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Our Courses
AI Workflow: Business Priorities and Data Ingestion
This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and ma
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Course by
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Self Paced
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8 hours
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English
Fundamentals of Machine Learning in Finance
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.
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Course by
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Self Paced
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18 hours
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English
Coaching Practices
In order for coaching to be most effective, it’s important that there is strong culture of coaching and accountability which you will learn how to incorporate into your one-on-one meetings in this course. We’ll discuss strategies in coaching great employees who are highly motivated, consistent performers, and poor performers. We’ll explore specific tools, such as a coaching agenda, you can employ immediately in your coaching conversations. You will learn how to use the "Coaching Algebra" technique in typical performance scenarios managers encounter.
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Course by
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Self Paced
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19 hours
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English
AI Workflow: Data Analysis and Hypothesis Testing
This is the second course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA). Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work. You will learn techniques of estimation
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Course by
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Self Paced
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11 hours
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English
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used.
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Course by
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Self Paced
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9 hours
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English
Optimizing Machine Learning Performance
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model.
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Course by
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Self Paced
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12 hours
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English
Physics 101 - Energy and Momentum
This second course serves as an introduction to the physics of mechanical energy and momentum. Upon completion, learners will understand how mathematical laws and conservation principles describe the motions and interactions of objects around us. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This first course covers Energy, Translational Momentum, Collisions, Statics, and Elasticity.
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Course by
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Self Paced
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23 hours
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English
Physics 102 - Magnetic Fields and Faraday's Law
This third course serves as an introduction to the physics of electricity and magnetism. Upon completion, learners will understand how mathematical laws and conservation principles describe fields and how these fields are related to electrical circuits. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This third course covers Magnetic Fields and Faraday's Law of Induction.
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Course by
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Self Paced
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26 hours
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English
Mathematics for Machine Learning: PCA
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces.
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Course by
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Self Paced
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21 hours
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English
Physics 101 - Forces and Kinematics
This first course serves as an introduction to the physics of force and motion. Upon completion, learners will understand how mathematical laws and conservation principles describe the motions and interactions of objects around us. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This first course covers 1D Kinematics, 2D Kinematics, and Newton's Laws. Each of the three modules contains reading links to a free textbook, complete video lectures, conceptual quizzes, and a set of homework problems.
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Course by
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Self Paced
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30 hours
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English
Motion Planning for Self-Driving Cars
Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws.
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Course by
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Self Paced
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32 hours
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English
Discrete Mathematics
Discrete mathematics forms the mathematical foundation of computer and information science. It is also a fascinating subject in itself. Learners will become familiar with a broad range of mathematical objects like sets, functions, relations, graphs, that are omnipresent in computer science. Perhaps more importantly, they will reach a certain level of mathematical maturity - being able to understand formal statements and their proofs; coming up with rigorous proofs themselves; and coming up with interesting results. This course attempts to be rigorous without being overly formal.
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Course by
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Self Paced
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42 hours
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English
Advanced Linear Models for Data Science 2: Statistical Linear Models
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.
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Course by
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Self Paced
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6 hours
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English
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models.
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Course by
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Self Paced
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21 hours
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English
Physics 102 - Electric Charges and Fields
This first course serves as an introduction to the physics of electricity and magnetism. Upon completion, learners will understand how mathematical laws and conservation principles describe fields and how these fields are related to electrical circuits. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This first course covers Charge, Electric Forces, and Electric Fields.
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Course by
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Self Paced
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22 hours
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English
Mathematical Foundations for Cryptography
Welcome to Course 2 of Introduction to Applied Cryptography. In this course, you will be introduced to basic mathematical principles and functions that form the foundation for cryptographic and cryptanalysis methods. These principles and functions will be helpful in understanding symmetric and asymmetric cryptographic methods examined in Course 3 and Course 4. These topics should prove especially useful to you if you are new to cybersecurity. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Course by
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Self Paced
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15 hours
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English
VLSI CAD Part II: Layout
You should complete the VLSI CAD Part I: Logic course before beginning this course. A modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design.
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Course by
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Self Paced
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24 hours
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English
Physics 102 - AC Circuits and Maxwell's Equations
This fourth course serves as an introduction to the physics of electricity and magnetism. Upon completion, learners will understand how mathematical laws and conservation principles describe fields and how these fields are related to electrical circuits. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This fourth course covers AC circuits, Impedance, and Magnetism.
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Course by
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Self Paced
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18 hours
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English
CUDA Advanced Libraries
This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
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Course by
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Self Paced
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25 hours
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English
The Finite Element Method for Problems in Physics
This course is an introduction to the finite element method as applicable to a range of problems in physics and engineering sciences. The treatment is mathematical, but only for the purpose of clarifying the formulation. The emphasis is on coding up the formulations in a modern, open-source environment that can be expanded to other applications, subsequently. The course includes about 45 hours of lectures covering the material I normally teach in an introductory graduate class at University of Michigan.
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Course by
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Self Paced
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62 hours
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English
Matrix Algebra for Engineers
This course is all about matrices, and concisely covers the linear algebra that an engineer should know. The mathematics in this course is presented at the level of an advanced high school student, but it is recommended that students take this course after completing a university-level single variable calculus course. There are no derivatives or integrals involved, but students are expected to have a basic level of mathematical maturity.
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Course by
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Self Paced
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20 hours
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English
Natural Language Processing with Attention Models
In Course 4 of the Natural Language Processing Specialization, you will: a) Translate complete English sentences into Portuguese using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, and created tools to translate languages and summarize text! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning fra
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Course by
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Self Paced
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35 hours
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English
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
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31 hours
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English
Model Thinking
We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers?
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Course by
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Self Paced
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27 hours
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English
State Estimation and Localization for Self-Driving Cars
Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car.
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Course by
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Self Paced
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27 hours
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English