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Tag: courses on machine learning

How can you start programming machine learning and artificial intelligence?

Posted on by Imarticus Learning

One of the biggest developments in the world of computing over the last few years has undoubtedly been artificial intelligence. The ability for a machine to automatically learn and apply methods to improve the quality of output is one of the most in-demand jobs in the world today. Companies are willing to pay big bucks to create systems that can understand their user and make predictive techniques based on their behavior.
These techniques and systems are already being employed by some of the biggest companies in the world. If you’re looking to start your machine learning course, here are a few basics you need to know:
R or Python:
Python and R are two of the most commonly used programming languages with algorithms in all fields dependent on them. While Python is used more in the field of machine learning, it is also easy to understand and learn. Organizations have already implemented it in places to develop applications on analytics. It makes it easier for users to implement any type of algorithms as well.
R is used to create better and more statistical processes. R is generally used to create and formulate statistical processes and companies dependent on data analytics tend to use R.
Statistics:
A basic understanding of Statistics is necessary to comprehend machine learning. While you might need to know what the algorithm does, knowing how the tools can be used for the end result is also necessary. With time, you’ll be able to implement your own algorithms as well and create inferential and descriptive statistical methods at some point.

Artificial Intelligence Course
What skills would you require?
If you’re looking to get a job in the field of artificial intelligence or machine learning, there are a few essential skills, including:

  1. Communication skills – An ability to communicate is crucial in addition to professionally having a good knowledge of spoken English
  2. Education – A good graduation degree or A.I. certificate is also required to begin a career in the field of education. This is needed to create your base in this particular field.
  3. Programming languages – A knowledge of programming languages – understanding of python string, variables, statement, operators, conditions, modules, and sense are super necessary.
  4. Machine learning techniques – Knowing all about Artificial Intelligence, especially Machine Learning as it is the most lucrative field. It has helped to create powerful websites, given realistic speech popularity and more
  5. TensorFlow – This is a software program that is periodical for the dataflow to be streamlined to execute different duties. It is generally used for gaining practice in the systems, including the relation to nerve networks.
  6. Deep Learning – Knowing about deep learning is necessary to use strategies that are rooted in the evaluation of learning statistics, in order to create a unique set of rules and processes.

Hence, with the time you will be able to understand everything about the field of machine learning and artificial intelligence. With Imarticus, you can get the machine learning certification and begin your journey right away!

11 Ways Investing in Machine Learning Can Make You Successful

Posted on by Imarticus Learning

Machine learning is certainly a buzzword, most of us have been hearing about lately. In this digital age, most of us are exposed to technology on a daily basis, especially when it comes to our field of work. When applied properly, technology can yield mammoth results which can benefit the majority of us. Machine learning is a tool which is being used by various businesses, to predict market behavior, so be it marketing or finance, everyone is making the most of this particular technology. So let us find out how you can make the most of machine learning to carve your pathway to success.

  • Develop your business

If you are an entrepreneur, big or small, chances are, you will need machine learning for analyzing the market. Businesses these days make the most of the algorithms to boost their sales and profit. Machine learning allows business owners to know their buyers better, apart from that, it helps in analyzing trends and allows the business owners to curate plans, on the framework provided by the trends. If you know machine learning, you can successfully run a good online business campaign.

  • Accuracy in marketing

If you happen to be in the marketing team, and you have a good grip on the subject of machine learning, then your bosses will look at you as a huge asset to their company. Marketers are using machine learning to device new and improved campaigns, which can bring profit to your company, and therefore, get you a promotion.

  • Machine learning for hospital staff

If you happen to work at a hospital, then machine learning can help you out with data entry and making medical predictions as well as diagnosis.  ML helps in making identification of diseases nearly perfect and helps in building an accurate diagnosis and cure which facilitates faster recovery in patients.

  • Efficient utilization of resources

Time cycle reduction, helps in the proper utilization of resources. So matter which field you belong to, the machine learning course will help you to derive the optimum use of your resources.

  • Helps you act fast in practical situations

Machine learning can bring forth all the data predictions, but acting on it is in the hands of the entrepreneurs as well as the employees. If you know all about machine learning and can derive all the necessary data from it, then you can very easily help in making future plans pertaining to the business and please your bosses to climb the stairway to success

  • Spam detection

Machine learning is one of the best tools to solve the problems related to spam, by filtering them. So if you work in the tech sector, then machine learning is an absolutely imperative tool to become successful.

  • Better segmentation

Machine learning helps in achieving accurate predictions for individual marketing offers, which is a more customized approach to a data-driven market. Those who know about this subject, can easily segment their customers and trigger new marketing campaigns which are more persuasive than others, to woo the customer.

  • Forecast business maintenance

Those employees, who can make big contributions to the company by making a clear-cut analysis of the trends, go higher up in the company. Huge companies are making the most of machine learning, which is why a degree or diploma in the subject will help in landing up with a good job in a reputed company.

  • Do well in finance

Machine learning is known to work magic when it comes to analyzing financial data. Dealing with customer data, bills, money transfers etc. will become really easy with the use of ML. So, if you wish to do well in the finance sector, a course in machine learning can take you a long way.

  •     Network security

If your brand happens to be an online startup, machine learning algorithms can help with network security and financial monitoring. This way you can retain the trust and confidentiality of your customer’s personal details and gain their trust.

  •       Precision

Accuracy and precision have a great role to play in every field. With Machine learning, the analyses you make are a lot more accurate, which can help in making future decisions, with utmost clarity, thus enhancing your reputation, as an employee or staff member.

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

Posted on by Imarticus Learning

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 Learning

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.

Why Do People Often Use R Language Programming for Artificial Intelligence?

Posted on by Imarticus Learning

Why Do People Often Use R Language Programming for Artificial Intelligence?

All over the world, machine learning is something which is catching on like wildfire. Most of the large organisations now use machine learning and by extension, AI for some reason or other – be it as a part of a product or to mine business insights, machine learning is used in a lot of avenues. Even the machine learning future in India seems all set to explode in the next couple of years.

All this has led companies to be on the lookout for proficient practitioners, and there are a lot of opportunities existing currently in this field. You might have started to wonder how you can make your mark in this science field – machine learning and AI are something which you can learn from your home, provided you have the right tools and the drive for it.

Many students have already started learning R, owing to the availability of R programming certification course on the internet. However, some are still not sure whether they want to learn R or go for Python like many of their peers are. Let us take a look at why R certification course is a great choice for machine learning and Artificial Intelligence programming and implementation. 

Features of R
R is a multi-paradigm language which can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualisation and statistics in general. However, it is less flexible compared to Python but is more specialised too. 

R is an open source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

One other thing that differentiates R and Python is the natural implementation and support of matrices, and other data structures like vectors. This makes it comparable to other stats and data-heavy languages like MATLAB and Octave, and the answer that Python has to this is the numpy package it has. However, numpy is significantly clumsier than the features that R has to offer.

Along with the availability of a lot of curated packages, R is definitely considered to be better for data analysis and visualisation by expert practitioners. If you think that you want to try your hand at machine learning and AI, you should check out the courses on machine learning offer at Imarticus Learning.

How is Machine Learning Helping Businesses Grow?

Posted on by Imarticus Learning

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.

Related Article : Skills Required to Learn Machine Learning