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What Are The Prerequisites For Artificial Intelligence?

Posted on by Imarticus Learning

Artificial intelligence keeps changing in its definition as does its scope and capabilities. A few decades ago, simple calculators were considered artificial intelligence since math problems were previously only solved by the human brain. Today, artificial intelligence powers home automation systems and gadgets like Google Home, Siri, and Alexa. We see new AI being released almost every week with juggernauts like Google and Facebook it improve the user experience. The auto-reply feature with suggested replies on Gmail is an example of artificial intelligence where the responses are ‘taught’ to the machine.
Having a good foundation is imperative if you want to foray into artificial intelligence. It isn’t as simple as attending a machine learning course to be a valuable employee in the field of AI. People who are interested in artificial intelligence can take several paths to learn the various AI skills necessary for the subject. Based on your previous knowledge and skill level, you should chart your own course.

The prerequisites of artificial intelligence will give you a good foundation to stand upon when you are learning the key concepts. You will have to have a good foundation in calculus, linear algebra, and statistics in order to help you to develop algorithms. You will also need a good knowledge of Python and Python for data science track as it is the predominant language used in machine learning.
Whatever math skills you might have already, you might want to brush up on them before foraying into Artificial Intelligence. There are many courses available online that will go into depth about the various concepts used in AI. If you are getting into AI to solve a problem, then you can rely on existing libraries to help you with the math required. However, if you are looking to get into research or deep into machine learning, you will have to get an in-depth knowledge of math.
The next steps involve learning and soaking up as much machine learning concepts and theory as you can. It will help you on many fronts including planning and collecting data, interpretation of model results, and creating better models.
The next step should focus on data cleaning, exploration, and preparation. As someone who will be working with machine learning, you will have to have a good quality of feature engineering and data cleaning on the original data you have. This is a very important step and will regularly feature in your work in the future. You should spend as much time as you can here, doing practice tests and runs.
For practice, you should participate in as many Kaggle competitions as you can. These are generally easy and will help you work with multiple scenarios and typologies. With machine learning, the more practice you have, the better you are.
As a beginner, these are the steps you will have to take in order to understand the basics of artificial intelligence. If you are interested in a deeper understanding of the subject, then you can opt of Deep Learning and Machine Learning with Big Data.

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How do you build a career in Machine Learning after completing the ML Foundation Course?

Posted on by Imarticus Learning

 

ML/Machine Learning has a promising future. Chatbots, smartphones and most AI platforms essentially use ML. For example, Alexa from Amazon, Google, Facebook, and almost all large platforms point to a growing industry and an all-time high ML jobs demand. Very obviously the need for professionals in ML, AI, and Deep Learning outstrips the demand.

Programmers, graduates in Computer Applications, and even graduates in mathematics, Social Science or Economics can learn and become ML professionals by doing a certified foundation course in Data Analytics/ Data Science course.

The ML professionals essential skill set include

·         Computer programming and CS Fundamentals.

·         Programming languages like R, Python and some more.

·         ML libraries and algorithms.

·         Statistics and Probability.

·         Software design and systems engineering.

Simple ways to get started with Machine Learning:

A. Read ML books and do a machine learning course with a reputed company like Imarticus which can provide you with reinforcement and certification of your practical skills. Data is the beginning and all about applying your machine learning training, programming knowledge, computer science techniques and statistics to data. R and Python are the most commonly preferred languages. While Python scores in leveraging libraries that are analytics-friendly, practical algorithms, the application development and end-to-end integration using sci-learn and Tensorflow APIs, R is preferred for advanced capabilities in data and statistical inferences analysis.

B. Hone your ML skills with ML Courses which provide ML fundamentals and basic algorithms, statistical pattern recognition and data mining. Your knowledge of statistics should include Bayesian probability, inferential and descriptive statistics for which you will find free courses by Udacity.

C. Applying your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.

D. Attend hackathons (Kaggle, TechGig, Hackerearth, etc) which give you support, exposure and mentorship in  ML practical ideas.

E. Build your portfolio with 

  1. A project where you collect the data yourself 
  2. A project where you deal with data cleaning, missing data, etc

F. Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

The Job Scope:

ML can be the most satisfying choice of careers today which include algorithm development and research used for adaptive systems, building predictive methods for product demand and suggestions, and exploring extractable patterns in Big Data.  Companies recruit for positions like 

  • ML Analyst 
  • ML Engineer 
  • Data Scientist NLP 
  • Lead- Data Sciences 
  • ML Scientist.

Expected payouts:

According to a Gartner report, 2.3 million ML jobs in AI are expected by 2020. Entering the ML field now, according to Digital Vidya, is a great option because the ML payouts for the new entrants vary from Rs 699,807- 891,326. With good expertise in algorithms and data analysis the range of reported salaries could be from Rs 9 lakh to Rs 1.8 crore pa.

What is the practical advice for Machine Learning?

Posted on by Imarticus Learning

Read, and re-read resources on introductions to Calculus, Mathematical statistics, both differential and inference, algorithm analysis, optimization, differential equations, linear algebra, Python, R and more. Does that sound difficult?

You don’t need advanced learning in them. You will however essentially need to understand how you can apply this learning to handling data analysis of the present and future of nearly every field under the ML, AI, Deep Learning and VR fields.

Here are some advantages of machine learning training in such courses.

  • You get to learn ML fundamentals and basic algorithms, statistical pattern recognition, data mining, statistics including Bayesian probability, working with Python, Pandas, and R, the Sci-learn and Tensorflow APIs, and more in a well-paced, learn-at-your-convenience online and classroom training mode.
  • The integrated curriculum helps you through practical industry-needed and relevant practical applications like

1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)

2. Supervised learning (neural networks, support vector machines, parametric/non-parametric algorithms, kernels)

3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).

  • Most learning is through applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.
  • Your certification carries weight as it declares you have applicative knowledge and our job and industry ready.

Data Science Course

That having been said, here are some practical tips for ML and discerning learners.

  • The first timers in ML rarely get things right. Don’t panic. ML skills are cultivated skills and are meant to be regularly practiced.
  • Implement your learning through a model. Compare your implementation skills with others while discovering the open-source libraries, mathematical or program techniques, and tricks, math-tools, etc. that can improve your efficiency.
  • Don’t get overwhelmed because leveraging your skills means research-work and doing small projects which help assimilate learning and applying the learning to practical situations whether they be smartphones, VR or chatbots. The tools in Python take care of the math while you get your hands deep into data analysis, data cleaning, and mining and data exploration and predictive analysis.
  • It isn’t just about math for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.
  • Apply your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
  •  Attend hackathons (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in  ML practical ideas.
  •  Build your portfolio with projects

a. Where you collect the data yourself

b. Where you get exposure to data cleaning, dealing with missing data, etc.

  • Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

As in all fields, it does get easier as you progress and get adept. So why wait? Partner with Imarticus courses and get a head-start in ML. Go ahead and do a machine learning course with a reputed training institute like Imarticus.

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!

What is the future of Artificial Intelligence?

Posted on by Imarticus Learning

One of the biggest developments in the world of computer science has undoubtedly been Artificial Intelligence. The ability of your machine to learn and understand all about certain processes and then implement methods to improve the same is one of the most in-demand jobs today.

It becomes necessary to evaluate a company’s software and see how they can implement artificial intelligence methods.

There is so much that is possible while applying artificial intelligence in marketing. By 2020, more than 30% of the companies worldwide will use AI to help streamline their sales. This will help them increase efficiency and focus more on converting sales and rates.

Here are a few other places where AI will play a prominent role:

  1. Driverless vehicles:

Automated vehicles aren’t a dream anymore. The likes of Tesla have already started implementing driverless cars on the road. The U.S. Department of Transportation has gone ahead and released certain definitions and rules pertaining to the various levels of automation which can be implemented.

Uber was also acquired by Google in order to help scale their properties and capture the driverless market in time. AI could help save lives lost in accidents and potentially save close to 30,000 people in the United States every decade.

As it is a disruptive technology, it is expected to create some big changes. It can also automate many jobs which affect people. In the near future, it is expected to be used for opportunity more than threats.

       2. Process automation:
Robotic process automation refers to the use of machine learning to automate tasks dependent on rules. It will help individuals focus on certain crucial aspects of their work and leave the routine work to machines.

Automated projects will take up a bulk of the automation work in the world of machine learning and artificial intelligence. Companies are always looking to be cost-effective and automated machinery will help them achieve that goal over the long term.

     3. Sales and marketing:
Artificial Intelligence is also being employed in so many sales and marketing sectors. AI can be used as a useful tool to make repetitive tasks much easier. This includes tasks such as scheduling, paperwork and even timesheets to make it easier.

Marketing teams will also be able to weed out fake leads from genuine ones to make it easier to choose the right people to market to. They will be able to make the process simpler and allow everyone to get better at their daily tasks.

Overall, artificial intelligence is on the route to make the world a better and easier place to live and work in. It is a disruptive technology which will create dramatic changes. It can also be used to automate a multitude of jobs, especially in the production sector and make it easier for companies to become cost-effective.

Over the long run, where this will head to cannot be predicted but by the looks of it, it seems like a good place to be in. With Imarticus, you will be able to take up an artificial intelligence course that makes it simpler for you to succeed. In the battle between machine learning vs artificial intelligence, you are the real winner!

3 Ways in Which AI is Transforming Business Operations

Posted on by Imarticus Learning

The business scenario today has evolved and kept pace with technological developments. And AI has been at the helm of the change experience impacting literally every area that affects growth and development. The changing economic, geopolitical and social environments are in a state of constant flux and need businesses to adapt very quickly to tide over the changes in organizational dynamics, critical business glitches like employee retention and hiring or landscape requirements like being scalable and Agile.
Artificial intelligence can help bridge over troubled waters in many areas where human intelligence and limitations fail. Let us explore some of these critical areas where AI has and still has the potential to improve the business scenario.
The successful customer and user experience:
The experience of the customer is what tells brands apart and this differentiator is best exploited through successfully harvesting of the data and changes brought about by AI. Research and use of Walker data suggest that large multinationals like Adobe, Intuit, and EMC have benefitted greatly by entwining the customer experience into their operational daily routines of marketing, sales, and operational routines. And AI makes it possible to offer those great user-experiences crafted from forecasts and gleanings of data on why the customer buys, when and for how much, how the competition fares and their latest parleys, or what the customer wants from you.
The arsenal of data forecasts and insights can personalize an individual’s experience to match his needs, budget, etc, through a more seamless integrated process that offers high satisfaction and customer loyalty. The results are most helpful in rapidly predicting markets, changing products, forecasting customer- behavior, and staying up to date with the latest offers of technology. Thus AI is the one tool that has immense potential in accumulating, understanding and changing the fortunes of business enterprises by forecasting touch-points, trends, brand preferences, pricing strategies and more.
Bettering the hiring process:
The acquisition of skilled talent is critical to all businesses. However, most processes like recruitments, interviews, talent hunting, employee-referrals, and assessments are subject to very many biases, nepotism, controls, and flaws.
For bettering the hiring process certain tasks are all important. Firstly, one has to cast the net wide. Secondly, the talents need to be matched to the job requirements and the process of pivoting in on the right candidate needs to be free of human errors and bias. Lastly, the holistic use of data using the latest developments needs to be deployed. Not surprisingly, AI aided assistants today can make short work of the recruitment process while ensuring a great supply database for recruitments and keeping in mind the specifics of talent growing into higher roles and reducing the pitfalls of employee migration and retention issues.
Retaining and engaging the employees:
Skill and talent lie at the core of the hiring process. With increased demand comes the problem of retention and employee engagement turning into a competitive minefield. Poor management practices, lack of growth on the job and employee engagement have turned into major contributors for lack of retention of employees as is evident from surveys conducted by SalesForce and Gallup.
AI has enabled cutting-edge technologies like analysis of employee sentiments, biometric trackers, and such AI-empowered techniques can aid in effective retention through timely motivation, employee empowerment, continued learning opportunities and ensuring deserving rewards, career growth, skill up-gradation and more. More engaged employees mean better retention, employee loyalty, and engagement.
Conclusion:
In parting, it is valid to note that AI helps the new operations in business which in turn can change the dynamics of a beyond satisfying customer-experience, growing engagement with employees, hiring and retention. People are assets to the company and the twist that AI and technology have brought in can easily transform companies through efficient dynamics, change and people management.
To learn all about futuristic technologies like adaptations of artificial intelligence, powering AI through effective Machine Learning, scouring the growing volumes of data through Deep Learning and beyond to futuristic technology like blockchains for fintech industries try the Imarticus Learning experience.
The Agile Scrum Tutorial are succinct with due emphasis on the practical applications of knowledge and concepts coupled with invaluable modules of self-development and soft-skill training. Besides, one gets the mentorship of certified and industry-drawn mentors and instructors. Go ahead and make the most of opportunities and jobs on offer in their placement program too. Why wait then?

How is Machine Learning Impacting The Education Industry?

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Machines today are being used more than ever due to the simplicity of their making and their ability to learn and create value to organizations. The story is not different in the education space either. Artificial intelligence and machine learning are being used to create modules for students, which are highly personalized and intuitive. One of the biggest benefits of using machine learning is because of the computers ability to process large volumes of data both historic and real-time and analyze it for predictive outcomes. Artificial Intelligence is already being used to grade papers (multiple choice questions) fairly and effectively in many schools across the world. It is also impacting the lives of specially-abled students by providing tools and equipment to study and succeed.
The education industry is moving beyond classroom and textbook learning to create more immersive programs for their students. Digital libraries are growing at a rapid pace due to emerging technologies such as big data, cloud computing, and AI.  Another great example of machine learning usage is to categorize content in a manner where the student finds it easy to build on existing knowledge. This means that it gives a student the opportunity to learn at his/her own pace and succeed while doing so, thereby greatly boosting the morale of the child.
Here are a few ways machine learning has become a game changer in the education space. 
Supporting Teachers
Machine learning helps teachers program a curriculum which is highly individualized for their students. Kids today are smart and fast learners due to their increased exposure to technology. Hence the subjects also need to be contemporary and relevant. All their students’ data in terms of marks, activities can be historically analyzed to create personalized lessons, thereby matching the child’s ability to learn and succeed.
Custom-Made Subjects
One of the biggest advantages of machine learning training is a personalized learning experience for individuals. Every child learns at a different rate and is proficient in different subjects. Once data is gathered on the child’s different abilities, a machine can analyze and build a program specific to the child’s needs thereby controlling the outcome as well as the rate of learning. Another aspect of this is it will help grade students fairly and as per their ability without any human bias.
Data Science Course
Increase Retention 
Through Machine Learning teachers can identify students who are likely to forget and help them with specialized chapters and techniques to retain the subject. Learning analytics tools such as Wooclap, Yet Analytics, BrightBytes provides precise predictive solutions through different learning ecosystems. This helps educators adapt and improve their content significantly mapping it according to the students’ needs.
Conclusion
Since we are discovering more ways machines can be used effectively in classrooms, one can predict that the growth trajectory and successful integration of machines are highly possible soon.

The Ultimate Glossary of Terms About Machine Learning

Posted on by Imarticus Learning

Machine learning is an artificial intelligence application which provides computer systems with the ability to learn on its own and improve with experience without any explicit requirement of additional programming. Machine learning has its focus on developing computer programs whole can access data and utilize the data to learn on its own.
Some of the commonly used terminology used in Machine Learning are as follows:

  • Adam Optimisation

It is an algorithm utilized to train models of deep learning and is an extension of the Stochastic Gradient Descent. In this algorithm, the average is run employing both gradients and using the gradient’s second moments. It is useful for computing the rate of adaptive learning for every parameter.

  • Bootstrapping

It is a form of the sequential process wherein each subsequent model tries to correct the errors in the earlier models. Each model is dependent on its previous model.

  • Clustering

It is a form of unsupervised learning utilized for discovering inherent groupings within a set of data. For instance, a grouping of consumers on the basis of their buying behavior which can be further used to segment the customers. It provides useful data which the companies can exploit to generate more revenues and profits.

  • Dashboard

It is an informative tool which aids in the visual tracking, analysis of data by displaying key indicators, metrics and data points on a single screen in an organized manner. Dashboards are often customizable and can be altered based upon the preference of the user or according to the requirement so of a project.

  • Deep Learning

It is a form of a Machine Learning algorithm which utilized the concepts of the human brain towards facilitation of the modeling of arbitrary functions. It requires a large volume of data, and the flexibility of this algorithm enables multiple outputs of different models at the same time.

  • Early Stopping

It is a technique of avoiding overfitting while training an ML model using iterative methods. Early stoppings are set in such a manner that it halts the performance of improvement on validation sets.

  • Goodness of Fit

It is a model which explains a proper fitment with a set of observations. Its measurements can be summarised into the discrepancies between its observed values with that of the expected values using a certain model.
This Machine Learning Course is a good fir when the errors on the models which are on training data along with the minimum test data. With time, this algorithm learns the errors in a model and corrects the same.

  • Iteration

It is the number of times the parameters of an algorithm is updated during training of a dataset on a model.

  • Market Basket Analysis

It is a popular technique utilized by marketers for identification of the best combination of services and products which are frequently purchased by consumers. It is also known as product association analysis.

  • MIS

Also known as Management Information System, it is a computerized system comprising of software and hardware which serve as the heart of a corporation’s operations. It compiles data from various online and integrated systems, conducts an analysis on the gathered data, and generates reports which enable the management to make informed and educated business decisions.

  • One Shot Learning

This form of machine learning trains the model which a single example. These are generally utilized for product classification.

  • Pattern Recognition

It is a form of machine learning which focuses on recognizing regularities and patterns in data. Some examples of pattern recognition used in many daily applications include face detection, optical character recognition, object detection, facial recognition, classification of objects etc.

  • Range

It is the difference between the lowest and the highest value in a data set.

How Do You Start Applying Deep Learning For My Problems?

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Deep Learning helps machine learn by example via modern architectures like Neural Networks. A deep algorithm processes the input data using multiple linear or non-linear transformations before generating the output.
As the concept and applications of Deep Learning are becoming popular, many frameworks have been designed to facilitate the modeling process. Students going for Deep Learning, Machine Learning course in India often face the challenge of choosing a suitable framework.
Machine Learning Course
Following list aims to help students understand the available frameworks in-order to make an informed choice about, which Deep Learning course they want to take.
1.    TensorFlow 
TensorFlow by Google is considered to be the best Deep Learning framework, especially for beginners. TensorFlow offers a flexible architecture that enabled many tech giants to embrace it on a scale; for example Airbus, Twitter, IBM, etc. It supports Python, C++, and R to create models and libraries. A Tensor Board is used for visualization of network modeling and performance. While for rapid development and deployment of new algorithms, Google offers TensorFlow which retains the same server architecture and APIs.
2.    Caffe 
Supported with interfaces like C, C++, Python, MATLAB, in addition to the Command Line Interface, Caffe is famous for its speed. The biggest perk of Caffe comes with its C++ libraries that allow access to the ‘Caffe Model Zoo’, a repository containing pre-trained and ready to use networks of almost every kind. Companies like Facebook and Pinterest use Caffe for maximum performance. Caffe is very efficient when it comes to computer vision and image processing, but it is not an attractive choice for sequence modeling and Recurrent Neural Networks (RNN).
3.    The Microsoft Cognitive Toolkit/CNTK
Microsoft offers Cognitive Toolkit (CNTK) an open source Deep Learning framework for creating and training Deep Learning models. CNTK specializes in creating efficient RNN and Convoluted Neural Networks (CNN) alongside image, speech, and text-based data training. It is also supported by interfaces like Python, C++ and the Command Line Interface just like Caffe. However, CNTK’s capability on mobile is limited due to lack of support on ARM architecture.
4.    Torch/PyTorch
Facebook, Twitter and Google etc have actively adopted a Lua based Deep Learning framework PyTorch. PyTorch employs CUDA along with C/C++ libraries for processing. The entire deep modeling process is simpler and transparent given PyTorch framework’s architectural style and its support for Python.
5.    MXNet
MXNet is a Deep Learning framework supported by Python, R, C++, Julia, and Scala. This allows users to train their Deep Learning models with a variety of common Machine Learning languages. Along with RNN and CNN, it also supports Long Short-Term Memory (LTSM) networks. MXNet is a scalable framework making it valuable to enterprises like Amazon, which uses MXNet as its reference library for Deep Learning.
6.    Chainer
Designed on “The define by run” strategy Chainer is a very powerful and dynamic Python based Deep Learning framework in use today. Supporting both CUDA and multi GPU computation, Chainer is used primarily for sentiment analysis speech recognition etc. using RNN and CNN.
7.    Keras
Keras is a minimalist neural network library, which is lightweight and very easy to use while stocking multiple layers to build Deep Learning models. Keras was designed for quick experimentation of models to be run on TensorFlow or Theano. It is primarily used for classification, tagging, text generation and summarization, speech recognition, etc.
8.    Deeplearning4j
Developed in Java and Scala, Deeplearning4j provides parallel training, micro-service architecture adaption, along with distributed CPUs and GPUs. It uses map reduce to train the network like CNN, RNN, Recursive Neural Tensor Network (RNTN) and LTSM.
There are many Deep Learning, Machine Learning courses in India offering training on a variety of frameworks. For beginners, a Python-based framework like TensorFlow or Chainer would be more appropriate. For seasoned programmers, Java and C++ based frameworks would provide better choices for micro-management.

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

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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.