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Tag: machine learning course in India

How Should You Learn Python For Machine Learning And Artificial Intelligence?

Posted on by Imarticus

In an era where Machine Learning/ML and Artificial intelligence/AI rule the roost of technology and analytics one can understand why Python experts are most sought after. With the advent and use of AI and ML in everything you do, there is an urgent need for collaborators who can tweak software, create new applications, use the predictive and forecasting alerts and insights gainfully to improve profits, efficiency and save time, effort and costs. It is still early days and the right time to upgrade and re-skill with machine-learning courses that will enable smart and creative use of Machine learning benefits. Big-data Hadoop training courses are also required to help ML understand and use the mind-boggling quantities of data that is now usable. Without the will to effectively use data and the training required to adapt you will be left far behind. The situation today is adapting, or die!
Python’s library versatility:
Learn-by-doing for tasks involving data analytics in Python machine learning which will help in the following.
Web development is simplified with Bottle, Flask, Pyramid, Django, etc especially to cover REST APIs at the backend.
Game development is not so difficult with Pygame where you can use the Python modules to build video and animated games.
Computer VisionTools like Face detection, Opency, Color detection and more are available for specific tasks in the Python suite.
Website Scraping that cannot expose data without an API is regularly undertaken using Python libraries like Requests, BeautifulSoup, Scrapy, Pydoop, and PyMongo by e-commerce sites for price-comparison, data and news aggregators and others.
ML algorithmic tasks like predicting stock prices, identification of fingerprints, spam detection and more using AI and ML is enhanced in Python’s modules and libraries like Scikit-learn, Theano, Tensorflow, etc. Even Deep Learning is possible with Tensorflow.
GUI desktop cross-platform applications can easily be developed with the Python modules of Tkinter, PyQt, etc.
Robotics uses Raspberry-Pi as its foundation for coding in Python.
Offline/online data-analytics needing data cleaning and being sourced from various databases can be achieved using Pandas. Find patterns and data visualization with Matplotlib which is an essential step before executing the ML algorithm.
Automation of browser tasks like FB posts, browser opening, and checking of status are rapid in Python’s library Selenium.
Tasks in Content- Management including advanced functions are quicker executed in Django, Plone, CMS, etc.
Big-Data handling libraries in Python are more flexible and can be used as effective learning tools.

Why Python?

Data Science and its analytics require good knowledge and the flexibility to work with statistical data including various graphics. Python is tomorrow’s language and has a vast array of tools and libraries. Its installation program Anaconda works with many operating systems and protocols like XML, HTML, JSON, etc. It scores because it is an OO language well-suited for web development, gaming, ML and its algorithms, Big Data operations, and so much more.
Its Scipy module is excellent for computing, engineering and mathematical tasks allowing analysis, modeling, and even recording/ editing sessions in IPython which has an interactive shell supporting visualization and parallel computing of data. The decorators of functionality are a good feature in Python. Its latest V3.6 features the a-sync-io module, API stability, JIT compiler, Pyjion, and CPython aids.

Learning Python Step-by-Step

Become a Kaggler on Python from an absolute newbie using the step-by-step approach to emerge complete with skills in Python tools and ready to kick-start your career in data-sciences.

  • Step 1: Read, learn and understand why you are using Python

Zero in on your reasons for learning to use Python, its features, functions and why it scores in the various verticals of data sciences like ML, AI, financial applications, Fintech applications and more.

  • Step 2: Machine set-up procedures

Firstly use Continuum.io to download Anaconda. Just in case you need help, refer to complete instructions for the OS by just clicking on the link.

  • Step 3: Python language fundamentals learning:

It is always better to gain experience from a reputed institute like Imarticus Learning for doing a Machine learning course on data analytics and data sciences. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical Python machine learning skills. The topics covered include linear and logistical regression, decision trees, K-clustering, dimensionality reduction, Vector Machines, ML algorithms and much more.

  • Step 4: Use Python in interactive coding and ‘Regular Expressions’:

When using data from various sources the data will need cleaning before the analytics stage. Try assignments like choosing baby-names and data wrangling steps to become adept at this task.

  • Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas, and SciPy.

Practice in these frequently used libraries is very important. Try out these following tasks and resources like NumPy tutorial and NumPy arrays, SciPy tutorials, Matplotlib tutorial, the ipython notebook, Pandas, Data munging and exploratory analysis of data.

  • Step 6: Use Python for Visualization

A good resource is linked in the CS 109 lecture series.

  • Step 7: Learn Scikit-learn and ML

These are very important data analysis steps.

  • Step 8: Practice using Python and then practice more

Try webinars, hackathons like DataHack, Kaggle, and such fun Python machine learning resources.

  • Step 9: Neural networks and Deep Learning

Do short courses on the above topics to enhance your skills.
Concluding note:
Machine learning and AI in data processing have changed drastically the way things work in enterprises and even our daily lives. Digital technology has been able to enable machines with ML software and algorithms to process intelligently and unsupervised the large volumes of data generated. The advent of the internet and such limitless uninterrupted data processing has generated many an error-free gainful insight. Businesses can use the Python programming language and shift gears to the high-efficiency mode where profits increase and employee-time is well-used in creatively use of forecasts and insight provided by data analytics, ML, big-data processing, and concise clear predictive analysis.
The Python machine learning course at Imarticus offers certification and other advantages such as global updated industry-relevant curriculum, learning through convenient modes and timings, extensive hands-on practice, mentoring, etc that ensure you use the mentorship to be career and job-ready from the very first day.

How Does a Beginner Start To Learn Machine Learning, Having Some Knowledge of Programming Languages?

Posted on by Imarticus

So you have learnt to programme and were having a happy time at work, by putting into practice what you learnt. Suddenly, out of nowhere, this huge wave of Machine Learning comes up, and you are all at sea! You know programming, but now you need to learn machine learning to stay relevant. And you are staring at a Herculean task, sifting through all the available machine learning courses online – unable to decide, whether any of them can actually help you out!
The first question you should ask yourself is what the kind of role that you see yourself playing in the future is? Yes, before actually diving into the time and effort consuming task of attending machine learning courses, you should make an effort to read as much as possible. Read articles, participate in forums, talk to people and find out, what are the kind of roles on offer in this field. See which of these matches your aspirations and career goals. Before you start to learn machine learning, you should know the field, however abstract it may seem.
Once you know, what the hullabaloo is all about, it is extremely crucial to assess your current skills. Yes, you know programming, but which languages are you familiar with? Can they help you in getting deeper in the domain of machine learning and artificial intelligence? If your answer is no, the first set of machine learning courses you should look into are the ones that familiarize you with such languages like R or Python.
As you become familiar with the programming languages that will help you learn machine learning, do an honest assessment, whether you really like programming in them and have you decided to pursue the career as a programmer. In that scenario, you should look at more and more advanced machine learning courses that teach you the intricate details of programming in R or Python. Also, you should undertake some of the basic machine learning courses that help you to familiarize yourself with algorithms and statistics.
In case programming is not what you want to pursue, but analytics is where your heart and ambition lies; you should look at machine learning courses or even generic artificial intelligence courses dealing with statistics, algorithms, when and where to use them. This will give you a much better grip on which algorithm to apply in which situation and the logic behind it.
As you become more and more proficient in the logic, you should also be able to develop quick prototypes of your proposed solutions with your understanding of the programming languages. This is the benefit of your efforts to learn machine learning in a streamlined manner. You can easily relate to the problem, identify solutions, build prototypes, evaluate and improvise. No one can actually stop you.
And lo! You have crossed the hurdle with the help of machine learning courses online. All you needed was a firm decision to learn machine learning and determination to achieve your goals with dedicated efforts.

7 Horrible Mistakes you’re making with Artificial Intelligence

Posted on by Imarticus

We could notice that, numerous marketers commit mistakes with regards to AI. That is a common thing. We’ve done it, as well. It requires a lot of investment to get settled with AI.
In any case, a few mistakes are more inflated than others. Furthermore, these mistakes will bring your association down the wrong track with regards to AI.
No one wants to get in that bad situation. So to prevent this issue and to get benefit from AI later, marketers should think to avoid below 7 horrible mistakes that they are making while implementing a machine learning or an artificial intelligence course.
1. Thinking AI usage is simple.
Several marketers think if they have the accurate information, implementation is easy. a few of the AI tools are very simple to utilize and you can begin quickly. But transforming your association into an AI-driven organization is another responsibility completely. Receiving AI association wide requires some serious energy. It needs cash. What’s more, it takes experimentation.
You need to commit for the long period. In reality, the correct data and methodology are fundamental. Implementation is secondarily come!
2. Marking down artificial intelligence altogether.
The opposite side of the coin is advertisers who trust AI are all publicity. We get it. There is a huge amount of promotion out there and a ton of extremely strong claims. Normally, you may trust AI is simply one more popular buzzword.
Nothing could be further from reality. Over the most recent couple of years, critical advances in AI and machine learning have happened. This is an undeniable, exceptionally impact arrangement of technologies that will influence your profession.
3. Focusing on complete automation
Businesses aiming for entire automation process might merely save the salaries of the populace being supposedly substituted by AI. As per Jeremy, businesses that target to make a return on the employees by enhancing and rising workforce competence using AI would attain noteworthy ROI.
4. Fixating on where AI is going.
We get it. We cherish guessing about where AI is going. We even have a deadline for when our machine overlords will make their play for global control. But an excess of hypothesis on the most distant eventual fate of AI is diverting.
There are numerous miracles ahead as we enter the period of AI. Give yourself a little AI wandering off in fantasy land time, beyond any doubt. But, at that point discover a couple genuine implementation cases you can begin applying AI to begin at this point.
5. Thinking beginning with AI is too hard or excessively specialized.
It certainly requires some investment to get settled with ideas in Artificial Intelligence. What’s more, profoundly understanding the tech probably won’t be simple for the non-engineers among us.
This is not a regulation only for the technicians. As an advertiser, you have a gigantic chance to attach the specialized to the commonsense and discover genuine implementation cases for AI.
6. An inadequate foundation for machine learning
For most associations, dealing with the different parts of the foundation encompassing machine learning exercises can turn into a test all by itself. Trusted and dependable social database service frameworks can bomb totally under the load an assortment of data that organizations seek out to collect and investigate today.
7. Assuming AI can’t execute whatever marketers perform.
Indeed, even with a sound thankfulness for AI’s potential, it’s anything but difficult to laugh at it. How might it displace you or your partners? We can’t wait how ground-breaking AI will be, so we’re not saying it’ll replace anybody. Yet, it will change the idea of your work.
AI can do the plethora of things that marketers do today, quicker, less expensive and at scale. Inside this reality lies either guarantee or risk, reliant upon how you see it.
Marketers need to turn an attentive eye to how they create importance for associations and highlight the high-esteem innovative work

How is Machine Learning Helping Businesses Grow?

Posted on by Imarticus

Machine learning (ML) technologies represent one of the exciting new aspects of the digital age. These technologies are premised on sophisticated algorithms that empower modern enterprises to tackle a variety of business problems. Computers and digital systems that use Machine learning are designed to gain experience from various processes and apply certain rules and data sets to perform complex calculations.

Modern machine learning systems also leverage the use of cloud technologies in a bid to maximize speed and cost-effectiveness.

  • Recent advances in the commercialization of cloud computing services allow business organizations to utilize huge offerings in compute and storage services. Large cloud players are offering modern enterprises an opportunity to use cloud computing solutions powered by machine learning technologies. These systems are also “creating new opportunities for innovators to offload labour-intensive research and analysis to the cloud.”
  • Machine learning systems are enabling business decision makers to visualize data more efficiently. The use of these technologies enables business analysts and business managers to access and utilize data analysis paradigms. This means that machine learning systems are essentially crunching huge volumes of data and electronic information and presenting patterns, analysis, and insights to modern businesses. This personnel can analyze these patterns to quickly initiate business decisions in response to evolving market conditions.
    • Digital technologies have emerged as a major enabler in modern societies. Machine learning and artificial intelligence technologies are no exceptions. Cloud-based machine learning algorithms can process current data from business environments to predict future consumer requirements, market trends, etc. This enables business organizations to process unprecedented amounts of data in the ultimate pursuit of growing and expanding their commercial footprints. Companies and brands that can effectively anticipate future requirements are better positioned for future market performance.
    • Certain industries such as logistics and transportation can gain clear benefits by implementing machine learning technologies. Vehicles can be fitted with digital devices and transmission systems that generate data regarding the performance of vehicle systems and sub-systems. Analysis of such data can help vehicle designers and engineers to refine and improve the performance of each vehicle over time. Higher mileage from each vehicle and fewer maintenance hours can help these businesses to earn larger profits.

 

  • Machine learning algorithms can help the banking and insurance industry to spot and prevent instances of fraud. Certain insurance service providers are using the technology to scan the faces of loan applicants and insurance policy applicants. These algorithms have access to huge databases that enable them to detect any scope or intent of fraud ahead of time.
    Thus, machine learning systems help these service providers to expand the scope of their business while cutting scope for malfeasance and thereby reducing losses. Such use of machine learning technologies is expected to gain momentum in time.
  • Artificial intelligence technologies and machine learning algorithms are helping businesses to make decisions that are more efficient. Retailers can use these technologies to analyze sales data from the past and other points of market information to control their inventories and supplies. This approach removes the scope of guesswork in certain aspects of business operations while creating scope for efficient operations and greater profits.

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