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Tag: machine learning training

What Are The Machine Learning Interview Questions?

Posted on by Imarticus Learning

 

It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition.

Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

Here is a collection of a comprehensive set of interview questions about machine learning and guidelines for the answers:

1. What are the different types of machine learning?

Machines learn in the following ways:

Supervised learning: A supervised learning essentially needs a labeled data which are pre-defined data set using which machines provide a result when new data is introduced.

Unsupervised learning: Here machines learn through observation and defines structures through data as these models do not require labeled data.

Reinforcement learning: Here there is an agent and reward which can meet by trial and error method. Machine tries to figure out ways to maximize rewards by taking favorable action.

2. How does machine learning differ from deep learning?

Machine learning essentially uses algorithms to parse data, learn from them and makes informed decisions based on the learnings. Whereas, deep learning structures different algorithms and gimmicks an artificial neural system to make intelligent decisions by learning on its own.

3. Having too many False positives or False negatives which one is better? Explain

It completely depends on the question and domain for which we are figuring out a solution. For a medical domain showing false negatives may prove risky as it may show up no health problems when the patients are actually sick. If spam detection is the domain then false positives may categorize an important email as spam.

4. What is your idea about Google training data for self-driving cars?

Google uses Recaptcha to sense labeled data from storefronts and traffic signals from its eight sensors interpreted by Google’s software. Creator of Google’s self-driving car Sebastian Thrun’s insights is used to build a training data.

5. Your thoughts on data visualization tools and which data visualization libraries do you use?

You may explain your insights data visualization and your preferred tools. Some of the popular tools include R’sggplot, Python’s seaborn, Matplotlib, Plot.ly, and tableau.

6. Explain about a hash table?

In computing, a hash table is a data structure which that implements an associative array. It uses a hash function using which a key is mapped to certain values.

7. Explain the confusion matrix?

Confusion matrix or error matrix essentially visualizes the performance of algorithms in machine learning. In the below table TN= True negative, FN=False negative, TP=True Positive and FP=False positive.

8. Write pseudo-code for a parallel implementation by choosing an algorithm

Enlighten your knowledge about pseudo-code frameworks such as Peril-L and some visualization tools like Web sequence diagram to aid you in showcasing your talent to write a code that reflects parallelism well.

9. How do you handle missing or corrupted data in a dataset efficiently?

You could identify missing or corrupted data in a dataset and ideally drop them or replace them with another value. In pandas isnull() and dropna() are two useful methods which can be used to identify columns of missing or corrupted data and drop them or replace an invalid value with a placeholder value like fillna().

10. Difference between a linked list and an array?

An array consists of an ordered collection of objects wherein it assumes that every object has the same size. A linked list, on the other hand, is a series of objects with directions as to sequentially process them which helps a linked list to grow organically than an array.

Conclusion

For becoming a successful machine learning engineer, you could join Machine learning certification training to make yourself proficient in various topics of machine learning and its algorithms. From this curated list of interview questions, you would have understood that machine learning is an internal part of data science. Use these sample questions to broaden your knowledge about the questions that may pop up in your interview and be ready to spellbind the interviewer with your swift answers.

For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

How Can You Learn Deep Learning Quickly?

Posted on by Imarticus Learning

 

Why is Deep Learning important to learn in today’s world of ever-changing technologies? Human capabilities to do tasks especially on very large volumes of data are limited. AI stepped in to help train computers and other devices to aid our tasks. And how does it do so? The evolved devices use ML to learn by themselves recognizing data patterns and arriving at predictions and forecasts very much like the human brain. Hence one would need to learn all of the above-mentioned concepts to even reach the deep-learning possibility.

In order to learn ML, one would need to have knowledge of Java, R or Python and suites like DL4J, Keras, and TensorFlow among others depending on the areas you are interested in. It is also important to have the Machine Learning Course before one delves into deep-learning. And yes there is a lot of statistics, probability theory, mathematics and algebra involved which you will have to revise and learn to apply.

 

If you are interested in learning Deep Learning quickly, here are the top four ways to do so.

A. Do a course: One of the best ways is to scour the net for the best top free MOOC courses or do a completely paid but skill oriented course. Many are online courses and there are classroom courses as well. For the working professional course from a reputed training partner like Imarticus Learning makes perfect sense. Just remember that to learn Deep learning you will need to have access to the best industry-relevant solutions and resources like mentoring, assured placements, certification and of course practical learning.

B. Use Deep Learning videos: This is a good resource for those with some knowledge of machine learning and can help tweak your performance. Some of the best resources of such videos are ML for Neural Networks by the Toronto University, the tutorials of Stanford University on Deep Learning, ConvNet resources on Github, and videos by Virginia Tech, E and CE, the Youtube, etc.

C. Community Learning: There are communities available online like the Deep Learning community and r-learning communities from Quora, Reddit, etc. Such communities can be of immense help once you have a firm grasp of the subject and need to resolve or are practicing your skills.

D. DIY books: There is a wealth of books available to learn Deep Learning and understand the subject better. Do some research on the best deep-learning resources, the limits of it, differences between ML and deep-learning, and such topics. DIY books are easy to read and hard to practice with. Some excellent books are the TensorFlow-Deep Learning, Nielsen’s Neural Networks-and-Deep Learning, and Chollet’s Python and Deep Learning.

The Disadvantages:

  1. Rote knowledge is never really helpful and the syllabus is very vast and full of complicated subjects.
  2. The practice is the key is only acquired through constantly doing relevant tasks on relevant and industry-standard technology.
  3. Mentorship is very important to learn the current best practices.
  4. Time is a constraint, especially for working professionals.
  5. The best value courses are often paid-for courses.
  6. DIY is bereft of certification and hence a measure of your skills.
  7. The DIY approach may also never train you for the certification exams.
  8. Assured placements in the paid for courses are a huge draw for freshers making a career in deep-learning.
  9. There are non-transferable soft-skills that you require and do not find in the packages.
  10. Industry acceptance is often sadly lacking for the self-learning candidates.

Conclusion:

Learning is always a process where reinforcement and practice scores. Though there are many options available to do deep-learning for free and on one’s own, the route is never easy. Thus it seems the paid courses, like the one at Imarticus Learning, is definitely a better bet. Especially if the course is combined with mentorship of certified trainers, assured placements, widely accepted certification, personalized personality-development modules and a skill-oriented approach with tons of practice as the one at Imarticus is.

The Imarticus Learning courses deliver well-rounded and skilled personnel and offer a variety of latest technology courses which are based on industry demand.

Given the above information, the quickest way to master deep-learning definitely appears to be doing a course at Imarticus. If you want to be job-ready from day one, then don’t wait. Hurry and enroll. We have multiple centers in India – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon and Ahmedabad. So you can consider as per your need!!

 

How Do You Start Learning Artificial Intelligence? Is it Possible to Get Research Work in The Field of AI?

Posted on by Imarticus Learning

The last decade saw the introduction of Machine Learning Training, Deep-Learning and Neural networks in AI to acquire the capacity to reach computational levels and mimic human intelligence.
The future scope of Machine Learning appears bright with ML enabled AI being irreplaceable and a composite part of evolving technologies in all verticals, industries, production means, robotics, laser uses, self-driven cars and smart mobile devices that have become a part of our lives. It thus makes perfect sense to learn Machine Learning and make a well-paying career in the field. Since the early 50’s a lot of research has gone into making these developments possible, and the trend for continued research into AI has made it the most promising technology of the future.

Why study AI:

AI rules and has become a reality in our lives in so many different ways. From our smartphones and assistants like Siri, Google, Alexa etc, the video games and Google searches we do, self-driven cars, smart traffic lights, automatic parking, robotic production arms, medical aids and devices like the CAT scans and MRI, G-mail and so many more are all AI-enabled data-driven applications, that one sees across verticals and without which our lives would not be so comfortable. Fields like self-learning, ML algorithm creation, data storage in clouds, smart neural networking, and predictive analysis from data analytics are symbiotic. Let us look at how one can get AI skills.
Getting started with AI and ML learning:
To start AI learning the web offers DIY tutorials and resources for beginners and those who wish to do free courses. However, there is a limit to technical knowledge learned in such ‘learn machine learning’ modules, as most of these need hours of practice to get adept and fluent in. So, the best route appears to be in doing a paid classroom Machine Learning Course.

Here’s a simple tutorial to study ML and AI.

1. Select a research topic that interests you:

Do brush through the online tutorials on the topic on the internet. Apply this to small solutions as you practice your learning. If you do not understand the topic well enough use Kaggle the community forum to post your issues and continue learning from the community too. Just stay motivated, focused and dedicated while learning.
2. Look for similar algorithm solutions:
The process of your solution would essentially be to find a fast solution and it helps when you have a similar algorithm. You will need to tweak its performance, make the data trainable for the ML algorithm selected, train the model, check the outcomes, retest and retrain where and when required by evaluating the performance of the solution. Then test and research its capabilities to be true, accurate and produce the best results or outcomes.

3. Use all resources to better the solution:

Use all resources like data cleaning, simple algorithms, testing practices, and creative data analytics to enhance your solution. Often data cleaning and formatting will produce better results than self-taught algorithms for deep learning in a self-taught solution. The idea is to keep it simple and increase ROI.

4. Share and tweak your unique solution:

Feedback and testing in real-time in a community can help you further enhance the solution while offering you some advice on what is wrong and the mentorship to get it right.

5. Continue the process with different issues and solutions:

Make every task step problem you encounter an issue for a unique solution. Keep adding such small solutions to your portfolio and sharing it on Kaggle. You need to study how to translate outcomes and abstract concepts into tiny segmented problems with solutions to get ahead and find ML solutions in AI.

6. Participate in hackathons and Kaggle events:

Such exercises are not for winning but testing your solution-skills using different cross-functional approaches and will also hone your team-performance skills. Practice your collaborative, communicative and contributory skills.

7. Practice and make use of ML in your profession:

Identify your career aims and never miss an opportunity to enroll for classroom sessions, webinars, internships, community learning, etc.
Concluding notes:
AI is a combination of topics and research opportunities abound when you learn to use your knowledge professionally. Thus the future scope of Machine Learning which underlies AI contains newer adaptations which will emerge. With more data and emerging technological changes, the field of AI offers tremendous developmental scope and employability in research and application fields to millions of career aspirants.
Do a machine learning training at Imarticus Learning to help with improving your ML practical skills, enhance your resume and portfolio and get a highly-paid career with assured placements. Why wait?

Ships Of The Future -Will Run on AI Instead of A Crew?

Posted on by Imarticus Learning

Technology has taken a high route since Artificial Intelligence has gained immense impetus over the years. Alexa and Siri have become household names as millions of their users, start the day, and close the same with them.

Artificial Intelligence is also seen to be transforming a number of industries including the shipping industry.

This means that your cruise ships are about to you take you into the future. They will be driven by artificial intelligence instead of a crew member. In the year 2017, two friends Ugo Vollmar and clement Renault were all set to work on a self-driving car project until they stumbled upon an article that talked about autonomous shipping which made them sail in a different direction.

Human resources and autonomy 

Autonomy would operate in a different manner when it comes to water than it does for roads. In the case of waterways, it will not completely eliminate the human resources on board. This is because when it comes to cars, there is only one person that takes over the entire control to operate it while for ships, there is a bare minimum of at least 20 crew members on board, all of them being assigned crucial duties.

Thus, in the case of roads, that one person can be completely replaced by autonomy, but not all the crew members can be replaced by autonomy in its entirety.

“Diesel engines require replacement of filters in oil systems—the fuel system has a separator that can get clogged. There are a lot of these things the crew is doing all the time” quoted Oskar Levander, the head of Rolls Royce’s autonomous system efforts.

This is why it can be said that the helm is most likely to be operated with autonomy using a robot or remote control while a part of the crew can help in taking care of the vessel. In addition to this, these automated journeys will have special rules created by the International Maritime Organisation which is most likely to happen in the coming years.

Key examples

One of the examples of companies that have employed artificial intelligence in order to robotize ships is Shone. They visualize employing artificial intelligence by planting sensors like radar and cameras that can help simulate a number of hazards around the ship and to navigate amidst them. Autonomous shipping helps in cutting costs of consumer goods as well as provides a safer environment for passenger ferries and cruise liners. Tugboats and ferries are likely to operate autonomously for at least a part of the time, the ones that only operate for shorter distances and time duration.

Finland and Norway have staked out testing areas for pioneering the commercial applications of autonomous systems that are likely to happen on the small coastal waters of Scandinavia. Rolls Royce orchestrated the first-ever public demonstration of an autonomous voyage by a passenger’s vessel. It was a state-run vessel that happened to avoid obstacles for 1 mile and also docked automatically.

Rolls Royce also revealed that on the day of the demonstration and the trails before that, the vessel was able to perform well even in rough waters, handling snow and strong winds which indicates that we are moving towards a world that will have machines employed everywhere to augment our experiences and make life easier.

Transportation made easy

At ports like Scandinavia where small ferries play a crucial part in the transportation network, in order to carry cars across fjords and connecting them to islands, autonomous systems will have it made it a lot easier. This is because the remote-control systems could allow for an expansion of service at the routes that are not very long, especially during the late hours and help reduce staffing, thus cutting costs, increasing efficiency and saving time. You can save big bucks by employing autonomous systems as the crew costs are really high and you can eliminate a big part of the same with artificial intelligence.

In a nutshell, we can say that we are moving towards living in a world that will be much easy to live in. Machine learning Training and Artificial Intelligence are taking over various industries eliminating its glitches and making operations better and more efficient.

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a form from the website or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi and Gurgaon.

What are The Top 10 Algorithms in Machine Learning?

Posted on by Imarticus Learning

Machine learning is the essential part of the developing technology of Artificial Intelligence. It analyses enormous amounts of data and comes at customized predictions which can help the user to deal logically with an overload of information.

A student of Machine Learning course must be aware of the need of making algorithms since these are what enhance the self-teaching capacities of the system. There are three primary techniques to design an algorithm- supervised, unsupervised and reinforced.


Also Read: What is The Easiest Way To Learn Machine Learning?

Here is a list of the top 10 algorithms which every Machine Learning student must know about –

  1. Decision Tree is one of the most comfortable supervised structures that is very useful to form deep connections and is based on questions in Boolean format. The fabric is systematic and easy to understand, and it is beneficial to determine model decisions and outcomes of chance-events.
  2. Naive Bayes is a simple and robust algorithm for classification. The “naive” term implies that it assumes every variable to be independent which can turn out as impractical sometimes. However, it is a great tool that is successfully used in spam detection, face recognition, article classification, and other such operations.
  3. Linear Discrimination Analysis or LDA is another simple classification algorithm. It takes the mean and variance values across classes and makes predictions based on the discriminated value assuming that the data has a Gaussian curve.
  4. Logistic Regression is a fast and effective statistical model best used for binary model classifications. Some real-world applications of this algorithm are scoring credit points, understanding rates of success in market investments and earthquake detection.
  5. Support Vector Machines or SVMs are a well-known set of algorithms which is binary based. The principle is to find the best separation of variables in a hyperplane. The support vectors are the points which define the hyperplane and construct the classifier. Some successful sites to try this algorithm is image classification and display advertising.
  6. Clustering algorithm follows the unsupervised technique, and it works on the principle of determining the more similar characteristics of nearby parameters to patch themselves up in a set cluster or group. There are different types of clustering algorithms such as centroid-based algorithms, dimensionality reduction and neural networks.
  7. Linear regression is a very well understood form of the algorithm which works on quite the same mathematical formula of a linear equation in two dimensions. It is a well-practised algorithm to determine the relationship between two variables and can be used to remove unnecessary variables from your target function.
  8. Ensemble methods are a group of learning algorithms working on the principle of predictive analysis. They construct a chain of classifiers such that the final structure is established to be a superior one. They are very efficient regarding averaging away with biases in poll decisions, and the algorithms are entirely immune to the problem of over-fitting.
  9. Principal component analysis or PCA employs an orthogonal transformation to convert relatable variables into a set of uncorrelated variables called principal components. Some essential uses of the method are compression and data simplification
  10. Independent Component analysis or ICA is a statistical method to determine underlying data which come obscured in data signals and variables. Relative to PCA, this is a more powerful method and works well with applications like digital images, documented databases and psychometric detections.

While no algorithm in itself can be guaranteed for a specific result, it’s always ideal to test multiple algorithms cumulatively. The ultimate task of an algorithm is to create a target function which can process a set of input into detailed output data.

Related Article :

How have statistical machines influenced Machine Learning?

Posted on by Imarticus Learning

The past few years have witnessed tremendous growth of machine learning across various industries. From being a technology of the future, machine learning is now providing resources for billion-dollar businesses. One of the latest trend observed in this field is the application of statistical mechanics to process complex information. The areas where statistical mechanics is applied ranges from natural models of learning to cryptosystems and error correcting codes. This article discusses how has statistical mechanics influenced machine learning.
What is Statistical Mechanics?
Statistical mechanics is a prominent subject of the modern day’s physics. The fundamental study of any physical system with large numbers of degrees of freedom requires statistical mechanics. This approach makes use of probability theory, statistical methods and microscopic laws.
The statistical mechanics enables a better study of how macroscopic concepts such as temperature and pressure are related to the descriptions of the microscopic state which shifts around an average state. This helps us to connect the thermodynamic quantities such as heat capacity to the microscopic behavior. In classical thermodynamics, the only feasible option to do this is measure and tabulate all such quantities for each material.
Also, it can be used to study the systems that are in a non-equilibrium state. Statistical mechanics is often used for microscopically modeling the speed of irreversible processes. Chemical reactions or flows of particles and heat are examples of such processes.
So, How is it Influencing Machine Learning?
Anyone who has been following machine learning training would have heard about the backpropagation method used to train the neural networks. The main advantage of this method is the reduced loss functions and thereby improved accuracy. There is a relationship between the loss functions and many-dimensional space of the model’s coefficients. So, it is very beneficent to make the analogy to another many-dimensional minimization problem, potential energy minimization of the many-body physical system.
A statistical mechanical technique, called simulated annealing is used to find the energy minimum of a theoretical model for a condensed matter system. It involves simulating the motion of particles according to the physical laws with the temperature reducing from a higher to lower temperature gradually. With proper scheduling of the temperature reduction, we can settle the system into the lowest energy basin. In complex systems, it is often found that achieving global minimum every time is not possible. However, a more accurate value than that of the standard gradient descent method can be found.
Because of the similarities between the neural network loss functions and many-particle potential energy functions, simulated annealing has also been found to be applicable for training the artificial neural networks. Other many techniques used for minimizing artificial neural networks also use such analogies to physics. So basically,  statistical mechanics and its techniques are being applied to improve machine learning, especially the deep learning algorithms.
If you find machine learning interesting and worth making a career out of it, join a machine learning course to know more about this. Also, in this time of data revolution, a machine learning certification can be very useful for your career prospects.

How important is the R programming language nowadays?

Posted on by Imarticus Learning

R is a popular programming language used for statistical computing and graphics by developers. This open sourced tool is not only just a programming language but also an excellent IDE. One important field of its applications is data analysis. Statisticians and data miners largely prefer R to develop their statistical software. However, R is not as popular as programming languages such as Java or Python. This article discusses the importance of R in the current era where data is everything.
How important is R?
We know that programming software like python offers an easy to understand syntax and higher versatility. Yet, R is preferred among data analysts. The reason for is that R was designed for statisticians. Hence R comes with field-specific advantages such as great data visualization features. A large number of major organizations are found using R in their operations. Google not only uses R but developed the standards for the language which got wide acceptance.
Revolution Analytics, kind of a commercial version of R was purchased by Microsoft and they provided servers and services on top of it. So, in general, despite the steep learning curve and uneasy syntax, R has its own advantages and the industry has recognized it very well.
In the opinion of experts, R is expected to remain as an indispensable resource for the data scientists for a very long time. The wide range of pre-defined packages and libraries for the statistical analyses will keep R in the top. The introduction of platforms such as Shiny has already resulted in increased popularity of R, even among the non-specialists.
So, Should You Continue Taking that Course Teaches Machine Learning via R?
It is known that every professional with a machine learning certification has huge career opportunities waiting ahead. But it is important to possess the exact skills the employers are looking for. So, is R such a skill wanted by employers? Well, it is observed that organizations are moving towards Python at a slow pace. In academic settings and data analysis R is still most popular, but when it comes to professional use, Python is leading. Python has achieved this by providing substantial packages similar to R. Even though most machine learning tasks are doable by both languages, Python performs better when it comes to repetitive tasks and data manipulation. A better possibility of integration is another advantage of Python. Also, your project may consist of more than just statistics.
It is recommended to start learning Python if you haven’t spent much time with your Machine Learning course that teaches through R. After learning python, you can use RPy2 to access the functionalities offered by R. In effect, you will have the power of two different languages in one. Since most of the companies have production systems ready for this language, Python is always production-ready. Even if you feel like learning R after learning RPy2, it is pretty easy to do. But moving to Python after R is relatively much difficult. If you are already too deep in R, ignore everything and focus on it.

How do you balance Machine Learning theory and practice?

Posted on by Imarticus Learning

Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications,  the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice 
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn’t feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don’t be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.

What are good ideas for Hackathon in Machine Learning?

Posted on by Imarticus Learning

 

Hackathons are not merely fetes where you can show off your skills but are also huge opportunities aimed at engaging gainfully and celebrating solving business issues and problems.

The Indian hackathons are corporate sponsored glitz-and-swag events where developers can compete and push boundaries by tackling industry-relevant issues in an environment that is supportive, has fireside learning, exposure to the latest gadgetry and quite like a convoluted career fair.

Job opportunities, internships, different vertical exposure, startup offers, mentorships, peer interactions, rights to brag and prizes abound. For the ML starters, it can be the pitch to learn on, join a community, hone skills, get ideas, and find the right tools and projects in coding, discover the best training and even get placed.

Take your pick from popular and reputed hackathons like MachineHack, TechGig, Hackerearth, Kaggle, and OpenML. Here are some hackathon ideas that can be advantageously used.

  1. Reach out to the online community through online ongoing hackathons where tech and ML beginners can participate, work on third-party APIs and resolutions and learn from the community. Alexa is being tweaked by Amazon in this manner.
  2. Permit multiple categories, levels, and submissions: Teams can participate in multiple category hackathons as individuals or by submitting multiple solutions at hackathons. This builds team spirit, allows multiple submissions in various categories and promotes working in communicative teams.
  3. A balanced cross-functional team yields better results: This secret in hackathons helps teams compete better, ensures better team coordination, provides a platform for the newcomers to work with the experienced and definitely satisfies learning for the whole team. Go for the prize with your team!
  4. Count results to be superior to techniques:  All hackathon participants should use their stack wisely and well, and showcase in the prototype their algorithm and skills in programming. Many a failure occurs with developing scalable models instead of the prototype, using complex databases, algorithms, and a limited stack to develop the prototype in the specified timeframe.
  5. Hackathons are about prizes for quality problems: Nobody expects a complete solution. What does impress is a simple tweak, innovation or prototype that has the potential to solve and provide a scalable solution?
  6. The product demo is essential: While the presentation has a bearing on the success and winning becomes addictive the product demo is crucial as it sums up the learning, efforts, and technology used. The real winners are those who compete and learn from their mistakes.
  7. Follow the hackathon code: The opportunity to learn should not cause problems for others. Follow the guidelines and conduct codes to provide a supportive environment for all.
  8. Exploit the learning opportunity: To break into the ML field you will need to do a machine learning course and get practical machine learning training with a reputed institute like Imarticus. Then move on to hackathons because these are events akin to sprints where hardware/software is tweaked over the next 24 to 48 hours. The skills and tasks are graded and provide participants with the chance to come up with quick solutions without needing code understanding. Do explore the workshops and 101 sessions on coding to help pick-up the requisite skills.

On a concluding note, there will be various platforms hosting events and hackathons both offline and online which provide participants for everyone.

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

Posted on by Imarticus Learning

Python is essential for those looking to get into machine learning and artificial intelligence. It is one of the easiest languages to learn and its range of dynamic semantics is unparalleled. It is easy to read and has reduced the cost of program maintenance. Artificial intelligence allows computers and software to ‘learn’ and identify patterns in order to predict outcomes and make conclusions without human interference or supervision. An example of this is the auto-reply feature on Gmail which ‘reads’ emails and predicts the reply. A machine learning engineer develops intelligent algorithms using data that has to be collected, assembled, and arranged first.
Learning Python is not just important, it is essential to machine learning and AI. There are several courses available online where you can get a Python certification and you should pick one that suits your level of expertise. If you are an absolute beginner, you should choose a course that will help you master the basics of Python. You will also learn how to use popular scientific libraries that support Python users.
The next step involves learning about Python in the scientific computing environment. As a machine learning engineer, one of your main tasks will be to work with large amounts of data. Python allows for intricate statistical modeling of said data. It works well with other programs and tools and allows for a wide range of interaction across different players.
An important area with Python learning is classification. Engineers have to be able to develop a model that classifies, identifies, and describes data classes in order to be able to classify unknown data in the future. It is one of the main forms of supervised learning and is an essential tool in your development of AI. Different types of classifier models include support vector machines, logistic regression, neural networks, and decision trees.
Regression is just as useful as classification and it also is an important form of supervised learning. However, unlike classification where there are distinct finite classes, regression works with predicting continuous numerical data.
When you are faced with data that does not have pre-defined classes, then your best tool is clustering. Simply put, clustering puts together data that are similar and separates the ones that differ. This type of data pooling is a form of unsupervised learning.
One of the best ways to learn the different aspects of Python is to learn by doing. There are several places online where you can practice your knowledge. You can also connect with other engineers and programmers and join a community to discuss and learn from others. Kaggle exercises and competitions are recommended to beginners who are looking for a challenge to flex their theoretical skills.
For those who are serious about machine learning, joining a reputed machine learning course will set you on the right path. The right machine learning training is intensive and allows you to learn hands-on with live projects. However, it is still recommended that you have some previous knowledge about Python, math, and statistics before venturing into these intensive courses.