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Tag: machine learning courses

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

Posted on by Imarticus Learning
Reading Time: 4 minutes

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.

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

Posted on by Imarticus Learning
Reading Time: 2 minutes

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.

What are the best practices for training machine learning models?

Posted on by Imarticus Learning
Reading Time: 3 minutes

As we all know, Machine learning is a popular way of learning at your own pace. Machine learning also facilitates learning based on your likes and interests. For example, you are a person who is interested in space and astronomy, a machine learning driven course to learn mathematics for you, will first ask you few basic questions about your interest.

Once it establishes your interest, it will give examples of mathematical calculations using objects of space to keep you engaged. So, how are these machines able to establish your interest? What are the best practices for training machine learning models is something that we will see in this article.

Machine learning is based on three important basics.
• Model: A Model is responsible for identifying relationship between variables and to make logical conclusion.
• Parameters: Parameters are the input information that is given to Model to make logical decisions.
• Learner: Learner is responsible for comparing all the Parameters given and deriving the conclusion for a given scenario.

Using these three modules, machine is trained to handle and process different information. But it is not always easy to train the machine. We need to adopt best practices for training machines for accurate predictions.

• Right Metrics: Always start the machine learning training or practice with a problem. We need to establish success metrics and prepare a path to execute them. This is possible when we ensure that the success metrics that have been established are the right ones.

• Gathering Training Data: The quality and quantity of data used is of utmost importance. The training data should include all possible parameters to avoid misclassifications. Insufficient data might lead to miscalculated results. The quantity of data also matters. Exposing the algorithms to a small set of humongous data can make them responsive to a specific kind of information again leading to inaccurate results when exposed to something other than the test data.

• Negative sampling: It is very important to understand what is categorized as negative sampling. For example, if you are training your data for a Binary classification model, include data that requires other models like multi class classification model. By this, you can train the Machine to handle negative sampling too.

• Take the algorithm to the database: We usually take the data out from the database and run the algorithm. This takes lot of effort and time. A good practice would be to run the training algorithm on the database and train it for the desired output. When we run the equation through the kernel instead of exporting the data, we not only save hours of time but we also prevent duplication of data.

• Do not drop Data: We always create pipelines by copying an existing pipeline. But what happens in the background is, the old data gets dropped many a times to provide place for the fresh data. This can lead to incorrect sampling. Data dropping should be effectively handled.

• Repetition is the key: The Learner is capable of making very minute adjustments for refining the model to obtain the desired output. To achieve this, the training cycle must be repeated again and again until the desired Model is obtained.

• Test your data before actual launch: Once the Model is ready test the data in a separate test environment till you obtain the desired results. If your data sample is all the data up to a particular date for which you have all predictions, the test should be conducted on upcoming data to test the predictions.

Finally, it is also important to review the specifications of the Model from time to time to test the validity of the sample. You may have to upgrade it after a considerable amount of time depending on the type of model.

There is a lot to learn about ML(Machine Learning) that cannot be explained in a simple article like this. The Machine learning future in India is very bright. If you have the desired machine learning skills and need to pursue big data and machine learning courses in India, learn from pioneers like Imarticus.

Top Features of Amazon Sagemaker AI Service

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Amazon Sagemaker is the latest service that has changed the programming world and provided numerous benefits to machine learning and AI. Here’s how:

The Amazon Sagemaker or the AWS as its popularly known as has many benefits to organisations. It can scale large amounts of data in a short span of time, thereby reducing the overall cost of data maintenance.  Amazon Sagemaker provides data scientists with the right data to make independent strategic decisions without human intervention. It helps to prepare and label data, pick up an algorithm, train an algorithm and optimise it for deployment. All this is achieved at a significantly low cost.

The tool was designed to ensure that companies have minimum issues while scaling up when it comes to machine learning.  The most common programming language used for AI programs Python and also Jupyter Notebook is in-built into the Amazon Sagemaker.

You can start by hosting all your data on Amazon Sagemaker’s Jupyter Notebook and then allow it to process that information, post which the machine will begin the learning process.

One of the best features of Amazon Sagemaker is the ability to deploy a model which can be a tricky business. Apart from this, we have listed down the top features of Amazon Sagemaker below.

Build the Algorithm

The Sagemaker allows organisations to build accurate and relevant data sets in less time by using algorithms that support artificial intelligence and machine learning courses.  It becomes extremely easy to train machines using this service as they are given easy access to relevant data sources in order to arrive at correct decisions. It has the ability to automatically configure frameworks such as  Apache, SparkML, TensorFlow and more thereby making it easier to scale up.

Testing can be done locally

When there are broad open source frameworks such as Tensorflow and Apache MXNet, it becomes easy to download the right environment and locally test the prototype of what you have built. This reduces cost significantly and does not remove the machine from the environment it is supposed to function in.

Training

Training on Amazon Sage Maker is easy as the instructions for the same are specific and clear. Amazon SageMaker provides end to end solution to the training that is there is a setup of computer distributed cluster, and then the training occurs and when results are generated the cluster is torn down.

Deployment

Amazon Sagemaker has the feature of deploying on one click once the model production is complete and the testing is done.  It also has the capacity to do A/B testing to help you test the best version of the model, before deploying it. This ensures that you have the best results for the program itself.  This will have a direct impact on reduced cost due to continuous testing and monitoring.

Conclusion

Amazon Sagemaker service provides many benefits to companies who are heavily invested in deep learning and AI. These enable data scientists to extract useful data and provide business insights to organisations.

What Do You Need To Know For AI

Posted on by Imarticus Learning
Reading Time: 2 minutes

In a world where technology is developing at a rapid rate, fields that focus on automation and artificial intelligence are becoming the most lucrative. With many artificial intelligence courses available, it is important to remember that a strong base is fundamental.

So, the question is – What do you need to know before you can venture into a field like artificial intelligence?

Before heading to the pre-requisites, it is important to understand that AI is a field that is multi-dimensional. It can be used for anything from medicine to education. This also means programming AI is diverse, akin to law, where you constantly need to educate yourself on the updates of the technologies available in your field of AI.

Finally, the different fields of AI can have specific requirements, but on a broader scale, most AI in any field requires strong foundations that are basically the same. Here are a few things you need to know before studying about artificial intelligence.

Numbers Are Key
A strong understanding of mathematics is a must when venturing into artificial intelligence. The key here isn’t just knowing basic math. If you hope to venture into artificial intelligence, a deep understanding of discrete mathematics is part of the core foundation of the field.

Most artificial intelligence is based on various algorithms, and an understanding of these algorithms, as well as the ability to mathematically analyse them for errors and solutions, are considered the most basic requirement for AI.

Programming
 Much like math, programming is an essential part of artificial intelligence, implementing the mathematical data into code in a manner where you can not only develop but maintain and enhance machine learning is also part of the core foundation of AI. This means you must be able to code at a high level and find a way to be creative with code to improve the functions of a developing AI system.

In-depth knowledge of Python is often considered a mandatory pre-requisite to learning artificial intelligence as this open sourced programming language is currently the most popular and widely used.

Analyzing Data
 While programming and math are the foundations, the ability to analyse and interpret data is considered a cornerstone for anyone involved with developing AI. This skill is important as this is where the error guidance and solution base of AI stems from. Imagine a world where you create an algorithm and program that algorithm into a robot to vacuum.

This works successfully as a single task. Imagine now that you integrate another code into the same robot to do the dishes. The robot accidentally breaks the dishes or uses bleach to wash the dishes. This error is because the codes can overlap and create a fault or a bug. Data interpretation is essential to identify faults and bugs in order to rectify them.

Conclusion
While the three pre-requisites mentioned above are core tools for those studying AI, they aren’t the only ones you need. The field of AI you venture into may require knowledge of the field itself. An example of this is medical AI, where you will need an in-depth knowledge of medicine and how medicine functions. AI is ever growing, and its complexities are deep.

No matter the type of AI you choose to learn, a strong understanding of math, programming and the ability to analyse data accurately are a must.

Getting on the Right Artificial Intelligence Path

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Are you looking to expand your current skill-sets? Does Artificial Intelligence pique your interest? Artificial Intelligence uses software or machines to use intelligence similar to that of humans. Even the humble calculator is an example of artificial intelligence. The field of AI is currently focusing on creating systems that can reason, learn, present knowledge, plan, and understand natural language amongst many others.

If you want to jump into this new and exciting field of innovation, you might want to make sure that you have your basics covered. There are several artificial intelligence courses in India that you can enrol in. However, if you are looking to explore on your own, you can follow the path given below to give you an understanding of how AI functions.

Brush Up On Your Math
A strong understanding of mathematics is key to your ability to move forward in the field of AI. Knowing as much math as you can will definitely help you later, but at the start, you can focus on statistics, calculus, and optimization. There are several resources available online for these topics, and you can also brush the dust off your old math textbooks.

Learn A Language
No, we don’t mean French. You need to learn the right programming languages in order to be able to delve into Artificial Intelligence. Focus your time on learning Python, C, and C++. These languages come with well-stocked toolkits and libraries which will help you navigate your future projects. Each of these languages has their own benefits and limitations, but starting with Python is a good bet. Look up artificial intelligence online courses offered by Imarticus Learning.

Solve a Problem you Know
One of the best ways to get started on AI is to practice with a problem you know and are interested in. It will keep you motivated as you continue to delve deeper into the intricacies of AI. The problem should interest you and must come with ready to access data that can be worked with a single machine. You could also start with the Titanic Competition that is tailormade for beginners like you.

Make Your Own Bot
A BOT is a type of weaker AI that can complete automated tasks. Try your hand at building your very own Chatbot. An example of an advanced chatbot is the Google Search Engine. It has three basic components – input text, send button, and output text. You can explore open source platforms like XPath and Regex in order to build your very own chatbot. This chatbot can be complex or funny and helpful. You can choose what your bot does for you.

Participate in an Actual Kaggle Competition
Kaggle has many real-time competitions that see hundreds of enthusiasts try to solve a problem. You can test out your knowledge and also learn where you need to explore more. This opportunity also allows you to connect to other AI enthusiasts. The forums are a rich resource on problem-solving and debugging.

Free Resources
There are many places on the internet and artificial intelligence courses which will help you expand your knowledge of AI one skill at a time. A great free resource is the Intel AI Academy which provides much-needed support, tech, and other tools for beginners like yourself.

Artificial Intelligence Apps can Challenge Humans

Posted on by Imarticus Learning
Reading Time: 2 minutes

Try memorizing all the phone numbers from your contact list. Now recall the numbers of all people whose name begin with the letter ‘S’.

Possible? Maybe.

Easy? No.

Humans have finite limits, and that’s why man has trained artificial intelligence to mimic his learning.  Now, is there a future possibility of AI taking over humans?

Here are the latest artificial intelligence updates on software applications that challenge the human brain and its finite limits.

Latest AI Software Capabilities:

Deep Mind’s AlphaGo

The game is based on an abstract strategy of a board game with two players each trying to surround more areas than the rival. AlphaGo uses deep learning neural networks with advanced searches to win against humans. The software is an example of advanced AI learning on its own.

DeepStack

The game of Poker also fell to the might of DeepStack. Based on intuitive decisions and deep learning from self-play, the ML computes the possibilities instantly to base its decisions. It can be used in the fields of cybersecurity, finance, and health care.

Philip

MIT’s CS and AI Laboratory use a gamer “Philip” to kill multiple players. Using neural networks and deep self-learning on Nintendo games, the ML using Q learning and actor-critic techniques is successful most of the time.

COIN

This JPMorgan software COIN used in investment banking has made commercial contracts an instant process saving 360,000 human work-hours. The word “COIN” is coined from contract intelligence, and that’s what it exactly is! COIN uses ML which ingests data and picks up on error-free relationships and patterns.

AI Duet

The software is an artificial “pianist” and is created by Google’s Creative Lab using neural networks, Tensorflow and Tone.js.

LipNet

University of Oxford’s CS Department has eased disabilities by making lip reading easy. The software uses neural networks, video frames-to-text and spatiotemporal convolutions on variable length sentences to lip read. It definitely has a massive impact on the movie industry, disability-prone deaf, biometric identification, covert conversations, dictating silently in public spaces and so much more.

GoogLeNet

This Google system can detect cancer better than the most experienced pathologists. ML and smart algorithms can learn to scan and interpret images and predict a diagnosis with greater accuracy.

DeepCoder

Microsoft and Cambridge University have developed this software that writes its own code. They have trained the ML to forecast properties and outputs from inputs. The insights are used to augment searches for 3/6 line code. The DeepCoder uses the synthesis of programs and an SMT-solver to put the pieces together mimicking programmers. This eventually helps people who cannot code, but know where the problem lies.

In conclusion, it is true that humans have surmounted the challenges of artificial intelligence. Machine learning has taught and brought machines very close to mimicking human behavior and thinking. There could be a possibility of a clash between the abilities and capabilities in the human vs. AI war. Will machines and AI overtake us?

Not if we intelligently harness ML capabilities. We have to use our finite abilities to limit rogue applications. And that is a huge positive!

Technical Approaches for building conversational APIs

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Today’s GUIs can understand human speech and writing commands like the Amazon Echo and Google Home. Speech detection and analysis of human sentiments are now being used in your daily life and on your smart devices like the phones, security systems and much more. This means learning the AI approach.

The six smart system methods:
The existing artificial intelligence process and systems are not learning-based on interactive conversations, grounded in reality or generative methodology. The system of AI training needs to be one of the following.

Rule-based systems can be trained to recognize keywords and preset rules which govern their responses. One does not need to learn an array of new commands. It does need trained workforce with domain expertise to get the ball rolling.

Systems that are based on data retrieval are being used in most applications today. However, with speech recognition and conversational Artificial Intelligence courses being buzzwords, the need to scale and update quickly across various languages, sentiments, domains, and abilities needs urgent skilled manpower to update and use knowledge databases which are growing in size and volume.

The Generative methodology can overcome the drawbacks of the previous methods. In simple language, this means that the language system could be trained to generate its own dialogues rather than rely on pre-set dialogues.
The popular generative and interactive systems today incorporate one or all of the following methods to train software.

• Supervised learning is used to develop a sequence-to-sequence conversation mapping customer input to responses that are computer-generated.

• Augmented learning addresses the above issues and allows optimization for resolution, rewards, and engaging human interest.

• Adversarial learning improves the output of neural dialog which use testing and discriminatory networks to judge the output. The ideal training should involve productive conversations and overcome choice of words, indiscriminate usage and limitations on prejudging human behavior.

Methods relying on the ensemble that use the method most convenient to the context are being used in chatbots like Alexa. Low dialogue levels and task interpretation are primarily addressed. This method though cannot provide for intelligent conversations like human beings produce.

Learning that is grounded uses external knowledge and context in recognizing speech patterns and suggesting options. However, since human knowledge is basically in sets of data that is unstructured, the chatbots find it difficult to make responses of such unstructured data that are not linked to text, images or forms recognized by the computer.

The use of networking neural architecture into smaller concept based parts and separating a single task into many such components instantly while learning and training can help situational customization, external memory manipulation and integration with knowledge graphs can produce scalable, data-driven models in neural networks.

Learning interactively is based on language. Language is always developing and interactive when being used to enable collaborative conversations. The operator has a set goal based on the computer’s control and decisions. However, the computer with control over decision making cannot understand the language. Humans can now use SHRDLURN to train and teach the computer with consistent and clear command instructions. Based on experience it was found that creative environments were required for evolving models.

Which method to use is and how is where the creativity of human operators counts! To learn machine learning or an artificial intelligence and the systems of deploying it is the need of the hour no matter which technical method you use.

Is Machine Learning Right for You?

Posted on by Imarticus Learning
Reading Time: 2 minutes

The world today has been technologically changed by machine learning and big data analytics. Our challenges today, lie in understanding the large volumes of data we have created and using it intelligently. 

That is precisely what Machine Learning, Artificial Intelligence and machine learning courses in India have helped us with.Examples are everywhere and especially on your smartphone. ML has helped understand your shopping preferences and auto-suggests what you could be interested in. The same thing happens when you use your Facebook account which tags your friends and suggests videos that may interest you.

The Data Analyst and ML Engineer Roles
As a Data Analyst, your end goal is to use data to produce insights that are actionable by other humans. The ML Engineer does the same. However, its end goal is used by artificial intelligence systems to make the machines or systems behave in a particular way. This decision will impact the service or product and eventually the success of the enterprise.

Skills Required
ML requires a mix of skills to understand the complete environment, the how and the why of the issues you are designing and dealing with. Machine learning courses should ideally cover

Computer Science and Programming
Fundamentals including data structures, algorithms with their functioning, complex and complete solutions, approximation in algorithms, and system architecture. Hackathons, competitions in coding and plenty of practice are best at honing skills.

Statistics and Probability
The engine for ML runs on these and helps it in validating and building models from the provided algorithm which evolves from statistical models.

Evaluation and Data Modeling
These are important as ML build the model based on measures, weights, models, iterative algorithms and strategies it develops depending on its learning from the base algorithm.

Applying Libraries and ML Algorithms
Libraries and APIs like Theano, Scikit-learn, Tensor Flow etc., need a precise model and effective application for success.

Software Engineering and System Design
Output depends on the software and its design for applicability to provide robust, scalable and efficient solutions.

Job Roles with Demand
Data analysts, core ML engineers, applied ML engineers, and ML software engineers are jobs that will exponentially rise. Skills and Big data Hadoop training courses that help in applying ML algorithms and libraries will stand you in good stead. System design and software related jobs using ML, data modeling and evaluation, ML probability and statistics experts, and CS fundamentals and programming specialists jobs offer huge potential for professional development in the near future.

The Future of Machine Learning
Machine Learning, data analytics, AI and predictive analysis has no limits to its applicability and has already impacted every field like health, computers, life sciences, banking, education, insurance, finance, and literally every field you can think of.

Your weather forecasts, prices on stock exchanges, trends for the next decade, oil exploration, the MRI machines, predicting the subsequent breakdown, strategy building for marketing, automatic machine lines, and production are all today complex uses of techniques of using machine learning and AI for data analysis, analytics and predictive analysis. Will there be any field that is not impacted then by ML in the future?

If ML interests you then now is the time to update your knowledge and upgrade your skill-sets. There are courses and materials readily available. However, you will need a plan of action that you must adhere to. Good Luck!

How to Work on Deep Learning programming?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Learning Algorithms

Algorithms are at work all around us. Right from suggestions displayed in a text box while using Whats App to time boxing traffic signals, algorithms greatly improve the quality of human life these days. The more efficient the algorithm, the better the quality of service. Imagine an elevator system for a skyscraper with a thousand floors.

An adaptive machine learning algorithm can change the way it works depending on the demand and timetable of people going to different floors and dramatically reduce the waiting time for a person taking the elevator when compared to a static algorithm with no feedback loop.

Machine Learning is nothing but the improvement in performing a task with experience.The more the experience, the better is the performance of a machine learning algorithm. It can also be used for predicting the outcome of an event based on the historical data available. Filtering spam from your mailbox, Commute time predictions, Suggestions in social media, digital assistants are a few examples of the applications of machine learning algorithms.

Deep Learning and the Complexities involved

The fundamental rule in computer science is the use of abstractions. All concepts act as building blocks to another seemingly advanced concept, which is nothing but a layer of abstraction added over the older concept.

Algorithms, data structures, machine learning, data mining are the building blocks of Deep learning which is Machine learning and the concept of feature wise classification. Deep learning defines which feature characterizes a pattern and then uses data mining to classify, compare and define a feature.

Deep learning algorithms typically take more time to train but are more accurate and dependable as experience increases. They are used for speech recognition. NLP. Computer vision, Weather pattern analysis etc. They are usually implemented using neural networks. Deep learning is a subset of machine learning.

How to Learn Deep Learning programming

Below are few ways to understand and work on Deep learning:

  1. There are several machine learning courses, and deep learning courses available online,mostly in Python and R. Python training is usually a prerequisite for these courses. Some of the best ones are available in Udemy, Course Era, edX etc. These courses can be completed online and are prepared by the best minds in the field.  

  2. Understanding the inbuilt Python libraries: The future of machine learning and deep learning depends greatly on the inbuilt library support python provides. Tensor Flow, Thea nos, Pandas etc. are a few powerful libraries which it provides for programmers to explore deep learning concepts.

  3. Knowledge of Machine Learning or doing a machine learning course is generally preferred before diving into deep learning because conceptually machine learning is a general form of learning compared to the more specific deep learning. But based on the programmers understanding of the basic concepts, exposure to Python and R libraries, deep learning can also be started directly.

  4. However, the classic order is, do a python course -> Do a machine learning course -> Do a deep learning course and then contribute to the deep learning community after practice and execution.

  5. All the tools involved are opensource, so with sufficient interest, programming expertise and Python knowledge, cracking Deep Learning should be an easy task. Take part in the community and practice, practice, and practice to excel.

All the very best for your journey into Deep Learning..!!