Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
If you want a job in Machine Learning, you will probably have to learn all these languages at some point. Python has a lot of libraries. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need it to implement mappers and reducers.
Problem analysis is the process of understanding real-world problems and user's needs and proposing solutions to meet those needs. The goal of problem analysis is to gain a clearer picture and a better understanding, before development begins, of the problem being solved.
Continuous learning means we’re keeping the “raw material pile” of our brain freshly stocked, which enables us to come up with more and better ideas — which every business needs today. New ideas and solutions are a primary way you can add value to your job, and therefore increase your success.
Statistical analysis often uses probability distributions, and the two topics are often studied together. However, probability theory contains much that is mostly of mathematical interest and not directly relevant to statistics. Moreover, many topics in statistics are independent of probability theory.
Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient decent, convex optimization, lagrange, quadratic programming, partial differential equations and alike.
Conceptual thinking is the ability to understand a situation or problem by identifying patterns or connections, and addressing key underlying issues. It includes the integration of issues and factors into a conceptual framework. Conceptual thinking helps understand why something is being done.