Behaviorism[ edit ] This theoretical framework was developed in the early 20th century based on animal learning experiments by Ivan PavlovEdward ThorndikeEdward C. TolmanClark L. Hulland B. Many psychologists used these results to develop theories of human learning, but modern educators generally see behaviorism as one aspect of a holistic synthesis.
Deep learning has become standard in the tech industry, achieving state-of-the-art results in computer vision, natural language processing, and artificial intelligence.
TensorFlow provides the flexibility needed to implement and research cutting edge architectures while allowing users to focus on the structure of their models as opposed to mathematical minutiae.
Course Details Minimum Skill Level - Basic statistics - Basic linear algebra matrix multiplication, transposing matrices - Basic calculus derivatives, summations - Programming: Python preferred, but those comfortable with another language should be able to learn the material Prep Work There will be a pre-course workshop to refresh students on the requisite linear algebra and calculus techniques.
Students learn how data science is done in the wild, including data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation.
Students will use the Python scientific stack to work through examples that illustrate all of these concepts, with real-life use cases. Concurrently, students will learn some of the statistical and mathematical foundations that power the data scientific approach to problem solving.
The practice of data science involves both a collection of skills and a mindset for tackling data-intensive problems or problems looking in need of data-intensive solutions. Working through this course will give students the tools and necessary background to think about datasets that they encounter in meaningful ways, and will provide enough knowledge to continue their own data science learning in a vast, exciting, and rapidly evolving field.
This course is intended for people with a basic understanding of data analysis techniques, and those who are interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way.
They want to glean actionable, data-driven insights from that data. A familiarity with some programming language is helpful but unnecessary if the pre-work for the course is completed. No prior advanced mathematical training beyond an introductory statistics course is necessary.
Course Details Minimum Skill Level Students should have some familiarity with basic statistical and linear algebraic concepts.
In Python, it will be helpful to know basic data structures what distinguishes them. This course provides an overview of the core principles of machine learning using a hands-on, project-based curriculum.
There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data. Prerequisites Firm knowledge of the Python programming environment. There will not be any introductory Python material in this course. Students should not take this course if they are not comfortable coding in Python.
Basic calculus and linear algebra is helpful but not required e.
A quick refresher on linear algebra and basic calculus will be provided where necessary. Knowledge of statistics is not required for this course.
Outcomes Upon completion of the Machine Learning course, students will have:This is the second course in the Academic English: Writing specialization. By introducing you to three types of academic essays, this course will especially help prepare you for work in college classes, but anyone who wants to improve his or her writing skills can benefit from this course.
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The first lesson in this module introduces the Effective Communication specialization, the capstone project, and the Business Writing course.
You'll meet the writing instructor, Dr. Quentin McAndrew, and her counterparts Dave Underwood and Professor William Kuskin, who teach Graphic Design and Successful Presentation.
DOCUMENTATION and PUBLICATIONS Professional Genealogical Sources How to document and publish genealogy and family history; listing of scholarly Internet tools, resources and examples. Recent years have seen a dramatic increase in online education.
According to a February report, % of leaders at academic institutions say that online education is a "critical component of their long-term strategy." Many organizations have begun experimenting with Massive Open Online Courses, or MOOCs, as a new way to offer educational services.
Open data is the idea that some data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control.
The goals of the open data movement are similar to those of other "open" movements such as open source, open hardware, open content, open education, open educational resources, open government, open knowledge.