Currently IAM pursuing btech in madanapalle institute of technology and science and I have finished national cadet corps c certification . I'm writer as well as social worker.
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of Artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine Learning Types:
Supervised learning:
The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning:
No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end .
Reinforcement learning:
A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximise.
Best language for Machine Learning?
R:
R is a workhorse for statistical analysis and by extension machine learning. Much talk is given to the learning curve, I didn’t really see the problem. It is the platform to use to understand and explore your data using statistical methods and graphs. It has an enormous number of machine learning algorithms, and advanced implementations too written by the developers of the algorithm.
I think you can explore, model and prototype with R. I think it suits one-off projects with an artifact like a set of predictions, report or research paper. For example, it is the most popular platform for machine Learning competitors such as kaggle.
Python:
Python if a popular scientific language and a rising star for machine learning. I’d be surprised if it can take the data analysis mantle from R, but matrix handling in NumPy may challenge MATLAB and communication tools like ipython are very attractive and a step into the future of reproducibility.
I think the SciPy stack for machine learning and data analysis can be used for one-off projects (like papers), and frameworks like Scikit-Learn are mature enough to be used in production systems.
Java:
Implementing a system that uses machine learning is an engineering challenge like any other. You need good design and developed requirements. Machine learning is algorithms, not magic. When it comes to serious production implementations, you need a robust library or you customize an implementation of the algorithm for your needs.
Python is the best language for a reason:
“Python” comes with an assortment of excellent libraries and tools for ML, including Scikit Learn, TensorFlow, ChatterBot, and much more. One of the oldest programming languages, C++ is highly suited for Machine Learning, thanks to its ML repositories like TensorFlow, LightGBM, and Turi Create.
Advantages:
Automation for everything;
A very powerful utility of Machine Learning is its ability to automate various decision-making tasks. This frees up a lot of time for developers to use their time to more productive use. For example, some common use we see in our daily life is social media sentiment analysis and chatbots. The moment a negative tweet is made related to a product or service of a Company, a chatbot instantly replies as first-level customer support. Machine Learning is changing the world with its automation for almost everything we can think of.
Trend’s and patterns identification;
This advantage is a no brainer. All of us interested in Machine Learning technology are well aware of how the various Supervised, Unsupervised and Reinforced learning algorithms can be used for various classification and regression problems. We identify various trends and patterns with a huge amount of data using this technology. For example, Amazon analyzes the buying patterns and search trends of its customers and predicts products for them using Machine Learning algorithms.
Wide range of applications;
Machine Learning is used in every industry these days, for example from Defence to Education. Companies generate profits, cut costs, automate, predict the future, analyze trends and patterns from the past data, and many more. Applications like GPS Tracking for traffic, Email spam filtering, text prediction, spell check and correction, etc are a few used widely these days. Machine Learning is a branch of Artificial Intelligence, the latest trends and applications can be found in Artificial Intelligence Trends in 2020.
Conclusion:
These article would help you to get clear expression about Machine Learning. However we invite you to learn more about Machine Learning go through the website and follow us to know more about Machine Learning.
* The acronym “LASSO” stands for Least Absolute Shrinkage and Selection Operator.
Def: Lasso regression is a type of linear regression that uses shrinkage . Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). This particular type of regression is well-suited for models showing high levels of multicollinearity or when you want to automate certain parts of model selection, like variable selection/parameter elimination.
General Form:
Lasso regularization can be extended to a wide variety of objective functions such as those for generalized linear models, generalized estimating equations, proportional hazards model, and M-estimators in general, in the obvious way.
Given the objective function:
General Formula
the lasso regularized version of the estimator will be the solution to
General form
Where only Beta is penalized while Alpha is free to take any allowed value , Just as Beta0 was not penalized in the basic case.
Advantages:
Regularization;
Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients. This type of regularization can result in sparse models with few coefficients; Some coefficients can become zero and eliminated from the model. Larger penalties result in coefficient values closer to zero, which is the ideal for producing simpler models. On the other hand, L2 regularization (e.g. Ridge Regression) doesn’t result in elimination of coefficients or sparse models. This makes the Lasso far easier to interpret than the Ridge.
Ridge Regression:
Tikhonov regularization, named for Andrey Tikhonov, is a method of regularization of ill- possed problems. Also known as ridge regression, it is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers of parameters. In general, the method provides improved efficiency in parameter estimation problems in exchange for a tolerable amount of bias
In the simplest case, the problem of a near – singular moment matrix (X^T *T) is alleviated by adding positive elements to the diagonals, thereby decreasing its condition number. Analogous to the ordinary least squaresestimator, the simple ridge estimator is then given by
General Formula
where Y is the regressand , X is the design matrix, I is the identity matrix, and the ridge parameter lambda >=0 , serves as the constant shifting the diagonals of the moment matrix. It can be shown that this estimator is the solution to the least squares problem subject to the constraint .
General Formula
which shows that lambda is nothing but the Langrangemultiplier of the constraint. In the case of lambda =0, the ridge estimator reduces to ordinary least square.
Ridge regression is a way to create a persimonius model when the number of predictor variablesin a set exceeds the number of observations, or when a data set has multicollinearity(correlations between predictor variables).
Advantages:
Regularization;
Ridge regression belongs a class of regression tools that use L2regularization. The other type of regularization, L1 regularization, limits the size of the coefficients by adding an L1 penalty equal to the absolute value of the magnitude of coefficients. This sometimes results in the elimination of some coefficients altogether, which can yield sparse models. L2 regularization adds an L2 penalty, which equals the square of the magnitude of coefficients. All coefficients are shrunk by the same factor (so none are eliminated). Unlike L1 regularization, L2 will not result in sparse models.
Lasso vs Ridge Regression:
Ridge regression is an extension for linear regression. It’s basically a regularized linear regression model. The λ parameter is a scalar that should be learned as well, using a method called cross validation that will be discussed in another post.
A super important fact we need to notice about ridge regression is that it enforces the β coefficients to be lower, but it does not enforce them to be zero. That is, it will not get rid of irrelevant features but rather minimize their impact on the trained model.
Lasso is another extension built on regularized linear regression, but with a small twist. The loss function of Lasso is in the form:
L = ∑( Ŷi- Yi)2 + λ∑ |β|
The only difference from Ridge regression is that the regularization term is in absolute value. But this difference has a huge impact on the trade-off we’ve discussed before. Lasso method overcomes the disadvantage of Ridge regression by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant. Therefore, you might end up with fewer features included in the model than you started with, which is a huge advantage.
Conclusion:
Machine learning is getting more and more practical and powerful. With zero knowledge in programming, you can train a model to predict house prices in no time.
We’ve covered the basics of Losso Regression and Ridge Regression.
will discuss what we covered at a high level, and I’ll list some suggested reading so you can dive further into lasso Regression and ridge Regression
Why people are curious to go abroad to do their masters without having money?
In a globalised, well-connected world, employers increasingly value graduates with international experience and education. Studying abroad helps you to learn new languages, appreciate other cultures, overcome challenges of living in another country and gain a greater understanding of the world.
It is believed that candidates having an international degree are able to impress the employers because of the kind of experience they have gained while studying abroad. Therefore, going abroad for studying is a preferred choice for the youth as it enhances their chances of getting good career opportunities.
Six reasons to study in abroad:
Study abroad to explore a new culture::
It is one thing to experience a country’s culture when you visit for vacation, but it’s completely different when you immerse yourself in the culture as you live and study there.
Become More Responsible While Studying in a Foreign Country::
Until now, you may have lived with your parents or guardians, but when you study abroad, you will be in charge of everything from your well-being to your education.
Build a Network of Friends From Around the World::
One of the most obvious reasons to study abroad is to meet people from different places. Creating an international circle of friends is a huge benefit of studying abroad
Experience a different education style::
Indian students used to a passive style of learning will get to experience a more engaging and active learning style when studying abroad
Stand Out to Future Employers with Your Academic Experience Abroad::
Regardless of whether you choose to work in India or at a company abroad, your experience in a foreign country does matter to your future employer.
Improve Your English Skills No Matter What You Study Abroad::
When you study abroad in the US, Canada, the UK, Ireland or the UAE, you are surrounded by English. Not only are your classes conducted in English, but you use English in your free time when you meet with friends and take part in activities on campus.
Requirements:
– Latest Degree Certificates
-Aadhaar
-Valid Passport
-Valid Toefl (Test of English as foreign language) or GRE ( Graduate record examination).
-I20 Document
-Joining letter from foreign University.
-Income ,Caste certificates or ration card
Opportunities:
* Ambedkar overseas vidyanidhi
*Department of Higher education of India
* so many private organization scholarships are there!
It’s a dream of many poorers in India and so many countries including me also. Being bright student , nothing will impact for getting fully funded scholarship in India because politics involvement and Recommendations are more. So don’t loose Hope’s do hardwork as much as you can.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention
Machine learning image
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works . Machine learning and Artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.
* How We Arrived at Our Definition:
As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practical definitions from reputable sources:
“The subfield of artificial intelligence, Machine learning focuses on algorithms that learn. Our research asks how we can build algorithms that automatically improve with experience, taking into account the fundamental laws that define this process.”_The University of Auckland
“Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinley & co
“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” –Carnege Mellon University
Python Vs R: What’s Best for Machine Learning
R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.
Python
Initially released in 1991, Python is a general-purpose programming language that was designed with a philosophy of optimizing the code readability. It is often referred to as a “batteries included” language because of its comprehensive standard library.
Python is used for Machine Learning is a hot topic that has been doing the rounds all over the industry. According to Unbuntu Pit, it’s also ranked 2nd among the list of best programming languages, right after Javascript, and there are really good reasons for that.
The Python programming language was developed in the late 80s and plays a crucial role in powering the internal infrastructure of Google. Python comprises of enthusiastic developers and now it’s been used in the widely used applications of YouTube, Instagram, Quora, and Dropbox. Python is broadly utilized over the IT business and permits simple effort of collaboration inside development groups. In this way, in the event that you need an adaptable and multi-reason programming language with a supporting enormous network of engineers alongside the extendable AI bundles then Python is a top pick.
What’s so special about Python?
Many people don’t know this but Python was conceived as a successor to the ABC language. Guido van Rossum, the guy who designed Python, really liked a few features of the ABC language but also had a fair share of grievances with the language, the biggest issues being lack of extensibility, which gave birth to Python.
Let’s dive deep into the specifics of why people love Python:-
Free and open-source
Even though several of them are, in fact, free and open-source, it’s still one of the features of Python that makes it stand out as a programming language. You can download Python for free, which means that Python developers are able to download its source code, make modifications to it and even distribute it. Python comes with an extensive collection of libraries that support you to carry out your tasks.
Awesome Collection of Inbuilt Libraries
Python offers a vast number of in-built libraries that the Python development companies can use for data manipulation, data mining, and machine learning, such as:-
NumPy — Used for scientific calculation.
Scikit-learn — For data mining and analysis which optimizes Python’s machine learning usability. Panda — offers developers with high-performance structures and data analysis tools that help them reduce the project implementation time.
SciPy — Used for advanced computation.
Pybrain — used for machine Learning
Matplotlib:
Saying that matplotlib is the O.G. of Python data visualization libraries wouldn’t be an overstatement. Despite being over a decade old (the first version was developed in the 1980s), this proprietary programming language is regarded as one of the most sought-after libraries for plotting in the coder community. It was designed to closely resemble Matlab, enabling programmers to stack charts either by putting multiple metrics onto a single chart or multiple charts on a single page.
Seaborn:
may be able to support some more complex visualization approaches but it still requires matplotlib knowledge to fine-tune things.
Bokeh:
is a robust tool for setting up your own visualization server but maybe a bit overkill when creating simple scenarios.
Geoplotlib:
will get the job done if you need to visualize geographic data.
Ggplot:
shows a lot of promise but still has a lot of growing up to do.
Advantages Of Python
● General-purpose language — Python is regarded as a better choice if your project demands more than just statistics. For instance — designing a functional website
● Smooth Learning Curve — Python is easy to learn and easily accessible which enables you to find the skilled developers on a faster basis.
R
R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative. If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance — if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.
Advantages Of R
● Suitable for Analysis — if the data analysis or visualization is at the core of your project then R can be considered as the best choice as it allows rapid prototyping and works with the datasets to design machine learning models.
R is free, open-source code
R is available under an open-source license, which means that anyone can download and modify the code. This freedom is often referred to as “free as in speech.” R is also available free of charge — a second kind of freedom, sometimes referred to as “free as in beer.” In practical terms, this means that you can download and use R free of charge.
Another benefit, albeit slightly more indirect, is that anybody can access the source code, modify it, and improve it. As a result, many excellent programmers have contributed improvements and fixes to the R code. For this reason, R is very stable and reliable.
It’s important to stress that the GPL does not pertain to your usage of R. There are no obligations for using the software — the obligations just apply to redistribution. In short, if you change or redistribute the R source code, you have to make those changes available for anybody else to use.
R runs anywhere
The R Development Core Team has put a lot of effort into making R available for different types of hardware and software. This means that R is available for Windows, Unix systems (such as Linux), and the Mac.
R supports extensions
R performs a wide variety of functions, such as data manipulation, statistical modeling, and graphics. One really big advantage of R, however, is its extensibility. Developers can easily write their own software and distribute it in the form of add-on packages.
Because of the relative ease of creating these packages, literally thousands of them exist. In fact, many new statistical methods are published with an R package attached.
R connects with other languages
As more and more people moved to R for their analyses, they started trying to combine R with their previous workflows, which led to a whole set of packages for linking R to file systems, databases, and other applications. Many of these packages have since been incorporated into the base installation of R.
Several add-on packages exist to connect R to database systems, such as the RODBC package, to read from databases using the Open Data Base Connectivity Protocol (ODBC) and the ROracle package, to read Oracle data bases.
R vs. Python: Which One to Go for?
When it comes to machine learning projects, both R and Python have their own advantages. Still, Python seems to perform better in data manipulation and repetitive tasks. Hence, it is the right choice if you plan to build a digital product based on machine learning. Moreover, if you need to develop a tool for ad-hoc analysis at an early stage of your project then go for R. The ultimate choice depends on which programming language you want to go. Till then — keep learning!
Why Choose Python For Machine Learning As Topic?
According to a recent report by Analytics India Magizine, the most preferred Data Science programming language used across organisations is Python, with 53.3% of the respondents utilising the language. Other languages that follow are R, Matlab, SAS, Scala, Java and more.
1) Python offers concise and readable code. While complex algorithms and versatile workflows stand behind machine learning and AI
2) Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing.
A) Back-endWeb Development:
Python is a perfect match for database interactions and writing APIs, so it’s no wonder the language became the key element in the development of many globally popular websites and web services. Python frameworks Django and Flask are the most popular web development frameworks nowadays.
B) DataAnalytics And Machine Learning:
Python is often the language of choice for the development of data analytics solutions and data science- and machine learning-powered solutions. ScienceSoft’s data science team relies on such Python libraries as OpenCv and Scikit-Learnfor building machine-learning algorithms, and Keras for creating and training neural networks.
C) It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a Command Line- Interpreter and it responds immediately. Java lacks this feature.
3) Many programmer’s and developers have also used python to build productivity tools, games e.t.c…
A) Is python fast enough for Machine learning?
1) Python’s simple syntax means that it is also faster in development than many programming languages, and allows the developer to quickly test algorithms without having to implement them.
2) Python has a huge set of libraries which can be easily used for Machine learning.
4) If computing speed is not the issue, code readability can be the thing that clinches the case in favor of going with Python for Machine Learning.
5) There are plenty of resources that make it easy to develop in ML using python , probably more than for any other language_ Tensor flow being just one example.Tensor flow chart
In terms of AI Frameworks, Scikit Learn is most preferred and is adopted by 19.9% of the respondents, followed by TensorFlow, Keras, PyTorch and Google ML.
Conclusion:
Well, here we are! The end of the book. While you probably don’t have the same depth of understanding as a PhD in machine learning, I hope you have learned something. Specifically, I hope you’ve developed a thought process for approaching problems that machine learning works so well at solving. I firmly believe that using tests is the only way that we can effectively use the scientific method. It is the reason the modern world exists, and it helps us become much better at writing code.
Of course, you can’t write a test for everything, but it’s the mindset that matters. And hopefully you have learned a bit about how you can apply that mindset to machine learning. we will discuss what we covered at a high level, and I’ll list some suggested reading so you can dive further into machine learning research.
Note:
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