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Tag: machine learning courses in India

AI, Data Science, Machine Learning Terms You Need to Know in 2022!

Posted on by Imarticus Learning

In the present paradigm of technical knowledge, it is imperative to be aware of certain concepts to survive and thrive. Whether you are pursuing a career in artificial intelligence (AI), have a cursory interest in data analytics, or simply wish to broaden your horizons, here are some artificial intelligence, data science, and machine learning terms you need to know in 2021. Read on…

  1.     Natural language processing: 

Both humans and computational devices use their own modes of language to communicate and share ideas to the extent of imparting and debating on the information. The languages, however, are different in their basic forms and formats. Using natural language processing, or NLP, artificial intelligence can decipher many human languages to suit specific functions that may range from the academic study of linguistics to providing utility to hearing-impaired people.

  1.   Data warehouse: 

A data warehouse, as the name suggests, contains a large ensemble of data pertaining to businesses and learnings from past successes and failures to provide better services. One who is not entirely proficient in data architecture may yet take the advantage of data warehouses to gather business analytics courses and make far better decisions. This method allows one to find new ways to process old data and change future iterations of that data with his/her actions. 

Career In Data Analytics   Data journalism: 

This is a mode of journalism that is slowly gaining greater prominence and is proving its necessity in combating the ever-growing trend of fake news. In this form of news reporting, one focuses on proving his/her assertions through the collection and presentation of reliable data. This may be done through human and/or AI collection and calculations. Soon, we may be able to have a collated base of data obtained through AI learning. This will make it very hard for individuals and/or groups to spread misinformation.

  1.   Deep learning:

This uses artificial intelligence to construct structures that mimic the human neural network – starting from simple problems to finding layers of hidden information. Meanwhile, it makes errors and learns from them with the program often ending up with a different solution than what was expected by its programmers and set parameters. Using this process, we can identify and solve possibly any real-world problem. The degree of human supervision in this process can be ascertained at various levels of this process.

  1.   Cybersecurity: 

Both defenders and attackers of databases are getting smarter, escalating the never-ending battles between cybersecurity and hackers. Often, the strategies used by either group are similar to the point of being indistinguishable. Here, any large organization employs AI and/or deep learning to be one step ahead of the threats that plague them.

The above-mentioned terms are only the tip of the iceberg when it comes to talking about new technology-related topics. Hopefully, they have provided you with new avenues to look into as per your interests, or at least recapitulated some of the basic terminologies.

TOP 10 APPLICATIONS OF DEEP LEARNING ARTIFICIAL INTELLIGENCE IN DIVERSE INDUSTRIES

Posted on by Imarticus Learning

Artificial Intelligence is the indispensable future. It is already in power and used by diverse industries like healthcare, education, and finance.

And now, deep learning has come as an addition to the next level of technological advancement. This blog post explores ten applications of artificial intelligence across different industries.

 TOP 10 BEST APPLICATIONS OF DEEP LEARNING

Virtual Assistants

AI Deep Learning has led to virtual assistants that understand natural languages; the best examples to quote being Siri, Alexa, and Google Assistant. The technology allows them to comprehend human speech more than before, turning everyday words into actionable data.

A virtual assistant is an application that handles day-to-day tasks and answers questions using artificial intelligence, natural language processing (NLP), and machine learning algorithms. Today, there are many popular virtual assistants: Amazon’s Alexa, Apple’s Siri; Microsoft Cortana; Google Now.

Chatbots

The chatbot has rules that use natural language processing to communicate with users. Chatbots can engage in one-on-one conversations and group chats on Facebook, Slack, or Telegram platforms.

Healthcare

Deep Learning & Artificial intelligence has found their application in diagnostics and healthcare. It combines the input of a large set of variables with historical patterns from similar cases to make accurate predictions on patient outcomes. It enables doctors to provide better diagnoses and personalized treatments.

Entertainment

Creators are using it to engage their audiences and create new experiences. For example, many music companies are using it for music composition. In contrast, other multimedia giants like Disney explore storytelling possibilities such as virtual reality movies or interactive games.

News Aggregation

A news aggregator is an application that collects articles, videos, and other content from different sources to organize it into categories.

Composing Music

Computer-generated music is possible because of AI methods. Such as generative adversarial networks (GAN). Computers can create new musical compositions inspired by those composed by humans.

Image Coloring

This technology is being used for image coloring as well. It is a visualization of an uncolored photo or artwork. It helps artists and designers understand how their work will look when it gets printed on paper.

Robotic

AI/ML and Deep Learning allow robots to learn from their own experiences by performing a task. For example, they can become more efficient at drilling holes in walls. Thus they perform the same action many times across different surfaces through deep reinforcement learning.

Automobiles

Automobile companies are also exploring the benefits of applying this technology to their cars. They have begun using computer vision and image processing techniques, which allow vehicles to learn how to drive over time by detecting any obstacles on the road. This technology helps prevent accidents as well as reduces traffic congestion through self-driving cars.

E-commerce

E-commerce uses it for product recommendations and helps consumers make better buying decisions. Thus providing them with a shopping experience based on their preferences and behavior patterns.

Why Enroll In AI Progam At Imarticus Learning

artificial intelligence courses in IndiaImarticus Learning offers Artificial Intelligence and machine learning courses that improve students’ foundational abilities.

Take advantage of the Expert Mentorship programs from Imarticus Learning to learn about Artificial Intelligence and Machine Learning in a real scenario.

This program enables you to gain access to attractive professional prospects in the disciplines of Artificial Intelligence and Machine Learning. This intensive 9-month curriculum prepares students for roles like data scientist, Data Analyst, Machine Learning Engineer, and AI Engineer.

Some course USPs:

  • The course lets the students learn job-relevant skills that prepare them for an exciting Data Science career.
  • Impress employers & showcase AI skills with a certification endorsed by the most prestigious academic collaborations of India – E&ICT Academy, IIT Guwahati, and Imarticus Learning.
  • World-Class Academic Professors to learn from through live online sessions and discussions. This will help students learn the practical implementation of AI/ML & Deep Learning techniques through real-world projects.
  • Flexible Learning Journey that gives you the flexibility to transfer program credits for a period of 1 year.

For further details, contact us through the Live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, and Gurgaon.

How Can Computer Vision Protect Millions of Homes From Intrusion?

Posted on by Imarticus Learning

Introduction

We need to embrace the concept of computer vision in homes rather than shy away from the idea of exchanging personal data to achieve new levels of protection, safety, comfort, and entertainment. Computer vision combined with NLP and ML enables computers/systems via digital images or video to understand what they see.

When systems can detect and recognize objects, according to what they are scheduled to do, they can deliver intelligent behavior. Automotive space is one area that has successfully demonstrated how computer vision can change our lives. Car systems that use computer vision can recognize the driver behind the wheel and can warn the driver when he starts to swerve out of his lane to see the surrounding area.

Many customers on their smartphones are already using computer vision and don’t even know it. To recognize facial features and position overlays (philters) in the right positions, both Snapchat and Instagram use computer vision tracking.

How does Computer Vision help us in making things secure?

Accepting computer vision into your house and connecting it to your connected devices helps your daily routine to have a new level of convenience. When you arrive and open the door for someone, the front door will be able to see or stay locked when an unknown person (face) approaches. Alarm systems are smarter, able to distinguish who are family members (including age and gender) and who are not.

If an elderly family member or visitor trips, or if a child is climbing up the stairs, on the countertop, or anywhere that puts the child in danger, indoor surveillance cameras will send a warning to your mobile, taking it a step further. Nest, Logitech, and other smart home manufacturers have either begun offering customers these smart security features as a premium subscription service or have already incorporated them into their newest devices.

Computer Vision in Intrusion Detection

Abbreviated as IDS, an Intrusion Detection system plays an important role in providing the required security assurances for all networks and information systems in the world. One of the solutions used to decrease malicious attacks is IDS. As attackers often change their attack tactics and find new methods of attack, IDS must also develop by implementing more sophisticated detection methods in response.

The enormous data growth and substantial developments in computer hardware technology have led to the existence of new studies in the field of deep learning, including intrusion detection.

To provide a high degree of security and security staff monitoring effectiveness, high-performance AI systems can make the task monitoring process automatic for high-risk sites. Also, these intrusion systems can identify objects based on size and location. However, they fail to recognize the type or form of the detected object.

Perimeter Defense (Intrusion Detection) systems with high-end artificial AI algorithms to identify a multitude of different types of objects can now discern objects of interest, thus dramatically reducing the rate of such intrusions that might indicate a false alarm. The more sophisticated systems, such as those provided at IronYun, allow its customers to design ROIs based on intrusion detected points, high-value areas, and or any other region that may be beneficial for alerts.

Similarly, the applications designed for face and license plate recognition have the ability to detect people or cars(the license plate) in addition to solutions for motion detection and use pre-designed data to identify distinct faces or plates that should be watched regularly, similar to the pre-designed lists.

Needless to say that these systems will also allow its customers to search for faces that are not provided already on the camera. For example, if a person is identified hanging outside a house many times, one can store their pictures in the designed watchlist and fix an alarm when the face is identified again around the house or in your surroundings.

The main advantage of the system is that before the troublemaker completes the act, the warnings will assist in discouraging and avoiding vandalism or robbery and inform the authorities of the scene.

Conclusion

AI-based security measures combined with computer vision, deep learning, ML, and NLP training can do all the boring work for you to help deter fraud and vandalism. They are also the most accessible security solutions available with a strong return on investment due to their low cost and outstanding reliability.

computer vision coursesStopping crime is a challenging, ongoing challenge, but enterprise vendors and law enforcement can do it more easily with the right AI apps. This is also one of the reasons why people are excited about an acceptable career in the AI sector.

Jobs of The Future: Artificial Intelligence and Machine Learning

Posted on by Imarticus Learning

COVID-19 has inverted the ways we lived. The jolts can be felt across workplaces, particularly where it has forced organizations to reduce activities, including leisure, restaurants, oil & gas, and airlines. Throughout COVID-19, the technology industry remains strong. The pandemic spurred technological innovation and enabled many to continue work despite lockdowns & other pandemic mitigation measures.

Benefits of AI?

  • Automation: AI gives a better understanding of machines to interpret a situation or perform necessary action. Tasks can be automated with minor human intervention through AI/ML. While automation takes place, the roles requiring human attention automatically become more productive with more time to focus on them.
  • Speed: AI is efficient in expediting much work when compared to humans. AI lets us complete tasks flexibly before deadlines. This reduces human labor & provides great speed & efficiency.
  • Accuracy: AI eliminates maximum chances of error. The machine always acts according to a fixed AI algorithm; there are fewer errors in every given scenario. In short, AI defines new limits of accuracy & precision with lesser risks.
  • Exploration: AI has helped to discover many new sites, for example, volcanic sites, ocean beds, etc. Humans being vulnerable to these sites, can’t reach and survive these scenarios. Robots are meant to go to these places and collect data.
  • Data Collection & Analysis: Data analytics is the future technology in today’s business world. Industries & businesses analyze valuable chunks of data & extract helpful information.

Applications of AI?

Artificial Intelligence and Machine Learning courses in IndiaAI is applicable in every conceivable field & recent advancements are increasing the relevance of AI in every sphere. Here are the top applications of AI:

  • Speech Recognition: AI allows us to convert spoken words into digital content. Speech recognition has various uses like voice-enabled messaging, content writing, voice-controlled remotes, & appliances. Speech recognition is also used for authorization & validation.
  • Natural Language Processing: NLP enables a machine to understand the human text. Virtual assistants like Siri, Google Assistant, Alexa, are all an example of chatbots working on the principle of NLP.
  • Stock Trading: There are AI platforms that allow automated stock trading. With the algorithms, these bots understand the fluctuations in the stock market & predict high-return stocks with more accuracy. The future scope of AI/ML in the finance sector is fuelled up due to the increasing craze for cryptocurrency.
  • Robots: Besides developing intelligent robots, AI has created robots that assist humans with routine tasks like cleaning, gardening, serving, etc.

Explore Careers in Artificial Intelligence with Imarticus Learning:

Freshers need to realize their competencies & acquire skills for AI roles with chances of upward mobility in career. The future scope of Artificial Intelligence is increasing due to new job roles & advancements in the AI sector. 42% of the IT workforce in India will require upskilling or reskilling by 2025. Imarticus Learning offers artificial intelligence and machine learning courses and machine learning certification courses to upskilling & and stay relevant.

The program builds a strong foundation of Data Science concepts. Industry experts will help you learn the practical implementation of Machine Learning, Deep Learning, and AI techniques through real-world projects from diverse industries. The 9-month extensive program will help you prepare for the Data Analyst, Data Scientist, Machine Learning Engineer, and AI Engineer roles.

This state-of-the-art Artificial Intelligence and Machine Learning Certification Course aim to let students learn machine learning & prepare for future jobs.

For further details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Bringing AI and Machine Learning Accessible to Enterprises Credit to Cloud!

Posted on by Imarticus Learning

Artificial Intelligence (AI) technology has been a game-changer for businesses. It has revolutionized how businesses operate and get the work done. Artificial intelligence technology imparts machines with the ability to understand and apply intelligence while processing complex data that would’ve earlier required human aid. Machine learning is a part of Artificial Intelligence technology and entails training machines to process information using large data sets.

Let’s discuss a real-life scenario to understand the functioning of machine learning technology better. Have you ever wondered why the prices keep on fluctuating when you book a Cab using Uber? Well, that’s machine learning technology into action for you.

Dynamic pricing is how the machine learning algorithms leverage buyer’s curiosity, demand, traffic congestions, etc. to regulate the cost and price the fare accordingly. Machine learning is increasingly being deployed by organizations to help with complex real-time data processing.

AI & ML Accessibility  

Accessibility has always been a challenge when it comes to adopting AI & ML technology for businesses; cloud solutions have helped paved the way for even smaller businesses to adopt AI & ML technology. Here is a list of few cloud services that is changing the way businesses adapt to AI & ML solutions.

  1. Amazon Web Services (AWS)

Amazon needs no introduction; it has always been about boosting customer satisfaction and improving business practices. AWS is a cloud solution offering from Amazon that provides a diverse range of machine learning solutions including Amazon SageMaker that simplifies the process of creating, training, and deploying machine learning models to work. Other machine learning-related solutions by AWS includes dynamic pricing models, search recommendations, automated customer service, etc.

  1. Google Cloud

Google’s cloud solution is second in this list of cloud services that have made machine learning more accessible for companies. After the development of an open-source platform named TensorFlow, Google has achieved new heights in the AI & ML arena. In addition to its indigenous open-source application, it is also associated with DeepMind, one of the most prominent players in the machine learning space. AlphaGo is a flagship program by DeepMind that has revolutionized the machine learning and AI space.

  1. Azure by Microsoft

Azure by Microsoft is another prominent name in the list of cloud platforms that have made machine learning more accessible for organizations of all sizes. Azure boasts of in-built machine learning services for organizations that want to leverage machine learning models into their business operations. To make it more easily and user-friendly it has both code-based and drag and drop functions. Azure aims to revolutionize the machine learning space by focusing on building a bias-free responsible machine learning solution.

Conclusion

Machine learning is an indispensable tool for businesses in the contemporary that rely on the use of sophisticated technology to operate and reach new customers. Machine learning career is in huge demand as more and more businesses are leveraging this remarkable technology to grow their business and optimize their operations.

One can opt for a machine learning course from reputed institutions like Imarticus Learning to obtain comprehensive knowledge about this technology and obtain a job with some of the most reputed organizations.

What are the best practices for training machine learning models?

Posted on by Imarticus Learning

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.

Artificial Intelligence Apps can Challenge Humans

Posted on by Imarticus Learning

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!

Is Machine Learning Right for You?

Posted on by Imarticus Learning

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

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..!!

Why Knowing Python Is Essential For AI And Machine Learning?

Posted on by Imarticus Learning

Getting started in a field like machine learning or artificial intelligence can be a challenge. Due to the numerous coding mechanism sand tools available to help you program your potential AI software, using an open-source tool like python is considered an essential skill. Python is one of the easiest coding languages around today and is also one of the most versatile and wide-spreading tools available.

To learn Machine Learning and AI you will need a specific language. There are several languages that one can learn including C++, Java, and R. However, most industry experts agree that Python is one of the best places to start. It has a well-stocked library and comes with an extensive and diverse toolkit.

Here is a closer look at how you can chart your learning of Python for Machine Learning and AI.

  1. Learn the Syntax

Python is all about the various syntax. The good news is that you do not have to learn all of it. However, there is no getting around learning the basic syntax of Python. With this step, it is recommended that you do not spend too much time on it. A few days, up to a week, is enough to learn the basic syntax of Python as you can always refer to it later.

There are many places on the web where you can familiarize yourself with artificial intelligence courses including on the main Python website.Other web pages include Imarticus Learning who teach Python with learning data science as the end game.