How to Build a Successful Machine Learning Team?

How to Build a Successful Machine Learning Team?

Build-a-Successful-Machine-Learning-Team

 

Machine learning has become the tech scene influencer right now, and you only have to look at how much the companies of all sizes are investing in this technology to see how vital a role it’s going to play in our future lives – both personally and professionally. As per one of the Gartner’s prediction, “By the year 2020, consumers will manage 85% of their relationships with organizations without interacting with humans”. With 20% of the C-Suite already using machine learning, businesses are planning to grow their teams with Machine Learning experts. But an excellent ML team isn’t just about the engineers; it’s a different combination of talents and perspectives. If you’re one of those planning to build a successful Machine Learning team, here we will help you grow your organization.

 

Standard Job Roles within Machine Learning Teams

 

Before creating a team, you need to know the current job roles that are best suited in a Machine Learning team. Usually, Machine Learning teams consist of engineers, scientists, analysts, and managers. Below we have listed down the individual responsibilities of each team member.

 

  • Data Engineers – Data engineers build and maintain the “big data” infrastructure needed for data modeling, predictions, and analysis that is later verified by data scientists.
  • Data Scientists – Data scientists are analytical data experts with an ability to solve complex problems using data-driven techniques. They take specifications from product leads to understanding the business objective.  They are mainly responsible for gathering massive amounts of disorderly data and changing it into a more usable format.
  • Data Analysts –Analysts are responsible for monitoring processes and production model performance, plus evaluating data quality.
  • Machine Learning Engineers – Having a background and skills in applied and data science, and intense coding, these experts execute the operations of an ML project and are responsible for running the data pipelines and infrastructure needed to conduct code to production.

 

Now, if you are trying to build a great Machine Learning team, you need to understand the necessary skills required. The ideal ML team members should be proficient in understanding the wide range of algorithms and applied mathematics, plus they must have analytical and problem-solving skills. Along with this, in-depth knowledge of some statistics and programming languages is a must. We will further explain the list of skill sets required. But before that, you must understand that knowing some programming languages isn’t enough.

A Machine Learning expert should understand how to build end-to-end machine learning solutions to existing problems. That means along with the curation of data; they need to absorb it, explore it, and cleanse it. Besides, they need to train and assess it, iterate on it, and then correctly execute it. Only then, they can claim to be the masters of Machine Learning.

When you hire member in your machine learning team, it’s essential that you know who can do research for ML and who can apply it to your business challenges. For a stronger team, hire brilliant programmers who can make use of existing libraries and frameworks, but can overcome inherently ambiguous data science.

 

Here Is a List of Primary Skill Sets Required

Here-Is-a-List-of-Primary-Skill-Sets-Required

1. Python/C++/R/Java: In a Machine Learning team, the members need to learn all these programming languages. Python and C++ help in speeding up the code, whereas R is necessary for statistics and plot. Besides, Hadoop is Java-based, so you may need to apply mappers and reducers in Java.

2. Probability and Statistics: Theories are essential for learning about algorithms. Some best examples are; Gaussian Mixture Model, Naive Bayes, and Hidden Markov Models. The team members need to firmly understand the Probability and Statistics to grasp these algorithmic models.

3. Algorithms and Applied Mathematics: Standard implementation of ML algorithms and knowing its working can help you discriminate supervised learning models. For that, you need to thoroughly study subjects such as convex optimization, gradient descent, partial differential equations, Lagrange’s theorem, quadratic programming, and more.

4. Distributed Computing: Usually, ML teams require working with large data sets. They can’t operate this data using a single machine, so it should be distributed across the whole cluster. To make the process easier, you can use Amazon’s EC2 and Apache Hadoop.

5. Expertise in Unix Tools: Your team members must have the knowledge in Unix tools such as grep, cat, fund, sort, se, tr, cut, head, tail and more. Since all the processing is on a Linux-based machine, professionals must have access to all these tools. Hence, it’s essential to learn the functions and use those.

 

Added Skills

 

Just having this technical expertise isn’t enough to make your team a successful one. It must stay up-to-date with the imminent transformations. That means the members should be well aware of the news about development tools, theories, and algorithms. For this, they can read papers like Google File System, Google Bigtable, and MapReduce plus several online books for ML.

 

Conclusion

 

We hope the above points help you build a greabout creating data science solution for a specified business problem. However, hiring Machine Learning experts can be costly and requires a lot of work. As the demand is more and resources arat Machine Learning team that takes your project to the next level. The best team members not only know the techniques to develop models and extract data from insights but also have the comprehension e less.

To overcome these challenges, several online marketplaces like RemotePanda are providing a cost-effective solution. We, at RemotePanda, help you conveniently connect with Machine Learning contractors. Hire from our wide range of resource base who can help you successfully build your next critical ML project.

 

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Machine Learning Interview: Questions Curated for You at One Place

Machine Learning Interview: Questions Curated for You at One Place

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1. What is the use of a training set, a validation set, and a test set? What is the difference between a validation set and a test set?

 

In Machine Learning, there are three separate sets of data when training a model:

 

Training Set: this data set is used to adjust the weights on the ML model.

 

Validation Set: this data set is used to minimize overfitting. You are not adjusting the weights of the model with this data set, you are just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been shown to the model before, or at least the model hasn’t trained on it (i.e. validation data set). If the accuracy over the training data set increases, but the accuracy over the validation data set stays the same or decreases, then you are overfitting your ML model, and you should stop training.

 

Testing Set: this data set is used only for testing the final solution in order to confirm the actual predictive power of the model.

 

Difference between a validation set and a test set

 

The validation data set is a set of data for the function you want to learn, which you are not directly using to train the network. You are training the network with a set of data which you call the training data set. If you are using a gradient-based algorithm to train the model, the error surface and the gradient at some point will entirely depend on the training data set thus the training data set is being directly used to adjust the weights. To make sure you don’t overfit the model, you need to input the validation dataset to the model and check if the error is within some range. Because the validation set is not using directly to adjust the weights of the network, therefore it’s a good error for the validation. Also, the test set indicates that the model predicts well for the train set examples, also it is expected to perform well when the new example is presented to the model which was not used in the training process.  Once a model is selected based on the validation set, the test set data is applied to the network model, and the error for this set is found. This error is a representative of the error which we can expect from absolutely new data for the same problem.

 

2. What is stratified cross-validation and where is it used?

 

Cross-validation is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is a prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a stratified variant of this approach, the random samples are generated in such a way that the mean response value (i.e., the dependent variable in the regression) is equal in the training and testing sets. This is particularly useful if the responses are dichotomous with an unbalanced representation of the two response values in the data.


Stratified cross-validation can be used in the following scenarios:

 

A dataset with multiple categories. When the dataset is smaller and categories are imbalanced, this is when stratified cross-validation will be used.

A dataset with data of different distributions. When we can’t ensure that both types of dataset are present in training and validation, we will have to use stratified cross-validation.

 

3. Why are ensembles typically considered better than individual models?

 

Ensemble models have been used extensively in credit scoring applications and other areas because they are considered to be more stable and, more importantly, predict better than single classifiers. They are also known to reduce model bias and variance.  However, Individual classifiers pursue different objectives to develop a (single) classification model. Statistical methods either estimate (+|x) directly (e.g., logistic regression), or estimate class-conditional probabilities (x|y), which they then convert into posterior probabilities using Bayes rule (e.g., discriminant analysis). Semi-parametric methods, such as NN or SVM, operate in a similar manner, but support different functional forms and require the modeler to select one specification a priori. The parameters of the resulting model are estimated using nonlinear optimization. Tree-based methods recursively partition a data set so as to separate good and bad loans through a sequence of tests (e.g., is loan amount > threshold). This produces a set of rules that facilitate assessing new loan applications. Moreover, Ensemble classifiers pool the predictions of multiple base models. Much empirical and theoretical evidence has shown that model combination increases predictive accuracy. Ensemble learners create base models in an independent or dependent manner.

 

4. What is regularization? Give some examples of the techniques?

 

Regularization is the process of adding information in order to solve an ill-posed problem or to prevent overfitting.

 

Some techniques of regularization:

 

L1 and L2 are the most common types of regularization. These update the general cost function by adding another term known as the regularization term.

 

Cost function = Loss (say, binary cross entropy) + Regularization term

 

Due to the addition of this regularization term, the values of weight matrices decrease because it assumes that a neural network with smaller weight matrices leads to simpler models. Therefore, it will also reduce overfitting to quite an extent.

 

 

5. What is the curse of dimensionality? How to deal with it?

 

The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience.

 

Dimensionality reduction is an important technique to overcome the curse of dimensionality in data science and machine learning. As the number of predictors (or dimensions or features) in the dataset increase, it becomes computationally more expensive (ie. increased storage space, longer computation time) and exponentially more difficult to produce accurate predictions in classification or regression models. Moreover, it is hard to wrap our head around to visualize the data points in more than 3 dimensions.

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6. What is an imbalanced dataset? How to overcome its challenges?

 

Imbalanced datasets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed of two classes: The majority (negative) class and the minority (positive) class. These type of sets suppose a new challenging problem for Data Mining since standard classification algorithms usually consider a balanced training set and this supposes a bias towards the majority class.

 

Ways to overcome the Imbalanced dataset challenges
1. Data Level approach: Resampling Techniques

2. Algorithmic Ensemble Techniques

 

7. What is the difference between supervised, unsupervised, and reinforcement learning?

 

Here is the difference


In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.

 

Semi-supervised learning takes a middle ground. It uses a small amount of labeled data bolstering a larger set of unlabeled data. And reinforcement learning trains an algorithm with a reward system, providing feedback when an artificial intelligence agent performs the best action in a particular situation.

 

8. What are some factors determining the success and recent rise of deep learning?

 

Here are some of the success factors of deep learning:
1. Gnarly data

2. Built-in feature engineering

3. Topology design process

4. Adoption of GPUs

5. Availability of purpose-built open source libraries

 

 

9. What is data augmentation? Provide some examples?

 

Data augmentation adds value to base data by adding information derived from internal and external sources within an enterprise. Data is one of the core assets for an enterprise, making data management essential. Data augmentation can be applied to any form of data but may be especially useful for customer data, sales patterns, product sales, where additional information can help provide more in-depth insight.

 

Computer vision is one of the fields where data augmentation can be used. We can do various modification with the images:

 

  • Resize
  • Flipping
  • Rotate
  • Add noise
  • Deform
  • Modify colors

 

10. What are convolutional neural networks? What are its applications?

 

A convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery.

 

Applications

1. Image recognition

2. Video analysis

3. Natural Language Processing (NLP)

4. Drug discovery

5. Health risk assessment

6. Checker games

 

 

Mobile Advertising Trends In 2019

Mobile Advertising Trends In 2019

We have observed a rapid advancement in technology in the past couple of years. These advancements have rendered traditional marketing strategies a little less convenient and have given rise to new and enhanced digital marketing strategies.

Mobile advertising has been one of the most widely used marketing strategies, and why shouldn’t it be? Gone are the days of sticking fliers on the trees, painting benches at bus stops with the advertisement, it’s time to be omnipresent, anyone with a mobile device should have a taste of your brand.

Year after year we have seen a new trend in advertising coming to life, let’s see what emerging technology will give rise to in 2019.

01. Augmented Reality/Virtual Reality

William Arthur Ward once said, “If you can imagine it, you can achieve it.” 
It’s not just a quote anymore, AR/VR has given us enough power to bring our imagination to life.

People only remember those ads which were interactive enough to hold their attention.
Current media puts a constraint on the amount of interaction one can have with an ad, but, AR/VR has surpassed all those limits and has provided users with interactive ads that go way beyond the traditional ads.

Anything can be put up for a display now- be it your new furniture or your next holiday. This is just the beginning, who knows, by 2025 you could see an ad on VR and place your order right from there, or have real-time notifications popping up and you’d have the option to choose what to do with them, it could be the best replacement for facetime, integrating VR with your video calls, holographic projections as real and interactive as if the person was standing right in front of you.

After Apple launched its ARKit last year in WWDC 2017, leading voice, video and broadcasting platform Agora.io has managed to integrate it with their platform to create an augmented reality video conference system.
Microsoft’s hololens has been able to bring holograms to life, this lets people interact with each other in real time and can watch and work on objects in 3D.

02. Artificial Intelligence & Machine Learning

There are specific times when your target audience is most active and the amount of engagement with your ads at this point is at its peak.

Advertisers need to analyze data to figure out this peak time when the user interaction is maximum in order to capture their attention.

Now the problem that every marketer faces is that there is a mountain of data and there is comparatively lesser time to analyze it and narrow it down on the demographics, their preferred time to watch and engage with the ads.

This is where AI/ML comes into the picture, it does all the work of keeping a track of user’s interaction with your ads, keywords they use and accordingly predict what’s the best time to display ads that will get maximum user interactions. ML can work wonders by analyzing all the data. This, in turn, helps to start predicting the best times to display an ad and help run optimized marketing campaigns.

Using data predictions from AI/ML, marketers can only direct those ads to user’s newsfeeds which are of interest to them. For instance, when we listen to songs on music streaming sites, we get suggestions of similar songs based on our search history.

AI/ML might just give rise to artificial entrepreneurs, they might be able to observe and identify a market opportunity and come up with various strategies to satisfy the market need.

MediaGamma an AI technology company which uses Machine Learning and Data Science to help digital clients understand user behavior, won a grant from the UK government’s innovation agency to develop AI that can generate text and images for the targeted ads.

“We could have a banner ad specifically tailored to a person’s tastes,” — Wang, co-founder of MediaGamma mentioned at the event.

Our favorite online streaming platform Netflix has been showing the users what they need to see, it uses AI to predict it’s users choice and recommend shows accordingly. No wonder they have got everyone hooked on to their platform.
AI/ML is going to ease a lot many things for all the marketers and advertisers.

03. Chatbot

We are living in an era where we text more than we speak, but we can’t have someone on the phone or sitting on a computer 24/7 to have a conversation with everyone.

We have a better solution, Chatbots! They are taking over customer service.

Let’s face it, customer service is one of the most important aspects of a user’s journey. Better the service, loyal the customers.

All a customer really wants is a direct line between their problems and their solutions.

Marketers can use Chatbots to their advantage as a communication channel to solve customer issues, promoting events and offers and many more things.

Chatbots aren’t just limited to customer service, utility bots can be used to order takeouts, placing orders, or book a hotel room or an entire trip.

It’s totally up to you what functionality you would like your chatbot to have and what customer needs you want to cater to.

Imagine if your brand could talk, what would you like it to say? And most importantly, how would you like to say it? As cool as Chuck Norris, right? That’s what you can develop your chatbot to be.

If you’re a coffee fanatic, you already must be aware of the chatbot Starbucks has been using in its app. It has made it incredibly easy to order your favorite snack or coffee with the exact cost and time it will take to prepare your order.

Staples has developed a Facebook messenger bot in partnership with IBM’s Watson which makes it convenient for the customers to order, track and return their packages, you can even check if the item of your interest is in stock or not.

04. GDPR

Users really like to experience something that is personalized for them, but what they also value more than anything is the privacy of their data.

The authorities in the European Union established the General Data Protection Regulation for protecting users from data breaches, identity theft and other forms of cybercrime.

Mobile marketers usually have a global audience, and if some of your audience hails from one of the 28 countries that make up the European Union, then you need to comply with the GDPR.

Marketers need to be completely transparent about how they are going to use the data they are gathering from their users, they need to get users’ consent before they even begin to gather data, no more pre-checked boxes.

If your company faces any kind of a data breach, it’s your responsibility to notify your customers about it.

05. Internet Of Things

IoT is a blessing for marketers who can come up with strategies to target connected devices to give their consumers a seamless experience. IoT gives marketers a chance to build their brand, voice their opinions at the right time and at the right place.

IoT can be used by marketers for real-time interaction, gathering insights about customers journey, their buying behavior. A lot of data can be gathered and analyzed to predict customers’ experience and buying preferences.

And since all of the devices will be connected, marketers won’t have to put much efforts in deciding which device to promote or engage the customer on, whichever device seems the best for a particular ad, the user would see it on that particular device.

One of the best examples of IoT is Amazon’s Alexa powered Echo.

You can get a feel of what it’s like to be Tony stark living with Jarvis. Your very own personal assistant, who take voice commands from you, play music and even order food.

Conclusion

These emerging trends can work wonders if utilized properly, marketers need to think about how they can use this immense power to their advantage and lead their business or company towards rapid growth. Uncle Ben was right all along, “with great powers, comes great responsibility”.