Introduction to Python programming for data analysis. Learn pandas, numpy, and visualization libraries for data science.
This program focuses on designing, planning, and operating intelligent and integrated energy systems. It covers the integration of renewable energy sources, energy storage, electric vehicles, and the use of AI and machine learning for digitalization and optimization of the grid.
A comprehensive data science program that covers R programming, data visualization, probability, inference, and machine learning. The machine learning section includes classification algorithms and case studies.
This professional certificate program is designed for a broad audience and focuses on the practical application of AI in organizations. It covers various AI solutions like machine learning and natural language processing, providing context for where few-shot and zero-shot learning can be applied.
Learn to use Power Query in Excel for data extraction, transformation, and loading, which are essential skills for data cleaning.
An introductory online course that guides government professionals through designing AI-based services for public administration, emphasizing technical, governance, ethical, and regulatory aspects.
A Harvard University course that teaches the use of causal diagrams (DAGs) to represent assumptions, understand biases, and guide data analysis for causal inference.
This course covers the fundamentals of convex optimization and approximation methods.
An introductory course that covers the data science process, including data acquisition, cleaning, and transformation, using tools like R and Python.
This HarvardX course covers central concepts of statistical inference and modeling, including how to perform inference on high-dimensional data.
Part of Harvard's Data Science Professional Certificate, this course covers the fundamentals of probability theory needed for a data science career.
The first course in the HarvardX Data Science Professional Certificate, it provides the foundational R programming skills necessary for data wrangling and exploration.
Part of the HarvardX Data Science Professional Certificate, this course covers the basics of data visualization and exploratory data analysis using ggplot2 in R.
This course from Microsoft on edX covers the essential mathematical foundations for machine learning and AI using Python.
A course that provides a solid foundation in optimization theory and algorithms.
This MIT course develops a deep understanding of the principles of statistical inference, including estimation, hypothesis testing, and prediction, on firm mathematical grounds.
An in-depth introduction to machine learning, covering topics from linear models to deep learning. The syllabus includes on-line algorithms and support vector machines, with practical implementation in Python projects.
Learn about various optimization algorithms and their applications in machine learning and data analysis.
An MIT course that provides a foundational understanding of probability models, including random processes and the basic elements of statistical inference.
This course provides an introduction to robotics, covering the core techniques for representing robots that perform physical tasks in the real world.
A Harvard University course that covers statistical concepts and models relevant for causal inference in the context of high-throughput experiments.
Another course in Harvard's Data Science Professional Certificate that introduces the basics of statistical inference using the R programming language.
This course from the University of Edinburgh introduces the fundamental concepts of statistics. You will learn about data collection, analysis, and interpretation. The course covers topics such as descriptive statistics, probability, and inference.
This course provides a deep dive into the theory of supervised learning, and then applies this theory to practical problems using Python.
A 3-week course to grow generative AI expertise by focusing on customizing, optimizing, and automating AI solutions using Amazon Bedrock.
This course covers the fundamentals of unsupervised learning techniques such as clustering and dimensionality reduction to make sense of large, unlabeled datasets. You will learn to implement k-means and hierarchical clustering.
This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence. It delves into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course covers topics including graph search algorithms, reinforcement learning, and neural networks.
This course teaches the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning, and deep learning to solve real-life problems. Students will learn the basics of several machine learning topics to help solve real-life challenges, including unsupervised learning techniques such as clustering and dimensionality reduction.
A course that explores the transformative potential of AI in educational contexts. It covers the fundamentals of AI, its applications in teaching and learning, and the ethical considerations that educators need to be aware of.
This course from MIT provides an introduction to computational thinking and data science. You will learn how to use computation to solve problems and explore data, which is a great foundation for machine learning.
This course focuses on the principles of data-driven decision-making. You will learn about data exploration, statistical inference, and how to build and interpret predictive models, including regression.
This program is designed for business leaders who want to understand the fundamentals of data science and how to apply it in their organizations. It covers key concepts, including machine learning and regression.
This course from the University of London provides an in-depth look at the role of data science and AI in human resources. It covers how machine learning algorithms can be applied to workforce analytics, including behavioral and performance assessments. The curriculum explores the ethical implications of using AI in hiring and employee management.
edX offers a variety of courses and programs on edge computing, covering fundamentals, use cases, and hardware and software components. Learners can gain hands-on experience with edge computing tools and technologies.
An introductory course on learning analytics that covers fundamental theories, processes, and different types of educational data. Students will gain experience with educational data sets and the R programming language.
This course provides a review of the basics of linear models and matrix algebra, which are foundational concepts for understanding regression methods.
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. The course covers the basics of linear regression and its application in real-world scenarios.
This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and scikit-learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.
Part of the Data Science Professional Certificate, this course covers popular machine learning algorithms, principal component analysis, and regularization. You will build a movie recommendation system.
This course helps you learn essential foundational math concepts for AI and machine learning, like calculus, linear algebra, and statistics, using a hands-on approach with Python.
An industry-focused introduction to machine learning that covers key algorithms, data preparation techniques, and model evaluation strategies. It is ideal for those looking to apply ML in a business context.
This course from Microsoft introduces the fundamental principles of machine learning using Python. You will learn about various machine learning algorithms, including regression, and how to implement them.
This course from the University of Toronto provides a hands-on introduction to quantum-enhanced machine learning. It covers the intersection of quantum computing and machine learning, focusing on algorithms that are challenging for classical computers. The course emphasizes implementing protocols using open-source Python frameworks and features guest lectures from prominent researchers in the field.
This course from Columbia University introduces the fundamental concepts of statistical thinking for data science. It covers topics such as probability, sampling, estimation, and hypothesis testing. The course emphasizes the practical application of these concepts to real-world data problems.
edX offers various courses on time series analysis from different universities. This is a general link to search for the latest offerings.
An in-depth introduction to time series analysis, covering structured models, predictions, and reinforcement learning with hands-on projects. This course is part of the MITx MicroMasters program in Statistics and Data Science.
This course, part of the TinyML Professional Certificate series, delves into the practical applications of Tiny Machine Learning. Students explore the code behind widely used TinyML applications like keyword spotting, visual wake words, and anomaly detection. The course uses real-world industry applications to illustrate the principles of TinyML.
As a key component of the TinyML Professional Certificate, this course offers hands-on experience in deploying machine learning models on small embedded devices. Students learn to program in TensorFlow Lite for Microcontrollers, write the necessary code, and deploy their models to a tiny microcontroller. The course utilizes a TinyML Program Kit that includes an Arduino board for practical projects.
This course from Harvard University introduces the field of Tiny Machine Learning (TinyML), which involves running machine learning models on low-power microcontrollers. It covers the fundamentals of deep learning, data collection, and model deployment on embedded devices, with a focus on applications like keyword spotting and image classification.
A MicroMasters program that includes a course on machine learning fundamentals, covering tree-based models and ensemble methods.
This program from UC Berkeley provides a comprehensive introduction to data science, including data wrangling and cleaning, using Python.
A self-paced course that provides a solid knowledge base in statistics, linear algebra, multivariable calculus, and probability for AI.
A three-course professional certificate program that provides a deep dive into computer vision. It covers principles from digital signal processing to machine learning, and topics such as image processing, 3D geometry, motion estimation, and object recognition.
This three-course professional certificate program, offered by Harvard University and Google TensorFlow, provides a deep dive into the emerging field of TinyML. It covers the essential language of TinyML, its real-world applications, and the practical deployment of machine learning models on resource-constrained embedded systems. The program emphasizes hands-on experience using a kit that includes an Arduino board.
This course equips learners with the skills to build and train powerful deep-learning models using PyTorch. It includes an in-depth exploration of convolutional neural networks for image recognition and covers advanced training techniques like dropout and batch normalization, which are crucial for avoiding common pitfalls.
This course introduces the fundamentals of reinforcement learning, guiding learners on how to frame RL problems and tackle classic examples. It covers basic algorithms and progresses to using function approximation with deep learning. It also features 'Project Malmo' for AI experimentation within Minecraft.
This program teaches how to use Python for data analysis in a business context, including data wrangling and visualization for EDA.
This course focuses on recognizing and solving convex optimization problems that arise in applications. Topics include convex sets, functions, and optimization problems; basics of convex analysis; and applications in signal processing, machine learning, and finance.
This course explores how learning analytics, machine learning, and AI integrate into modern L&D systems, featuring real LMS case studies. It is ideal for technical L&D professionals and data-informed instructional designers.
A search result page on edX for courses related to optimization for machine learning, featuring courses from various universities.
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