In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.
This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.
Some points apply to every course of this kind; see how we rank.
DeepLearning.AI is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (90/100). Andrew Ng's courses.
Instructor: Dennis Freeman, Kendra Pugh This course provides an integrated introduction to electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. The lecture videos provide an overview of each topic, whi…
This playlist contains video recordings of Stanford's graduate-level CS329A course. It covers the latest techniques and applications of AI agents that can continuously improve themselves through interaction with themselves and the environment. The lecture recordings start with self-improvement tech…
The field of Computer Science is headed for a major upheaval with the rise of large AI models, such as ChatGPT, that are capable of performing general-purpose reasoning and problem solving. We are headed for a future in which it will no longer be necessary to write computer programs. Rather, I beli…
Harvard University · edX
This introduction to moral and political philosophy is one of the most popular courses taught at Harvard College.
Instructor: Jon Gruber, 14.01 students View the complete course: http://ocw.mit.edu/14-01SCF10 This course includes a full set of lecture videos and a selection of problem solving videos. In the lecture videos, Professor Jonathan Gruber covers the principles of microeconomics conceptually, mathemat…
This course provides a thorough introduction to the principles and methods of physics for students who have good preparation in physics and mathematics. Emphasis is placed on problem solving and quantitative reasoning. This course covers Newtonian mechanics, special relativity, gravitation, thermod…
