Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Stability and Capability in Quality Improvement
In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability.
-
Course by
-
Self Paced
-
10 hours
-
English
Math behind Moneyball
Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
-
Course by
-
Self Paced
-
65 hours
-
English
Research Methodologies
This course focuses on research methodologies. In this vein, the focus will be placed on qualitative and quantitative research methodologies, sampling approaches, and primary and secondary data collection. The course begins with a discussion on qualitative research approaches, looking at focus groups, personal interviews, ethnography, case studies and action research. We will also discuss quantitative research methods with a focus on experimental research design and survey methodology.
-
Course by
-
Self Paced
-
20 hours
-
English
Uncertainty and Research
This course teaches the fundamentals of scientific research. We approach the research process as a means of systematically reducing uncertainty and demonstrate how conducting a scientific investigation can be posed as an exercise in Bayesian uncertainty quantification. We begin by exploring the scientific landscape to understand the different types of research, where they are conducted, how they are supported, and why each of these types of research is important. We then formalize scientific inquiry and the scientific method and elaborate the research process and its scientific merits.
-
Course by
-
Self Paced
-
15 hours
-
English
Machine Learning Algorithms
In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
-
Course by
-
Self Paced
-
16 hours
-
English
Statistics for Machine Learning for Investment Professionals
One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary).
-
Course by
-
Self Paced
-
18 hours
-
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.
-
Course by
-
Self Paced
-
15 hours
-
English
Bayesian Statistics: Time Series Analysis
This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models. Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
-
Course by
-
Self Paced
-
22 hours
-
English
AI Workflow: Feature Engineering and Bias Detection
This is the third 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. Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. Class imbalances can seriously affect the validity of your
-
Course by
-
Self Paced
-
12 hours
-
English
頑想學概率：機率二 (Probability (2))
這是一個機率的入門課程，著重的是教授機率基本概念。另外我們的作業將搭配臺大電機系所開發的多人競技線上遊戲方式，讓同學在遊戲中快樂的學習，快速培養同學們對於機率的洞察力與應用能力。
-
Course by
-
Self Paced
-
Chinese
Pricing Options with Mathematical Models
This is an introductory course on options and other financial derivatives, and their applications to risk management. We will start with defining derivatives and options, continue with discrete-time, binomial tree models, and then develop continuous-time, Brownian Motion models. A basic introduction to Stochastic, Ito Calculus will be given. The benchmark model will be the Black-Scholes-Merton pricing model, but we will also discuss more general models, such as stochastic volatility models.
-
Course by
-
Self Paced
-
69 hours
-
English
Bayesian Statistics: From Concept to Data Analysis
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions.
-
Course by
-
Self Paced
-
12 hours
-
English
Probabilistic Deep Learning with TensorFlow 2
Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets.
-
Course by
-
Self Paced
-
53 hours
-
English
Introduction to Software Testing
After completing this course, you will have an understanding of the fundamental principles and processes of software testing. You will have actively created test cases and run them using an automated testing tool.
-
Course by
-
Self Paced
-
30 hours
-
English
A Crash Course in Causality: Inferring Causal Effects from Observational Data
We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2.
-
Course by
-
Self Paced
-
18 hours
-
English
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data.
-
Course by
-
Self Paced
-
14 hours
-
English
Improving your statistical inferences
This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power.
-
Course by
-
Self Paced
-
28 hours
-
English
Six Sigma Advanced Define and Measure Phases
This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance.
-
Course by
-
Self Paced
-
15 hours
-
English
Basic Statistics
Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance).
-
Course by
-
Self Paced
-
27 hours
-
English
Understanding and Visualizing Data with Python
In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data.
-
Course by
-
Self Paced
-
21 hours
-
English
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. Some exercises require the use of R, a freely-available statistical software package.
-
Course by
-
Self Paced
-
22 hours
-
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.
-
Course by
-
Self Paced
-
27 hours
-
English
Introduction to Probability and Data with R
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project.
-
Course by
-
Self Paced
-
14 hours
-
English
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it?
-
Course by
-
Self Paced
-
24 hours
-
English
Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers.
-
Course by
-
Self Paced
-
21 hours
-
English