We are living in interesting times, and there’s no doubt about it. I mean who in their right mind have thought that humans and machines would coexist in a way that humans won’t even be able to tell how machines are making their lives easier? Today, machines are outperforming humans in every aspect. They have altered our ways of interacting with other humans, as well as, other machines as well.

Now, that being said, we are in no way under robot invasion right out of a sci-fi movie. Rather the machines that surround us are created and programmed by humans. Yes, today we will be talking about the machine learning engineers; the humans who program and bring life to the machines to perform their own. A machine learning engineer is responsible to program the machines, so it is able to experience, work, and even feel like us.

Now, with so much potential, machine learning engineers are one of the most in-demand professionals in the IT field. According to one report from Jobs and AI Anxiety, which looks at the future of work, and the influence of technology in the job market, the machine learning engineers will be the most sought after professionals. This is in line with the other findings of the report that details that over 30% of the managers reported the use of Artificial Intelligence (AI) and Machine Learning (ML) in their companies, whereas, the other 53% responded that they are looking to incorporate the technology in near future.

Looking at the stats, it is safe to claim that the future belongs to the machine learning engineers and as more businesses and industries explore the potential returns of investing in these disruptive technologies including ML and AI, they can rest assured for a bright and highly in-demand job market.

With such a bright potential, we know many youngsters would be looking to get more insights into the profession and how much do machine engineers make. So, in this blog we have come up with all the relevant details that will help fresh machine learning engineers, as well as, aspiring youngsters to know all about the potential of the profession and how much do machine learning engineers make.

So, let’s start exploring the potential of the profession.

What to Expect in a Machine Learning Engineer Job Description

The machine learning engineer salary will depend directly on his/her job description and roles for which he/she is hired. Now, since it’s still early days for machine learning and there aren’t machine learning engineers available in the industry, thereby, the job description of machine learning engineers will typically resemble that of data scientists, except that there will be a strong emphasis on programming skills.

Contrarily, the job description may also look out for professional programmers and coders with good skills in data management and analysis. Again, the machine learning engineer salary will depend on the job description and the role (junior, senior) he will be hired for.

Here is some prerequisite job description for machine learning engineer:

-  Advanced degree (Graduate, Masters) in computer science, mathematics or statistics

- Skills in data modeling and architecture

- Proven programming skills in multiple languages preferably in Python, R or Java

- Basic understanding and experience working with different machine learning frameworks like Keras or TensorFlow

- Advance mathematics skills

- Good communication skills

Machine Learning Engineer Salary in 2020

Since it’s still a fairly young job title with not enough data point to accurately give the range for machine learning engineer salary, we need to make some predictions based out on the salaries from data scientists and software engineers. It’s the right approach to predict how much do machine engineers make because machine learning engineer job description falls within the scope of these two fields. So, here’s a basic structure for machine learning engineer salary in 2020 against various positions:

Big data engineer: The salary would average at $163,250

Data architect: The salary would average at $141,250

Data scientist: The salary would average at $125,250

Data modeler: The salary would average at $101,750

Developer/analyst: The salary would average at $108,500

Software engineer: The salary would average at $125,750

What Does a Machine Learning Engineer Do?

If we are to speak broadly, the job of machine learning engineers is quite similar to some of the other roles in the data science industry like AI engineers, and data scientists. All of these positions involve working with the massive amount of data, needs a strong and polished data management skills, as well as, the ability to develop complex models to solve the problems using the massive data available to them.

However, with these three generic roles, the similarities come to end between machine learning engineers and data scientists. That’s because while data scientists will be developing critical and high-value insights from the data in form of reports or charts with human readability, machine learning engineers will be working to develop complex algorithms and models to enable machines to take over the predictive modeling. They will be using the massive data at their disposal to train the algorithms for specific tasks (that’s where the machine learning stems from), and once the model is trained it will be able to perform the tasks itself without the need for human intervention.

If you haven’t already versed with our routine interaction with a machine learning algorithm, the “recommendation algorithm” used in almost all e-commerce stores including Amazon, eBay, Netflix, and others is the simplest and brightest example of how machine learning algorithms are able to predict our preferences based on our profile and previous interaction with it. Every time you buy or even search a product on these sites, you add data to your profiles, which is then analyzed and processed by the “recommendation algorithm” to give you the most suited recommendations for more products or films you may like to watch. Interestingly, just like humans, the machine learning algorithms don’t stop learning and with each interaction, it continues to improve its accuracy and content value.

Machine learning is quite closely associated with other disruptive data technologies like Artificial Intelligence and Deep Learning. It actually can be said as a subfield of Artificial Intelligence, which is aimed to create intelligent and learning machines. On the other hand, deep learning is also a subfield of artificial intelligence, which is concerned with creating Artificial Neural Networks (ANNs), which are able to understand, analyze and solve multi-layers data sets. Virtual assistants, Chatbots, and driverless cars are some of the practical examples of deep learning.

How to Sharpen Your Saw in Machine Learning Engineer World (Update your Resume)

If we are to analyze the resumes of machine learning engineering aspirants, the most common weak part is the programming experience. This means that if you are seeking a job title of machine learning engineer, you got to have extensive programming experience in multiple languages. Python is especially the most important and most widely used programming language for developing machine learning algorithms, which is why you should get hands-on experience with Python programming. And since it is almost the default programming language for machine learning algorithms (not the default), there are various robust and intensive Python tutorials available online that can help you get experienced with Python language. The other important language for machine learning in R, which is also widely used in the industry. Both Python and R are also relatively easy languages, so you won’t have much difficulty in learning those languages. Other relevant languages for machine learning include Java and/or C++.

Apart from programming skills, you would also like to develop strong skills in dealing with massive data sets. One of the easiest ways to build your data management and analysis skills is to join Kaggle; a Google initiative to help data science aspirants learn the profession by working on data science projects. There are a variety of datasets available at Kaggle, shared by data scientists and machine learning experts from around the world; making it truly a great resource to polish up machine learning skills.

Also, you may wish to take up machine learning courses offered by Amazon – the tech giant and creator of Alex (virtual assistant). Amazon offers a high-value machine learning and certification course which is offered with four pathways including data platform engineer, business decision-maker, and developer and data scientists. Not only these are really great and enriched resources to learn machine learning, but the best part is that Amazon is offered all these courses for free.

Machine Learning Salary for Fresher or Drop Out

When we talk about machine learning fresher, it basically employees with a machine learning engineer with 0 to 4 years of experience. This would typically be a college graduate who has worked around at different companies in data science or machine learning role.

The machine learning engineer salary for fresher will vary broadly depending upon the role, job description, location, and the company. However, the average salary for a machine learning engineer is $97,000 approximately. Couple this with the bonuses and profit-sharing offered by companies and this can rise quickly to as much as $130.000.

Here are some skills that can improve your paycheck as a machine learning engineer:

C++ Programming Language

Python

Image Processing

Big Data Analytics

Natural Language Processing

Computer Vision

Data Analysis

Deep Learning

Software Development

Folio3 is Your Best Machine Learning Tech Partner

At Folio3 we bring extensive experience and an enhanced understanding of machine learning needs of customers. We have helped hundreds of businesses develop successful Machine Learning as a service solution. We have successfully developed customized machine learning solutions from ATM cash forecasting to Clinical Decision Support System (CDSS), establishing our dominance and authority as a reliable and capable machine learning Service Company.

Why Machine Learning Engineers are in Demand

As we said above, machine learning is all about leveraging the data to create intelligent machines. And if we are to look at the business models today, it’s highly dependent on data management and analytics. From clients’ interaction to internal systems and processes, businesses need to manage and analyze data effectively to gain a competitive edge over competitors. And in this quest, machine learning is the aptest technology that can help them leverage the full potential of the company’s data.

Here are some of the applications of Machine Learning in the practical business world:

Image and Speech Recognition

Machine learning assists businesses to implement automated systems and procedures to convert unstructured data into useful information like auto-tagging of images, speech to text and text to speech converter, and many other tasks.

Customers’ Insight

as we discussed above, the machine learning algorithms are able to learn the preferences of customers through their previous interactions with the business, thereby, giving them valid, highly-relevant, and targeted recommendations for personalized services.

Risk management and Fraud Prevention

The ability of machine learning algorithms to analyze and process a massive amount of historic data make them perfectly apt to analyze historical records of banks and other financial institutions to make accurate financial predictions, as well as, spot any fraudulent activity in real-time.

How much do machine learning engineers make in California?

The machine learning engineer salary greatly varies depending upon the location of the company. New York and California remain two of the top cities offered the highest salaries for machine learning engineers.

According to statistics, machine learning engineers are offered a 24.4% higher salary in California, as compared to the National average.

How much do Facebook machine learning engineers make?

Facebook is a tech giant and a major player when it comes to the implementation of machine learning algorithms. The social media giant hires hundreds of machine learning engineers with good payout. On average, a machine learning engineer at Facebook takes out $123,395 salary. The salaries can range from $15,000 to $188,000.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Deep learning and machine learning is big today. People are looking out for ways to get their hands with this futuristic technology. However, one common question that is frequently asked by newbies and learners is how many images are required for deep learning classification?

Honestly, I hate to say but there is no one clear answer to this question.

I mean, back in days I remember when Google Brain was in initial phases, the tech giant used over 15,000 images to train the algorithms, however, today this may be done with few hundred images or even less.

The right answer to this question is that it will depend on many factors including the complexity of the problem and your algorithm.

So, while I can’t answer the question of “how much images you may need for deep learning classification”, I can certainly give you some of the ways to think the deal with your question.

In this blog, I have come up with some steps that can help you to come up with the right number of images you may need for deep learning classification.

So, let’s dive into it.

What’s the Reason Behind this Question? 

You may not realize but to be able to get an answer for the size of the training dataset you may need, it is important to know why you want to know this, as your answer may influence the next steps.

For example:

You may have large data.

If this is your answer than you may consider developing some learning curves or may decide to go with a large data framework to be able to use the maximum amount of data.

You may have very scarce data

If that’s your reason for the question, you may want to consider some other options to collect data, or may opt for data augmentation methods, which can help you to artificially enlarge the size of the training data

You haven’t started collecting data, in this case, you may want to collect some data initially and see if that’s enough for the algorithms. In case of data collection is expensive for you, talk to a domain expert for a specific answer.

Ok, now let’s see how many images you may need for deep learning classifications.

1) It Depends

As I said earlier, there is no one size fit all model in this regard, so I doubt there would be anyone who can give a generic answer without understanding the specific predictive modeling problem.

The answer to this question would have to be found by you only through empirical investigation. Some of the factors that may influence the amount of data required for training purpose include:

The complexity of the problem; and

The complexity of the algorithm

And this will be your starting point.

2) Reason by Analogy

Thousands and thousands of programmers and data scientists have worked on the deep learning models before you and many of them have published their studies, which in most cases are available for reading free.

So, before you may ask anyone else the size of the dataset required for training purposes, it is better to go through the similar studies that have been done previously to get a better estimate for the amount of training data you may require for the classification.

Also, many studies have been done to estimate the optimum performance scale for algorithms with respect to the size of the training data. Such studies can greatly help you to predict the right amount of data required for specific algorithms.

In fact, you may want to average the findings of multiple studies to get a good estimation.

3) Use Domain Expertise

For training the algorithm, you would need to chunk out a sample data representative of the problem, which you are looking to solve.

Now, it is important to remember that you want the algorithm to learn the function to be able to map input data to output data. Now, the learning performance of the algorithm for the mapping function will depend on the quality and quantity of the learning data you input in the model.

This also means that to train your model to higher performance levels, you would need to have large training data to help the model to understand all different relationships that may exist in the data and to be able to learn them and map.

For this reason, you may want to consult the domain expert (for the problem you are trying to solve) to understand all possible functionalities, features, and relationships that would be required to be learned by the model and to completely understand the complexity of the problem.

4) Statistical Heuristic

If you haven’t heard of the Statistical Heuristic methods, then these are the models that can be used to estimate the appropriate training data size.

The best part about the Statistical Heuristic methods is that the majority of these are designed for the classification problems, so it may come handy in your case as well. While some of the Statistical Heuristic is comprehensive and robust, others may at best be defined as ad hoc.

5) Dataset Size vs Model Proficiency

It’s not uncommon in data science to demonstrate the performance scale of algorithms against the size of the training data, or the complexity of the problem.

Unfortunately, not all of these studies are published and available for review purposes, or those that are published may not relate well with the type of problem you are looking to solve.

For that reason, I often suggest aspiring data scientists continue with the available data with all the data that’s available to you and any available learning algorithm.

Basically, you would be performing your own study to determine the performance scale of the model against the size of the training data.

The results of the study may be plotted with the model skills on the y-axis and size of the training data on the x-axis. This will give you a fair estimate of how the size of the data will affect the model skills in your specific problem.

The plotted graph will be called a “learning curve”.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Machine learning (ML) is a subfield of the Artificial Intelligence (AI), which give machines the ability to learn and adopt from their experienced and enhance their ability to complete any specific tasks assigned to them. The most prevailing example of machine learning is the “people who bought XXX also saw XXXX”, which you must have seen across all ecommerce stores.

What is Machine Learning?

The term machine learning was coined by Arthur Samuel, in the year 1959. Arthur Samuel is also considered as the pioneer of Artificial Intelligence. According to him, machine learning is a field of study that enables computers to adapt and learn for themselves without any explicit need for programming. Obviously, this was the beginning of what we are seeing today, but machine learning as we know it today is pretty much what Arthur defined way back in the 1950s.

In this blog, we will be covering all aspects of machine learning including the working of machine learning, and machine learning process steps. We will also be looking at how does machine learning to work in today’s world, as well as, define some of the popular machine learning techniques used widely in different industries. last but not the least, we will also be looking at the best programming languages for machine learning, while finally rounding up our blog by summarizing the working of machine learning.

As said earlier, machine learning is a subfield of artificial intelligence. In the most basic terms, the machine learning algorithms are meant to create intelligent programs that are able to get trained for specific tasks by themselves and learn better ways to complete the tasks faster and with precision. It’s kind of similar like creating algorithms that replicate the human mind, with the ability to learn, adapt, and make intelligent decisions. Such algorithms are developed to minimize the need for human intervention. For the most part, the learning process of the machine is completed through supervised or unsupervised training with a large volume of the training dataset. The quality and volume of the data used to train machines are directly related to the preciseness of the machine learning models. Machine learning models can be developed for explicit tasks, where automation is desired.

How does Machine Learning work in today`s world?

Model, parameters, and the learners are three fundamental blocks of any machine learning model.

The model represents the system that is responsible to make forecasts/predictions. 

Parameters are the characteristics which are considered by the model to make forecasts/predictions.

The learner component of a machine learning model enables models to make learn and make necessary adjustments required to make accurate forecasts/predictions.

Now, to help you better understand how does machine learning work, we can consider an example. In this scenario, let’s consider that you have two different drinks; beer and wine. Now, you want machine learning algorithms to differentiate between the two drinks based on some fixed parameters which in this case would be the color and percentage of the alcohol found in each drink.

Here are the machine learning process steps that will complete the given task for you:

Learning from the training set

The first and foremost step for any machine learning model is to feed the model with a structured and large volume of data for training. In this case, you would require several (possible hundreds) samples of beer and wine with defined color and alcohol percentage. Now, you will feed the training data into the model and classify each of the samples as per their defined parameters. For instance, you would define the percentage of alcohol in samples of wine against the percentage of alcohol in samples of beers. Similarly, you will classify the other defined parameter that is ‘color’ for samples of wine and beet.

For easy representation, you may define the ‘color’ as parameter ‘X’ and alcohol percentage as parameter ‘Y’. Now in this case the (X, Y) will be the defined parameters of the training dataset for the model and will help the algorithm to adapt and learn differentiation between each of the drinks.

Measuring Error

The next logical step in our “how does machine learning work” is to measure the errors and discrepancies in the results of the model. For this step, you will input a fresh dataset (different from the training data) and the outcome of this step could be either one of these four:

- True Positive: this is the most ideal scenario where the machine learning model is able to predict each type of drink correctly

True Negative: in this scenario, the model misses out on classification of drinks when it is present.

False Positive: in this scenario, the model wrongly classify drinks when it isn’t present.

False Negative: in this scenario, the machine learning model isn’t able to classify drinks when it is present.

To estimate the total error from the model, you will use the sum of FP and FN

Manage Noise

Now, while to help you understand “how does the machine learning work” better, we have kept the set of defined parameters limited to only two. However, in real-world scenarios, there may be hundreds and thousands of parameters that have to be defined in the training data to enable machine learning models to classify the items precisely.

In such scenarios, there will be a large number of errors estimated in the second step. These errors will be sourced from the noise present in the training data. Noise in this case represents the unwanted anomalies that deviate the standards of the defined parameters; thus weakening the learning process of the model. The noise in training data may be present for various reasons including:

Large volume of the training data.

Unstructured training data.

Data labeling errors.

Overlooked attributes that influence the classification.

Testing and Generalization

In many cases, the machine learning algorithm fits perfectly with training data, however, it fails to produce results when a fresh dataset is an input to the model (other than the training data). This is why it is important to evaluate the fitness of the algorithm to the new/fresh dataset. This is achieved by giving new/fresh data as input and analyzing the results produced by the algorithm. The generalization in this case refers to the fitness of the model to make predictions for a fresh dataset.

How Supervised Machine Learning Works?

Supervised machine learning is one of the popular machine learning techniques. In this case, the model takes training data with known responses to the output to learn and build its capacity to make predictions for a new/fresh dataset. Supervised machine learning techniques should be used where users have a high-volume of structured data for training the model. In this case, the higher the volume and quality of the data, the better and more precise results can be expected from machine learning models. The supervised learning model makes prediction models based on regression and classification techniques.

Classification techniques 

The classification technique used by supervised machine learning models delivers discrete responses. For example, the model will simply inform if an email is a spam or genuine (you experience it in your email inbox). In classification techniques, the input data is classified into the defined categories. This technique is widely used in medical imaging, image processes, and speech recognition.

You can use classification technique if you have a structured, tagged, or categorized data which is divided into discrete classes or groups. For instance, the technique is used in handwriting recognition, where it is capable of classifying the handwriting based on the recognition of letters and numbers style. Unsupervised pattern recognition is widely used in image processes and computer vision to identify objects and image segmentation.

Some of the widely used supervised learning algorithms in the industry include Neural networks, support vector machine (SVM), K-nearest neighbor, logistical regression, and more.

Regression techniques 

The regression techniques forecast continuous responses. An example of these includes predicting the temperature changes or fluctuations in power demand. The regression techniques are used in algorithmic trading, energy load forecasting among others.

The regression techniques for classification can be used when the input data is in the form of a continuous range, or real numbers. Some of the regression algorithms include stepwise regression, linear regression models, non-linear regression models, adaptive neuro-fuzzy learning, and others.

How Unsupervised Machine Learning Works?

Unsupervised machine learning is used to identify hidden patterns and structures in data to draw inferences.

Clustering 

Clustering is one of the most widely used unsupervised learning techniques, which is used for exploratory data analysis to identify hidden patterns and structures in data. The technique is used in different applications including gene sequence analytics, object recognition, image processing, and others.

An example case study for the clustering technique is identifying the optimum location of building a cell phone tower for a telecommunication company. In this case, the unsupervised machine learning algorithm can be used to identify clusters of users in different areas who rely on cell phone towers. Since a cell phone may only be connected to a single tower at a time, the clustering algorithm can process the dataset and come up with the most suitable cell tower placement design to optimized signal reception for users.

Some of the common algorithms used to perform clustering include; fuzzy c-means clustering, K-means, Gaussian mixture model, and others.

Which Programming Language is best for Machine Learning?

Now that we are over the machine learning process steps and working of machine learning, let’s see which the best programming language for machine learning is.

This is rather a simple question, as Python is undoubted the most suited programming language that is also most widely used to develop machine learning applications. We will come to the advantages and strengths of Python as the best programming language for machine learning algorithms a little later. Some of the other popular languages that are used to develop machine learning applications include R, C++, Shell, Java, and others.

What makes Python the ideal choice for machine learning applications development is the simplicity and readability of the language, as compared to the other languages. This is important because developing machine learning algorithms is already inherently difficult with various complexities concepts like calculus and others involved in the process, which required a lot of time and effort to develop. The simplistic Python language is able to share some burden of the developers and ML engineers. There are various Python tutorials freely available online that can be checked to understand the basics of the language. Another great advantage of Python programming language for use in machine learning algorithms is its various pre-built libraries for machine learning. The language comes with various packages that can be used directly for various applications. Some of the Python packages widely used in machine learning applications development include:

Numpy, Scikit (used widely for image processing)

Numpy, Scikit, and NLTK (great when working with text)

Librosa (Audio applications)

Matplotlib, Scikit, and Seaborn (used for data representation)

TensorFlow, Pytorch (used for deep learning applications)

Pandas (used for high-level structured data analysis)

The language gives ML engineers and developers an option to choose between scripting or object-oriented programming. Moreover, the changes can be easily implemented without having to recompile the code.

Folio3 is Your Best Machine Learning Tech Partner

Folio3 is one of the leading machine learning application development companies in the world. With decades of experience in the development of high-tech and complex machine learning applications, Folio3 has partnered with some of the biggest companies from different industries and help them to initiate their digitalization process with robust customized solutions with advanced machine learning capabilities.

How Does Folio3 Machine Learning Solution Work?

Breast Cancer HER2 Subtype Identification

Folio3 partnered with one of the oldest public sector medical universities and hospital in Pakistan, Dow University of Health Sciences to develop a robust computer-aided assistance system with cell segmentation and spot counting capabilities to enable medical practitioners and doctors to perform faster and more precise breast cancer tests. The powerful computer-aided system was able to digitize and store the images for further analysis and processing.

Converse Smartly®- Speech to Text Software

Converse Smartly (CS) is a hugely successful in-house project of the company that was developed to establish the capabilities and expertise of Folio3 ML engineers in the fields of machine learning and Natural Language Processing.

Converse Smartly (CS) is an advance and robust speech to text application that uses state-of-the-art technologies including IBM Watson API, Amazon AWS, Microsoft Azure, Python’s Natural Language Toolkit, and Google Speech-to-Text API to deliver precise and outstanding results.

ATM Cash Forecasting

For this project, Folio3 partnered with one of the largest commercial banks in Pakistan, which is also a multination bank to develop an ATM cash forecasting system. The prediction model was able to forecast t4eh cash-flow management for over 2000 ATMs of the bank in Pakistan and globally. Based on the unique requirements of the bank, we developed a sophisticated and innovative predictive solution that increased the ATM management profits of the bank by up to 6%.

how does google assistant machine learning work?

Google Assistant is a personal assistant that leverage on the image recognition, NLP, and Google knowledge graph to converse with the users. It’s much like a personalized chatbot that using natural language processing to interact with the users and come up with the answers to users’ questions.

How does AI work with machine learning?

Machine learning is a subfield of a much broader Artificial Intelligence (AI) technology, which is meant to enable machines to execute tasks smartly. Machine Learning on its own is about developing intelligent algorithms for devices that can learn, adapt and execute tasks through their learned experiences.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188