Designing Our Artificial Intelligence Curriculum

We’ve expanded our Artificial Intelligence Nanodegree programs! Find the right program for you.
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Udacity Artificial Intelligence Nanodegree program

Now that we’re preparing to close the inaugural round of applications for our new Artificial Intelligence Nanodegree programs, we are quietly but excitedly settling down to the work of rolling out this incredible curriculum for our incoming students. In this post, we’re going to look at the origin story of this program’s curriculum. The first step in preparing a comprehensive AI program is to understand the relevance and trajectory of the field itself.

AI Today, AI Tomorrow

One thing is unequivocally clear: There has never been a better time to study Artificial Intelligence. Demand for smarter solutions to problems big and small—combined with increasing access to high performance computing and an abundance of rich data sources—means AI is poised to seismically impact the world of computing for the better.

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How Companies Are Using Kaggle To Find The Best Machine Learning Talent

Become a Machine Learning Engineer.

Udacity Kaggle Machine Learning Talent

The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it. This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how machine learning can be applied. It also includes an increasing number of machine learning enthusiasts with atypical backgrounds who are joining the conversation, bringing in diverse experiences and points of view.

Discovering and Attracting Machine Learning Talent

The increasingly symbiotic relationship between companies that need machine learning expertise, and data science competition platforms like Kaggle, has greatly impacted how rapid advancement is being achieved. This relationship has also changed the hiring landscape. Companies today face ever-increasing pressure to innovate in order to remain competitive, and they are pursuing comparatively unconventional means for discovering and attracting new talent in order to maintain their edge. The need for machine learning talent is so great, that companies are looking far further afield than once they might have.

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How To Prepare For A Machine Learning Interview

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Machine Learning Interview

Getting ready for a job interview has been likened to everything from preparing for battle, to gearing up to ask someone out on a date, to lining up a putt on the 18th green at The Masters. Meaning, at best, it’s nerve-racking, and at worse, it’s terrifying! Preparing for a Machine Learning interview is no different. You know you’ve got something ahead with the potential to be either really great, or really terrible. But how do you ensure your result is the great one?

It’s all about mindset, and preparation.

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Women In Machine Learning: Katie Malone

Find the right Nanodegree program for you in Artificial Intelligence, Machine Learning, or Data Science

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The excitement around our Machine Learning Nanodegree program has been amazing to witness, and the vitality and dynamism in the space right now is pretty incredible. There are so many fascinating storylines in the world of Machine Learning, it’s sometimes hard to even know what to focus on. But unquestionably, the people working in this field—those individuals at the cutting-edge of these new technologies—are a critical part of the Machine Learning narrative. One of the things I find personally really exciting is how many women are shaping the future of Machine Learning. My former colleague Katie Malone is a wonderful example of this, and I’m very grateful she was able to take some time recently to talk Machine Learning with us!

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Languages and Libraries for Machine Learning

Find the right nanodegree program for you.
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In our previous post 5 Skills You Need to Become a Machine Learning Engineer, we identified the key skills you need to succeed in this field. Now, we’re going to address one of the most common questions that comes up from students interested in Machine Learning: Which programming language(s) do I need to know?

The answer may surprise you. It doesn’t really matter!

As long as you’re familiar with the Machine Learning libraries and tools available in your chosen language, the language itself isn’t as important. A variety of Machine Learning libraries are available in different programming languages. Depending on your role within a company, and the task you’re trying to accomplish, certain languages, libraries and tools can be more effective than others.

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5 Skills You Need to Become a Machine Learning Engineer

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Interested in Machine Learning? You are not alone! More people are getting interested in Machine Learning every day. In fact, you’d be hard pressed to find a field generating more buzz these days than this one.  Machine Learning’s inroads into our collective consciousness have captured our imaginations, as when AlphaGo won 4 of 5 Go matches against the world’s best Go player. If you are interested in learning machine learning skills to enter this field, your moment is now. 

That said, it’s one thing to get interested in Machine Learning, it’s another thing altogether to actually start working in the field. This post will help you understand both the overall mindset and the specific skills you’ll need to start working as a Machine Learning engineer.

What is a Machine Learning Engineer?

To begin, there are two very important things that you should understand if you’re considering a career as a Machine Learning engineer. First, it’s not a “pure” academic role. You don’t necessarily have to have a research or academic background. Second, it’s not enough to have either software engineering or data science experience. You ideally need both.

It’s also critical to understand the differences between a Data Analyst, Data Scientist and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story, and to produce actionable insights for members of your team. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. The “audience” for your output is human. As a Machine Learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning. A Data Scientist lives somewhere between these two worlds. They must have the software engineering skills to collect, clean, and organize data to analyze, and use machine learning to extract insights. Their communication skills are also vital to their success. 

Understanding The Ecosystem

Before getting into specific skills, there is one more concept to address. Being a Machine Learning engineer necessitates understanding the entire ecosystem that you’re designing for.

Let’s say you’re working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use. In a Data Analysis model, you could collect the purchase data, do the analysis to figure out trends, and then propose strategies. The Machine Learning approach would be to write an automated coupon generation system. But what does it take to write that system, and have it work? You have to understand the whole ecosystem—inventory, catalog, pricing, purchase orders, bill generation, Point of Sale software, CRM software, etc.

Ultimately, the process is less about understanding Machine Learning algorithms—or when and how to apply them—and more about understanding the systemic interrelationships, and writing working software that will successfully integrate and interface. Remember, Machine Learning output is actually working software!

Now, let’s get into the real details of what it takes to be a Machine Learning engineer. We’re going to break this into two primary sections: Summary of Skills, and Languages and Libraries. We’ll begin with the Summary of Skills here, then in a follow up post we’ll address Languages and Libraries for Machine Learning.

Please subscribe to our blog to receive our follow up post on Languages and Libraries for Machine Learning in your inbox!

Summary of Skills

1. Computer Science Fundamentals and Programming

Computer science fundamentals important for Machine Learning engineers include data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.).

You must be able to apply, implement, adapt or address them (as appropriate) when programming. Practice problems, coding competitions and hackathons are a great way to hone your skills.

2. Probability and Statistics

A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many Machine Learning algorithms; these are a means to deal with uncertainty in the real world. Closely related to this is the field of statistics, which provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data. Many Machine Learning algorithms are essentially extensions of statistical modeling procedures.

3. Data Modeling and Evaluation

Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.). A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.). Iterative learning algorithms often directly utilize resulting errors to tweak the model (e.g. backpropagation for neural networks), so understanding these measures is very important even for just applying standard algorithms.

4. Applying Machine Learning Algorithms and Libraries

Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning. You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.). Data science and Machine Learning challenges such as those on Kaggle are a great way to get exposed to different kinds of problems and their nuances.

5. Software Engineering and System Design

At the end of the day, a Machine Learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality and maintainability.

Machine Learning Job Roles

Jobs related to Machine Learning are growing rapidly as companies try to get the most out of emerging technologies. The chart below depicts the relative importance of core skills for these general types of roles, with a typical Data Analyst role for comparison.

ML Graph

Relative importance of core skills for different Machine Learning job roles (click to enlarge)

The Future of Machine Learning

What is perhaps most compelling about Machine Learning is its seemingly limitless applicability. There are already so many fields being impacted by Machine Learning, including education, finance, computer science, and more. There are also virtually NO fields to which Machine Learning doesn’t apply. In some cases, Machine Learning techniques are in fact desperately needed. Healthcare is an obvious example. Machine Learning techniques are already being applied to critical arenas within the Healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis. David Sontag, an assistant professor at New York University’s Courant Institute of Mathematical Sciences and NYU’s Center for Data Science, gave a talk on Machine Learning and the Healthcare system, in which he discussed “how machine learning has the potential to change health care across the industry, from enabling the next-generation electronic health record to population-level risk stratification from health insurance claims.”

The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems. If this is YOUR future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.

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