Python for Machine Learning ,Learn Python from Machine Learning Projects

We noticed that when people ask about issues in their machine learning project, very often it is not specifically a problem in machine learning but a problem in the programming language they use. It is sad to see someone distracted by the language, such as misunderstanding the error message that the Python interpreter gave. If we know more about working in the Python ecosystem, we can be much more efficient and focused on the machine learning problem itself.

If you already finished a book in Python but still don’t feel comfortable using the language for your project, this new Ebook—in the friendly Machine Learning Mastery style that you’re used to—is all you need.

Using clear explanations and step-by-step tutorial lessons, you will learn the underlying mechanics of the Python language, the tools in its ecosystem, tips and tricks, and much more.

About this Ebook:


Read on all devices: PDF format Ebook, no DRM
Tons of tutorials: 33 step-by-step lessons, 479 pages
Foundations: Covering the language features in Python that you won’t find in another language, and more
Show you the toolbox: A wide variety of topics to show you what’s in the Python ecosystem that can help your project, from debugging to deployment
Working code: 308 Python (.py) code files included
Bonus: A free NumPy cheat sheet in PDF format enclosed!

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The role of artificial intelligence in today’s digital advertising

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By Manu Gupta

In recent years, the world has been more inclined toward technological advancement and the emergence of new-age technologies in the digital space affects every sector throughout the world. As a result of this, improvements in various machine learning techniques, like AI, have been playing a big part in digital advertising.

According to The Insight Partners’ analysis, the worldwide Artificial Intelligence in the marketing sector is estimated to reach US$ 107,535.57 million by 2028, growing at a 31.6% compound annual growth rate (CAGR). However, AI is transforming not just the overall operations, but the digital advertising landscape as well, from chatbots and virtual assistants to content development and user experience upgrades, among other things.

Digital Advertising benefits from the use of Artificial Intelligence.

AI helps to make appropriate judgments, AI thinks like a human to speed up and simplify the planning as well as execution process. Once the requirements are implemented and the inputs are provided, AI automates the entire routine procedure. A lot of growth marketing agencies are making use of AI & Machine learning. ET Medialabs is one such agency that has been successfully capitalizing on AI & Machine learning in order to provide sustainable business growth to its partner brands. At ET Medialabs, we understand the importance of moving with time and have made Artificial Intelligence and Machine learning a part of our daily execution processes. Our daily reporting contains the full-funnel view of all campaigns across Google and Facebook and helps us make informed decisions to drive sustainable growth.

AI offers many other benefits as well, as mentioned below –

Efficient Data Monitoring: It is common knowledge that marketing without data is like driving with your eyes closed. Taking data-backed decisions helps to drive real impact and real results through all the marketing channels.

More Effective Advertising: With AI’s data-driven research, businesses can now create a more effective advertising strategy for their company. By using technology, businesses can now forecast and improve the user experience and improve the user experience to meet their needs based on their buying patterns and decisions.

Increased Productivity and ROI: Using AI in Digital Advertising increases productivity because the operations are automated based on the instructions supplied. AI can also figure out which content works best and assist with content selection. As a consequence, the appropriate content reaches the right audience, resulting in a higher Return on Investment (ROI).

Enhances User Experience: Machine Learning (ML) is a concept used by Artificial Intelligence (AI) to learn about its users. It can analyze their habits and display content tailored to their preferences, keeping the audience interested and engaged. People are more inclined to acquire products or services if they have a pragmatic user experience.

further details please visit: https://www.financialexpress.com/brandwagon/the-role-of-artificial-intelligence-in-todays-digital-advertising/2541323/

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WHY IS NETWORK ANALYTICS IMPORTANT IN THE NEW ECONOMY?

As the world becomes more data-dependent, our tolerance for network unavailability or even visible lag is growing. Weak network performance, unlike in previous periods of the digital world, is now a challenge to our productivity, lifestyles, and ultimately even lives. Simultaneously, network infrastructure is growing into a multi-party architecture in which a single data stream interacts with hundreds of distinct providers, any of which could constitute a weak connection between program and user. This is compelling digital enterprises to become more aggressive in their network management and monitoring, fuelling demand for progressively complex and intelligent analytics systems. Here is why Network Analytics is important in the new economy:


Insight into Operations

It’s a basic networking notion that you can’t manage what you can’t see or understand. That’s why many firms are turning to younger generations of AI-powered analytics, which could not only analyze the performance of information faster and more precisely than software systems but can also flexibly shift their focus to spot anomalies and data trends that would otherwise go undetected.

The important part is that current analytics incorporates a wide range of variables to guarantee that networks are not only functioning but also efficient. Furthermore, intelligent analytics, like data patterns, can develop dynamically, which means they can keep up with new deployments and applications without the direct control of developers or network operators.

We may expect the pace of business to pick up in a digital economy, even as profit margins compress and prospects emerge from precisely focused, segmented markets. This means that network resource consumption, load balancing, and a variety of other operations must be moved to near-real-time to make sure that the data and services can be used to their full potential. With 5G networks and the Internet of Things (IoT) linking everything from automobiles to smart sensors, performance degradation would be far more severe than a few seconds of lag while watching the latest viral video.

Equally essential is the opportunity to lower present network administration costs and complexity. However, by installing intelligent agents in network infrastructure, enterprises may rapidly identify the source of any problem, redirect traffic around the impacted systems, and then perform repairs at a far faster rate than in a traditional management system. Even this degree of corrective action will become unusual as the intelligence embedded throughout the system will be able to spot little flaws long before they become huge problems, allowing the remedy to be applied before the client is even aware of the problem.
Honest Networking

On a strictly operational basis, AI does not increase the performance of the network. It can also investigate traffic patterns and other data sets to verify networks are used for their primary purpose and to prevent hacking and data theft.

Organizations can detect patterns disclosing all types of scams using massive data collection and high-speed intelligent analytics, such as fraud rings committing identity theft, falsification, and other offenses, and also attempts to create false identities, take over profiles, and send false information to obtain funds. Furthermore, many of these patterns contain digital traces that allow detectives to track down the culprits.

read more : https://www.analyticsinsight.net/why-is-network-analytics-important-in-the-new-economy/

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TOP 10 US-BORN COMPANIES PAYING A FORTUNE FOR DATA SCIENTISTS IN INDIA

 

Developing countries are buzzing with new ways of reinventing themselves and in the process, are including diverse opportunities

Pandemic has seen the emergence of new cultures and work habits spawning across the globe. While Indians are struggling with keeping up with the pace of digitalization and making it inclusive, developing countries are buzzing with new ways of reinventing themselves and in the process, are including opportunities for the people from other countries yearning to realize their dollar dreams. A report was released by Glassdoor in February, highlighting the 50 best jobs in America in 2022. Among all the jobs, positions in technology grabbed the most rankings with data science jobs in the third-place only after statisticians and information security officers. So, if you are a data scientist or want to be one, dreaming of the American dreamland, only your ignorance should be the limit. These top 10 US-born companies hiring data scientists can reward you with attractive pay.


Numerator:

Salary: $1,22,000 to $1,34,000 p.a.

A data science company based in the US, known for its trendsetting methods in reinventing the market research with first-party data, it is able to provide companies with real-time insights. Their USP lies in suggesting the kind of promotion to get the maximum turnout in terms of consumer behaviour. Working at numerator is enriching with good team support. The overall Glassdoor rating of 4.1 out of 5.


Spins:

Salary: $74,000 to $1,29,000 p.a.

It is a wellness-based data processing company, that monitors market trends specifically in the natural products sector. Apart from providing consumer insights to retail outlets, it also helps in weighing their performance, consumer engagement, and finding new market opportunities. Want to have work-life balance, and a wee bit of flexibility, then choose Spins. With an overall rating of 3.6, it is a great place to work if you are looking for a good team and uncomplicated work culture.
SAS Institute:

Salary: $1,01,347 p.a.

An independent vendor in business analytics market, it leverages innovation as its deriving force. It has clients currently being served at more than 70,000 sites and the number is only growing. It provides companies instant access to data analytics through its ever-evolving methodologies in market research, which they have named ‘The Power to Know’. Glassdoor rates carry an overall rating of 3.9 for its engaging work environment and amazing team.
MU Sigma:

Salary: $56,000 to $65,000 p.a.

MU Sigma calling its methods ‘Art of Problem Solving’ itself shows its vision toward designing innovative solutions for its clients. They achieve it essentially through data mining and machine learning consulting not just to step up the competition but to use data analytics in a unique way. It is considered one of the amazing places to work and at the same time grow and a great place to kick-start a data science career. Its overall Glassdoor ratings stand at 3.2.
Cloudera:

Salary: $1,43,000 to $1,54,000 p.a.

Specialized in cloud-native services, provides enterprise data cloud for the entire data-cloud lifecycle – ingesting data and experimentation, data warehousing, and using machine learning to build and deploy models. Working with Cloudera gives employees a great deal of positive energy for the importance they place on people and provide space for employees to grow. The ‘Unplug Days’ is the best thing you can look forward to at Cloudera. It carries an overall rating of 3.5 on Glassdoor.
Splunk

Salary: $81,000 to $1,68,000 p.a.

#TurnDataIntoDoing is what they stand by, to deliver hybrid data solutions. Primarily, a data service provider in the IT security domain also specializes in enterprise observability, unified security, and custom applications, all while helping its clients to derive insights from context-specific data. As a company with an overall rating of 4.1 on Glassdoor, a four-day workweek and people-oriented cooperative work culture, and the prospect of forays outside its conventional domains, definitely it makes for an intelligent choice for a data science job aspirants.
Biz2credit

Salary: $4,01,000 p.a.

A fintech company is fast transforming itself into a SaaS digital lending platform, and plans to leverage an AI-powered digital banking platform to automate business lending. They ensure companies provide a user-friendly experience for small businesses, using cloud-based technology and help them expand into new markets. Want to be treated as a person rather than an employee, this is the place you need to look for. With a great office environment and a collaborative team, you will get to learn every single day. It has an overall Glassdoor rating of 5.
Unified:

Salary: $1,33,000 to $1,43,000 p.a.

A paid social advertising solutions development company, which integrates expert services with advanced technologies to deliver at scale solutions, for companies to reap maximum ROIs. Businesses, which utilize their services can expect value-based insights that help run their businesses sustainably and profitably. A great company for a beginner, for them to learn a lot from the training programs. If you are fond of fun activities and paid vacations, maybe Unified is the right place! For this company, Glassdoor ratings stand at 3.9
Orbital Insight:

Salary: $1,32,000 to 1,44,000 p.a.

A geospatial analytics company collects data from satellite images to provide insights into environment-dependent activities and businesses. It can sift through millions of images, combined with AI and generate usable insights in no time. With awesome technology, good use cases, and visionary leadership, it makes for an ideal job destination. The overall Glassdoor ratings for Orbital Insight add up to 5.
Devo:

Salary: $79,000 to $1,00,00 p.a.

Touted as the only cloud-native logging service provider, it is the leader in providing clients with security analytics. It employs uncompromised data collection methods for the decision-makers to take bold actions. Though Glassdoor ratings stand at 2, most employees find it an interesting place to work because of its innovative products and the diversity of the company.

Read more : https://www.analyticsinsight.net/top-10-us-born-companies-paying-a-fortune-for-data-scientists-in-india/

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Developing a Python Program Using Inspection Tools

Python is an interpreting language. It means there is an interpreter to run our program, rather than compiling the code and running natively. In Python, a REPL (read-eval-print loop) can run commands line by line. Together with some inspection tools provided by Python, it helps to develop codes.

In the following, you will see how to make use of the Python interpreter to inspect an object and develop a program.

After finishing this tutorial, you will learn:How to work in the Python interpreter
How to use the inspection functions in Python
How to develop a solution step by step with the help of inspection functions



Tutorial Overview

This tutorial is in four parts; they are:PyTorch and TensorFlow
Looking for Clues
Learning from the Weights
Making a Copier

PyTorch and TensorFlow

PyTorch and TensorFlow are the two biggest neural network libraries in Python. Their code is different, but the things they can do are similar.

Consider the classic MNIST handwritten digit recognition problem; you can build a LeNet-5 model to classify the digits as follows:

Looking for Clues

If you understand what the above neural networks are doing, you should be able to tell that there is nothing but many multiply and add calculations in each layer. Mathematically, there is a matrix multiplication between the input and the kernel of each fully-connected layer before adding the bias to the result. In the convolutional layers, there is the element-wise multiplication of the kernel to a portion of the input matrix before taking the sum of the result and adding the bias as one output element of the feature map.

While developing the same LeNet-5 model using two different frameworks, it should be possible to make them work identically if their weights are the same. How can you copy over the weight from one model to another, given their architectures are identical?

You can load the saved models as follows:

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Ethical Issues in Artificial Intelligence

Niraj DubeyAI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. Artificial intelligence (AI) may still be far from the stuff of science fiction, but its impact on industries and society is profound and getting more prevalent.
With its subset, machine learning, AI helps create safer workplaces, easier access to information and reliable health diagnoses. Artificial intelligence is also used in virtual assistants, self-driving cars, facial recognition, and recommendation systems in social media, entertainment platforms, and e-commerce. The potential of AI is limitless, but as this technology advances, the risks also increase. One of the problematic issues with unrestrained AI is data privacy violations, like what Facebook did when it gave Cambridge Analytica access to the AI-collected personal data of more than 50 million users. Tech giants like Microsoft, Facebook, Google, and others are building teams to address ethical issues that result in the wholesale collection of data. Clearly, businesses and industries are responsible for ensuring that AI development is ethical and unbiased. The various factors that justify the artificial intelligence ethics are like:-
Customize AI ethical framework to suit your industry
Different companies use technologies differently. An organization serious about building an ethical AI should unequivocally express its ethical standards, including naming all its stakeholders and how the standards will be maintained. Moreover, risk mitigation should be baked into the framework. With this method in place, the ethical standards that the different stakeholders-product developers, data collectors, managers, and owners-should comply with are easily determined.
Conform with global AI ethical guidelines
Artificial intelligence has the potential to raise the global gross domestic product (GDP) by 14% by the year 2030. This game-changing impact makes it imperative for businesses to take advantage of AI. International organizations, such as UNESCO, developed a framework for member-states to adopt and ensure that disruptive technologies such as AI benefit the greatest number of people in a bid to ensure that AI builds are trustworthy and human-centric.
Security
Another vital concern for building ethical AI is data privacy and security. This concern becomes apparent when an organization has no governance or data strategy set up at the project’s onset. Privacy, however, isn’t the sole concern when it comes to data. Take companies that deal in financial services. Often, they collect confidential data that needs added security measures. The ideal data partner would have various security options to meet the clients’ requirements and a robust security system to protect the clients’ data and prevent data breaches. Moreover, the data partner should comply with the data regulations specific to the industry and the area.
AI-powered Addiction
Smartphone app makers have turned addiction into a science, and AI-powered video games and apps can be addictive like drugs. AI can exploit numerous human desires and weaknesses including purpose-seeking, gambling, greed, libido, violence, and so on. Addiction not only manipulates and controls us; it also prevents us from doing other more important things-educational, economic, and social. It enslaves us and wastes our time when we could be doing something worthwhile. When I talk about this topic with any group of students, I discover that all of them are “addicted” to one app or another. It may not be a clinical addiction, but that is the way that the students define it, and they know they are being exploited and harmed. This is something that app makers need to stop doing: AI should not be designed to intentionally exploit vulnerabilities in human psychology.
Isolation and Loneliness
Society is in a crisis of loneliness. For example, recently a study found that “200,000 older people in the UK have not had a conversation with a friend or relative in more than a month”. This is a sad state of affairs because loneliness can literally kill. It is a public health nightmare, not to mention destructive of the very fabric of society: our human relationships. One might think that social media, smartphones, and AI could help, but in fact they are major causes of loneliness since people are facing screens instead of each other. Loneliness can be helped by dropping devices and building quality in-person relationships. In other words: caring. This may not be easy work and certainly at the societal level it may be very difficult to resist the trends we have already followed so far. But resist we should, because a better, more humane world is possible. Technology does not have to make the world a less personal and caring place-it could do the opposite, if we wanted it to. If we instead find our humanity not in our brains, but in our hearts, perhaps we will come to recognize that caring, compassion, kindness, and love are ultimately what make us human and what make life worth living. Perhaps by taking away some of the tedium of life, AI can help us to fulfill this vision of a more humane world.

The author is Sr. Faculty – GCET Jammu & Cyber Passionate – (J&K)

link : https://www.dailyexcelsior.com/ethical-issues-in-artificial-intelligence/

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