Valuable lessons: 5G Network: How It Works, and Is It Dangerous?

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.

Valuable lessons: 5 Facts You Didn’t Know About 5G Wireless

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.

Valuable poetry: “Holy Sonnet 10: Death, Be Not Proud” by John Donne (1572-1631)

https://classicalpoets.org/wp-content/uploads/2016/01/donne.jpg

About the poet:

John Donne (born January 22, 1572 – died March 31, 1631) shifted dramatically in his life: The early Donne was the passionate lover and rebel of sense; the later Donne, a man consumed with his own spiritual journey and search for truth.

Donne is known as the first and greatest of metaphysical poets—those of a genre in which “the most heterogeneous ideas are yoked by violence together; nature and art are ransacked for illustrations, comparisons, and allusions,” as essayist and critic Samuel Johnson put it.

Here, Donne has taken a Romantic form and transformed a transcendental struggle of life and death into a quiet ending, one in which death “shall be no more.”

Where Johnson spied cumbersome force, Donne’s style dazzles with soft and calm brilliance, even in the cascade of calumnies against the great “equalizer” Death. “Fate, chance, kings and desperate men” are yoked together, not in bondage but in freedom, in their power to inflict and manipulate death at will. The panorama of life and legacy has overcome death time and again, yet Donne expounds the expansive exploitation of death in one verse.

It is the will of man that triumphs over the cessation of life, the will to believe in what cannot be seen, to dismiss “poor death” as mere “pictures” compared to the substance of life infused with the Spirit.

Death, be not proud, though some have called thee
Mighty and dreadful, for thou art not so;

No bragging rights for Death, according to the poet, who in the first two lines of his sonnet denounces in apostrophe the end of life, “not proud,” “not so.”

“Mighty and dreadful,” two weighty terms, do not belong nor confer any majesty on death. “Thou are not so.” A simple statement, a certain indictment, and the poet has dispensed with Death, who is ponderous, no preposterous for the previous fears His presence has impressed on mankind.

For those whom thou think’st thou dost overthrow?
Die not, poor death, nor yet canst thou kill me.

In this neat conceit, Death himself is fooled, limited by the surface. “Thou think’st thou dost overthrow,” the monarch of destruction is an impoverished exile, removed forever more from the room of imperious prominence. “Poor death” is now the object of pity, the last enemy that will be thrown into the lake of fire.

From rest and sleep, which but thy pictures be,
Much pleasure; then from thee much more must flow,
And soonest our best men with thee do go,
Rest of their bones, and soul’s delivery.

The poet compares death not to a savage desecration, nor a fatal, final battle, but instead an extension of any easy rest, one from which a man receives “much pleasure.” ”Rest and sleep” as “pictures,” the poet condescendingly remarks, bring death into the secondary status of demeaning dimension. Men’s bones receive a welcome respite, and their soul the final delivery from this earth. Death has nothing to brag about, for death is put in comparison with rest, with sleep, with regenerative silence. Death does not catch the prey of frail men, but instead sets men free, and without fail.

Thou art slave to fate, chance, kings, and desperate men,
And dost with poison, war, and sickness dwell,
And poppy or charms can make us sleep as well?
And better than thy stroke; why swell’st thou then?

Here, death as deemed a slave, a unique trope, one, which the poet fashions with wit and wisdom. “Fate” is far greater the force than the end of life which menaces many men. “Chance” is a game, a mere trifle, a toy which men gamble with, whether ending their fortunes or their lives. “Kings” put evil rebels, madmen, and threats to the state, to death. No one escapes the justice, the rule, the righteousness of the king, who even in passing, his dynasty passes on: “The King is dead. Long live the King!” is proclaimed from death to life, where the children of yesteryear become the rulers of today and the progenitors of the future. Death, mere bystander, ushers in the transitions of power.

As for the company of death, the poet outlines simply “poison,” natural or otherwise, which can slay a man in minutes or in hours. Poisons which have ended kings and queens, eradicated vermin and other pestilences, even drugs which prosper and prolong life began as poisons which in improper doses kill, and quickly.

Whether the vain ragings of craven men or glory on the battlefields, “war” covers a range of reigns and rights, ponderings and possibilities. Death is not even a scavenger, but a frustrated element pushed to the limit, expected to do the bidding of the common folk and the ruling elite, the final weapon which man overcomes even in being overcome.  In war, where men die for country, they live forever in the memory of their countrymen, mocking Death who has aided their eternity.

“Sickness” is the necessary pause for men who cannot contain their passions, for the growing race of human beings who run the race with no thought to running out. Sickness is the crucial agent that brings a long and much-needed arrest to those who inflict harm on their bodies, who resist the bounds of natural appetite. Sickness also is the final sign, the moments when a man who departs knows well that his time is short, and so the stultifying stops of pains and coughs at least buy him time to say “good-bye.”

“Poppy or charms can make us sleep as well.” “As well” communicates “in comparison” and “in addition,” gaily sporting with the super-abounding grace of nature’s wonders, which man has contrived to ease his pain and quicken his rest. “Poppy” is a joyful word, a colorful, childlike flower winding away with careless wonder in the wind. “Charms,” whether magical or romantic, are bewitching and bewailing, at least for the one who has fallen beneath their spell. Sometimes, the simple charm of a smiling face suffices more, traced with the soft face of a poppy gladly handed to a loved one. And so, Death is outdone once again!

One short sleep past, we wake eternally,
And death shall be no more, death, thou shalt die.

“Sleep” appears again, but not in conjunction with rest; instead, rest leads to life eternal, where man will no longer need to rest, fashioned as he will be in a body that does not age, that will never flag or fail, Donne decrees. Death is further impoverished, ruined, left desolate. Man in eternal life witnesses death succumbing to himself. “Death shall be no more,” the poet proudly yet dulcetly declares, not even bothering to speak to death. So certain, so final, so enriched with vigor, the poet then whispers, yet loudly of the import of the paradox: “Death, thou shalt die.”

Death dies, or is Death dying? What a wicked end, the poet has mocked, derided, denounced, and diminished death into a cruel joke, a maxim which maximizes the power of the man reborn, trusting in a higher power to infuse him with eternal life, forever inoculating him from the subtleties of war, poison, and sickness all. Fate is fated to disappear, chance has become certainty, kings of limited renown are dethroned, and desperate men now hope. “Death, thou shalt die.” Death is now bereft of pride, like a witless cowboy who has shot himself in the foot, powerless and wounded, and by his own stroke.

Donne indeed has done and dispensed with Death, and mortal man evermore may rejoice!

Arthur Christopher Schaper is an author and teacher who lives in Torrance, CA. He writes several blogs including Schaper’s Corner.

Originally published on The Epoch Times.

Death, be not proud, though some have called thee
Mighty and dreadful, for thou art not so;
For those whom thou think’st thou dost overthrow
Die not, poor Death, nor yet canst thou kill me.
From rest and sleep, which but thy pictures be,
Much pleasure; then from thee much more must flow,
And soonest our best men with thee do go,
Rest of their bones, and soul’s delivery.
Thou art slave to fate, chance, kings, and desperate men,
And dost with poison, war, and sickness dwell,
And poppy or charms can make us sleep as well
And better than thy stroke; why swell’st thou then?
One short sleep past, we wake eternally
And death shall be no more; Death, thou shalt die.

Meaning of the Poem

Death is a perennial subject of fear and despair. But, this sonnet seems to say that it need not be this way. The highly focused attack on Death’s sense of pride uses a grocery list of rhetorical attacks: First, sleep, which is the closest human experience to death, is actually quite nice. Second, all great people die sooner or later and the process of death could be viewed as joining them. Third, Death is under the command of higher authorities such as fate, which controls accidents, and kings, who wage wars; from this perspective, Death seems no more than a pawn in a larger chess game within the universe. Fourth, Death must associate with some unsavory characters: “poison, wars, and sickness.” Yikes! They must make unpleasant coworkers! (You can almost see Donne laughing as he wrote this.) Fifth, “poppy and charms” (drugs) can do the sleep job as well as Death or better. Death, you’re fired!

The sixth, most compelling, and most serious reason is that if one truly believes in a soul then Death is really nothing to worry about. The soul lives eternally and this explains line 4, when Donne says that Death can’t kill him. If you recognize the subordinate position of the body in the universe and identify more fully with your soul, then you can’t be killed in an ordinary sense. Further, this poem is so great because of its universal application. Fear of death is so natural an instinct and Death itself so all-encompassing and inescapable for people, that the spirit of this poem and applicability of it extends to almost any fear or weakness of character that one might have. Confronting, head on, such a fear or weakness, as Donne has done here, allows human beings to transcend their condition and their perception of Death, more fully perhaps than one might through art by itself—as many poets from this top ten list seem to say—since the art may or may not survive may or may not be any good, but the intrinsic quality of one’s soul lives eternally. Thus, Donne leaves a powerful lesson to learn from: confront what you fear head on and remember that there is nothing to fear on earth if you believe in a soul.

Source: https://classicalpoets.org/2013/01/31/poetry-analysis-death-be-not-proud-by-john-donne/

Please search; classicalpoets.org 10 greatest poem ever written.

With respect.

Valuable lessons: Careers in Machine Learning.

https://cdn.educba.com/academy/wp-content/uploads/2018/04/Career-in-Machine-Learning.jpg

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.

Valuable lessons: The Uncertainty in the Machine Learning Job Hunt.

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.

Valuable lessons: 10 Machine Learning Methods that Every Data Scientist Should Know.

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.

Valuable lessons: 9 Applications of Machine Learning from Day-to-Day Life

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.

Valuable lessons: Why Machine Learning is important? An In-depth analysis.

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.

Valuable lessons: How To Develop a Machine Learning Model From Scratch

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/

https://towardsdatascience.com/machine-learning-general-process-8f1b510bd8af

With respect.