Machine Learning has been reshaping and transforming the businesses since its inception. We as an industry leader are not only aware of its benefits but also helping companies to make informed decisions. If you are looking for machine learning projects for your projects, have a look at top 10 machine learning development companies in Seattle.
Zazz is a team of creative designers and developers building great digital products in Seattle and San Francisco. Our collective experience in the technology industry includes mobile app development, IOT application development, blockchain development with a design first approach to product development.
Appstudio is a full service Mobile App Design & Development Company offering services in Native iOS Development (Swift 3.0), Native Android Development (Java), React Native Development & Unity Game Development. They have collaborated with Fortune 500 companies, Startups and Mid Sized firms across a spectrum of industries, ranging from Health Care & Finance to On-Demand Services, to create Mobile apps that are actively being used by Millions of users across the globe.
We are Europe’s largest Python software house. We have over 200 Python, JS, React Native and full-stack developers ready to supercharge your project with extraordinary code, the Agile way.
Intellias was founded in 2002 and by 2019 has evolved into a 1,600+ people strong supplier of software development services, with a core delivery base in Ukraine, development office in Poland and local presence in Germany. We have been delivering solutions to Fortune 500 companies and helping leading technology innovators build their software products in a variety of domains.
Fayrix provides world-class custom & offshore software development. Relying on 14 years of experience and a talented team of 1500+ IT professionals including 700+ developers, we are ready to execute software development projects of any scale. We provide to our clients flexible terms, competitive rates & different models of partnership - time & materials, dedicated teams or project-based full outsourcing.
We are Neoteric and we help startups and enterprises build successful software ventures providing end-to-end product development services, extending our partners with product teams and implementing AI.
We combine analytical and consultancy skills with a full-cycle software development to create individually tailored solutions that meet your business needs. Being innovation-driven, we also provide services in area of Artificial Intelligence, and Deep Learning, and develop solutions for the commercial application of these technologies.
MobiDev provides custom software development. Main areas of expertise: ➤ Web development ➤ Mobile development (iOS/Android and cross-platform) ➤ Augmented Reality ➤ IoT & Hardware Integration ➤ Artificial Intelligence. Data Science & Machine Learning ➤ Microservices & Cloud infrastructure
Altoros is a 400+ person strong consultancy that helps Global 2000 organizations with the methodology, training, technology building blocks, and end-to-end solution development required to support digital transformation at scale. We turn cloud-native app development, customer analytics, blockchain, and artificial intelligence into products with a sustainable competitive advantage.
Netguru builds digital products that let people do things differently. As a company, we deliver digital products for top startups, Fortune500 companies, and well-known brands to help them solve real problems through software and product design. Our clients have changed the way people do banking, listen to music, learn languages and rent bikes. Their products have been featured in TechCrunch, Business Insider and Product Hunt.
Machine learning – the branch of artificial intelligence – is changing not only the way we interact with machines but also how we interact with the environment around us. In the last decade, machine learning has given us cars that drive by themselves, voice recognition, effective web searches, personalized recommendations, and a vast understanding of the human genome. The term “machine learning” dates back to 1959 when Arthur Samuel, an IBM researcher defined it as “the ability (for computers) to learn without being explicitly programmed,” and this field encompasses a variety of mathematical techniques where computers learn and redefine their solutions based on a simple “training” of data. But how does machine learning work?
Computers were originally designed to follow algorithms. An algorithm is simply a series of stages encoded in a computer language. Expert computer programmers would contact process experts to plan business operations in flowcharts which would then be implemented as computer programs. A flowchart explicitly positions the tasks that must be performed, in the order in which they should be performed, along with any decision that needs to be made along the way. Flowcharts are excellent for modeling repetitive and predictable processes where decisions are made on unambiguous data. These systems were created to be decisive.
But not all processes follow clear and immutable rules, and many real-world decisions do not lead to a single unambiguous answer. Machine learning systems are probabilistic: tasks are executed and decisions are made on incomplete information and the results are assigned probabilities of being correct. Machine learning is suitable for problems that involve classification (dividing objects between two or more classes) regression (discovering relationships between variables) and grouping (grouping objects with similar characteristics). Which brings us to certain uses such as:
- Objects in real scenarios
- Facial Identities or Expressions
- Spoken language
- Extract knowledge:
- Of free format, audio or video texts
- Email spam detection
- Unusual sequences of financial transactions
- Unusual patterns of sensor readings
- Make predictions:
- Future value of the shares or exchange rates
- What movies will a person like?
Many mathematical techniques support machine learning, the most important are these:
Linear and polynomial regression
Regression deals with modeling the relationship between numerical variables that is iteratively refined using an error measure in the predictions made by the model. The basic assumption is that the output variable (a numerical value) can be expressed as a combination (weighted sum) of a set of numerical output variables.
These tree-shaped flowcharts use ramifications to illustrate any possible outcome of a decision. Many of these tree diagrams use binary branching (two options) based on current values or data attributes. For large volumes of data, many of these multiple decision trees can be created, which together form a consensus decision on the results. Decision trees can be used for classification and regression problems.
This concept is inspired by the way the nervous system works, as well as the brain to process information. A large number of highly interconnected processing elements work in unison to solve specific problems, usually of classification or pattern matching. Each neuron “votes” on the outcome of the decision, which could urge other neurons to vote, so the votes are counted creating a classification of the results depending on the support each has received.
These graphic structures, also known as a belief network, are used to represent knowledge about an uncertain domain. The graph is a probabilistic map of causes and effects where each node represents a random variable, while the edges between the nodes represent probabilistic dependencies. For example “red sky at night” could lead to a 75% chance of “good weather.” These conditional dependencies are frequently estimated using statistical and computational methods.
They are mathematical systems that go from one “state” (situation or group of values) to another: it is assumed that future states depend only on the present state and not on the sequence of events that precede it. For example, if you make a model of the Markov chain about the behavior of a baby, you could include “play”, “eat”, “cry” and “sleep” as states, which together with other behaviors could form a “space of states ”which would be a list of all possible states. Additionally, a Markov chain tells you what is the probability of jumping or “transcending” from one state to another, for example the probability that a baby that is playing falls asleep in the next five minutes without crying before.
The plethora of algorithms available for machine learning has restricted this technology to those companies with the experience required to select the right tool for the project at hand. For machine learning to be more widely adopted, these technologies need to be simplified and delivered as a service. This is the goal of the IBM Watson machine learning service.
Created on Apache Spark, Watson machine learning intelligently and automatically builds models using open machine learning libraries and the most understandable algorithm groups in the industry. Its patented cognitive assistance for data science technology marks each machine learning algorithm against the data provided to recommend the best solution for the need.