AI News, Machine Learning FAQ

Machine Learning FAQ

This program is also excellent for Data Analysts who want to move into a more machine learning centric role because this program focuses specifically on building real world skills that you will be able to apply to your Machine Learning Engineer job.

The goal of the Machine Learning Nanodegree program is to equip you with key skills that will prepare you to fill roles within companies seeking machine learning experts as well as those looking to introduce machine learning techniques to their organizations.

Machine Learning’s inroads into our collective consciousness have been both history making (as when AlphaGo won 4 of 5 Go matches against the world’s best Go player!) and hysterical (Machine Learning Algorithm Identifies Tweets Sent Under The Influence Of Alcohol), but regardless how you discovered it, one thing is clear: Machine Learning has arrived.

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.

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.

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.

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.).

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.).

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.

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 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.”

Regardless of your previous experience or skills, there exists a path for you to pursue a career in data science.

Specifically, myself and my team have worked with industry leaders to identify a core set of eight data science competencies you should develop.

Programming SkillsNo matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade.

This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach.

Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments.

Linear AlgebraUnderstanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.

This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.

CommunicationVisualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions.

It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.

At some point during the interview process, you’ll probably be asked about some high level problem—for example, about a test the company may want to run, or a data-driven product it may want to develop.

Become a Machine Learning Engineer

This program is also excellent for Data Analysts who want to move into a more machine learning centric role because this program focuses specifically on building real world skills that you will be able to apply to your Machine Learning Engineer job.

The goal of the Machine Learning Nanodegree program is to equip you with key skills that will prepare you to fill roles within companies seeking machine learning experts as well as those looking to introduce machine learning techniques to their organizations.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

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