it. I’m paste the source link down below to read the full article. I sincerely encourage you all to visit further.

1. Virtual Personal Assistants.
2. Predictions while Commuting.
3. Videos Surveillance.
4. Social Media Services.
5. Email Spam and Malware Filtering.
6. Online Customer Support.
7. Search Engine Result Refining.
8. Product Recommendations.
9. Online Fraud Detection.

How do you Use Machine Learning Daily?

Please comment below.

Originally Published at: Daffodil’s App Development Blog

Source: https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0

With respect.

Here, we need to know why Machine Learning is important. When we are talking about Artificial Intelligence, Machine Learning and Deep Learning are the subset of Artificial Intelligence.

At this moment, after understanding the glimpse of Machine Learning from my earlier posts, this is right time to arise the “Why” factor. Additionally, two more you can see this article such as requirements for better process and terms should know.

To me personally, this is also the learning pace. As I started pursuing Data Scientist course, Machine Learning plays a major role. To really understand, why it is. It is better to rise and research about further.

So, here I’m gonna write the brief about why it is important and an article from techwhippet. I’m gonna paste the source link down below. I sincerely encourage you all to visit further to read the full article.

Why is machine learning important?

Machine learning is very important in our life. The modern world is dependent on the machine because of the development of volumes and easily accessible data, data processing and computerization is not very expensive. It is comprehensible to quickly and in a natural way to create a design and that can analyze greater, accurate information and perform faster and exact output.

Requirements to make a better machine learning process:

• Data construction abilities.
• Algorithms- fundamental and advancement.
• Computerization and iterative procedures
• Innovative
• Unity modeling

We all should know some of these terms related to machine learning:

• A target is known as a label in machine learning.
• A target is known as the dependent variable in the statistic
• In machine learning, a variable is known as a feature.
• In machine learning, a transformation is known as feature creation.

SOURCE: https://techwhippet.com/why-machine-learning-is-important/

With respect.

This is the one of the most important content I was searching to read. To build a Machine Learning model is vital, unless if you are passion about Machine Learning. I started thinking what is needed to write. The process, we had seen in the pictures.

Before looking at the abstract of this content, we should know the simpler meaning of the Machine Learning. “Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves”.

The article from towardsdatascience website, shows you the step by step to build the model. I’m gonna say every sub-headings of the step and rest of the diagrams and formulae, please check the links below.

The following steps covers;

• Define adequately our problem (objective, desired outputs…).
• Gather data.
• Choose a measure of success.
• Set an evaluation protocol and the different protocols available.
• Prepare the data (dealing with missing values, with categorial values…).
• Spilit correctly the data.
• Differentiate between over and underfitting, defining what they are and explaining the best ways to avoid them.
• An overview of how a model learns.
• What is regularization and when is appropiate to use it.
• Develop a benchmark model.
• Choose an adequate model and tune it to get the best performance possible.

This is overall steps to build a Machine Learning model from the scratch. I’m gonna paste the source link down below. I sincerely encourage you all visit further.

SOURCE: https://expertsystem.com/machine-learning-definition/

How To Develop a Machine Learning Model From Scratch

With respect.

The more you read the history, the more you will likely to know. Or even more, you could able to predict the future.

Categorically, If I’m writing, because I read substantially about the past.

To read any subject, you need to read about history on a particular subject. History teaches us a lot. Even more, I started watching History documentary too.

To me personally, when I started learning and delivering massive awareness about climate change, but I’m good and quite understood about what was happened regarding climate change over the last 5 years. Still I need to origin of climate that started deteriorating and I would like to some of the hard facts.

Here, I would like to submit the glimpse of data about History of climate change.

I should pin point the some of the historical moments and facts. An article from BBC news says, a brief history of climate change. In 1824 French physicist Joseph Fourier describes the Earth’s natural “greenhouse effect”. He writes: “The temperature [of the Earth] can be augmented by the interposition of the atmosphere, because heat in the state of light finds less resistance in penetrating the air, than in re-passing into the air when converted into non-luminous heat.”

And in 1896 – Swedish chemist Svante Arrhenius concludes that industrial-age coal burning will enhance the natural greenhouse effect. He suggests this might be beneficial for future generations. His conclusions on the likely size of the “man-made greenhouse” are in the same ballpark – a few degrees Celsius for a doubling of CO2 – as modern-day climate models.

In 1927 – Carbon emissions from fossil fuel burning and industry reach one billion tonnes per year.

In 1965- A US President’s Advisory Committee panel warns that the greenhouse effect is a matter of “real concern”.

In 1972 – First UN environment conference, in Stockholm. Climate change hardly registers on the agenda, which centres on issues such as chemical pollution, atomic bomb testing and whaling. The United Nations Environment Programme (Unep) is formed as a result.

In 1975 – US scientist Wallace Broecker puts the term “global warming” into the public domain in the title of a scientific paper.

In 1989 – Carbon emissions from fossil fuel burning and industry reach six billion tonnes per year.

In 1990 – IPCC produces First Assessment Report. It concludes that temperatures have risen by 0.3-0.6C over the last century, that humanity’s emissions are adding to the atmosphere’s natural complement of greenhouse gases, and that the addition would be expected to result in warming.

In 1992 – At the Earth Summit in Rio de Janeiro, governments agree the United Framework Convention on Climate Change. Its key objective is “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”. Developed countries agree to return their emissions to 1990 levels.

In 2006 – The Stern Review concludes that climate change could damage global GDP by up to 20% if left unchecked – but curbing it would cost about 1% of global GDP.

In 2006 – Carbon emissions from fossil fuel burning and industry reach eight billion tonnes per year.

In 2007 – The IPCC’s Fourth Assessment Report concludes it is more than 90% likely that humanity’s emissions of greenhouse gases are responsible for modern-day climate change.

In 2007 – The IPCC and former US vice-president Al Gore receive the Nobel Peace Prize “for their efforts to build up and disseminate greater knowledge about man-made climate change, and to lay the foundations for the measures that are needed to counteract such change”.

In 2011 – Data shows concentrations of greenhouse gases are rising faster than in previous years.

In 2013 – The first part of the IPCC’s fifth assessment report says scientists are 95% certain that humans are the “dominant cause” of global warming since the 1950s.

Additionally, there is an article from time magazine says, since roughly 1850, atmospheric CO2, the dominant greenhouse gas, has grown at an explosive rate, close to what mathematicians call “exponential.” Human population, GDP and fossil fuel emissions accelerated simultaneously in a similar manner.

Industrial emissions have driven atmospheric CO2 levels from about 280 to 410. Human populations are now surging toward eight billion. A doubling of CO2 from preindustrial levels, which is projected by 2075 — due to the combination of industrial emissions and huge volumes of ancient greenhouse gases rising from melting permafrost – will put the earth at CO2 levels not seen for 35 million years, the last time that Antarctica was ice-free. A quadrupling of CO2 would put us into the extreme hothouse conditions of the Jurassic era.

History says, By the 1930s, at least one scientist would start to claim that carbon emissions might already be having a warming effect. British engineer Guy Stewart Callendar noted that the United States and North Atlantic region had warmed significantly on the heels of the Industrial Revolution.

Callendar’s calculations suggested that a doubling of CO2 in Earth’s atmosphere could warm Earth by 2 degrees C (3.6 degrees F). He would continue to argue into the 1960s that a greenhouse-effect warming of the planet was underway.

While Callendar’s claims were largely met with skepticism, he managed to draw attention to the possibility of global warming. That attention played a part in garnering some of the first government-funded projects to more closely monitor climate and CO2 levels.

1988: Global Warming Gets Real

The early 1980s would mark a sharp increase in global temperatures. Many experts point to 1988 as a critical turning point when watershed events placed global warming in the spotlight.

The summer of 1988 was the hottest on record (although many since then have been hotter). 1988 also saw widespread drought and wildfires within the United States.

Scientists sounding the alarm about climate change began to see media and the public paying closer attention. NASA scientist James Hansen delivered testimony and presented models to congress in June of 1988, saying he was “99 percent sure” that global warming was upon us.

The UN Climate Action Summit reinforced d that “1.5℃ is the socially, economically, politically and scientifically safe limit to global warming by the end of this century,” and set a deadline for achieving net zero emissions to 2050.

This is fair view of history that I took some of the important key points. I sincerely encourage you all to visit further from these three sources. To me, it’s hard to choose the moments and facts. I hope you all will go further. If you have any suggestions, please comment below.

SOURCE: https://www.bbc.com/news/science-environment-15874560

https://www.history.com/topics/natural-disasters-and-environment/history-of-climate-change

https://time.com/5680432/climate-change-history-carbon/

With respect.