Social And Emotional Learning Courses

Kaggle;  Amazon Web Services;  Amazon Microcredential

Introduction to Machine Learning with TensorFlow

5 reviews

Learn supervised and unsupervised machine learning techniques using TensorFlow and Python. Master regression, neural networks, clustering, and more to solve real-world problems and build an image classifier.

• Udacity
• 49 hours
• On-Demand
• Paid Course

Kaggle;  Amazon Web Services;  Amazon Microcredential

Introduction to Machine Learning with Pytorch

5 reviews

Comprehensive introduction to machine learning using PyTorch, covering supervised and unsupervised techniques with hands-on projects for real-world problem-solving and model building.

• Udacity
• 49 hours
• On-Demand
• Paid Course

Machine Learning in Genomics - Fall 2019

Comprehensive exploration of computational biology techniques, from dynamic programming to deep learning, applied to genomics, epigenomics, and systems biology. Covers cutting-edge topics like single-cell genomics and genome engineering.

• YouTube
• 1 day 10 hours
• On-Demand
• Free Video

Machine Learning for Genomics Fall 2020

Fall 2020 Prof. Manolis Kellis Computational Biology: Genomes, Networks, Evolution, Health Machine Learning in Genomics: Dissecting the circuitry of Human Disease

• YouTube
• 1 day 8 hours
• On-Demand
• Free Video

Mathematics of Big Data and Machine Learning, IAP 2020

Explore artificial intelligence and machine learning with a focus on data handling challenges, covering topics from associative arrays to bio sequence cross-correlation and Kronecker graphs.

• YouTube
• 14 hours
• On-Demand
• Free Video

Data Augmentation for Image-Based Reinforcement Learning

Explores data augmentation techniques for image-based reinforcement learning, presenting a model-free algorithm and a self-supervised framework for visual continuous control tasks, achieving state-of-the-art results.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Computational Principles Underlying the Learning of Sensorimotor Repertoires

Explore computational principles of motor learning, focusing on context's role in memory activation and how statistical learning shapes object representations.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Diverse Data and Efficient Algorithms for Robot Learning

Explore diverse data collection and efficient algorithms for robot learning, focusing on large-scale approaches and reinforcement learning for deformable object manipulation.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Recent Progress on Foundation Model Supervision for Robot Learning

Discover how foundation models from non-robot domains can revolutionize robot learning, enabling automated task acquisition without extensive data collection or manual controller design.

• YouTube
• 53 minutes
• On-Demand
• Free Video

6.036 Introduction to Machine Learning

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction.

• Independent
• On-Demand
• Free Online Course

Reflective Practice: An Approach for Expanding Your Learning Frontiers

Explore reflective practice to enhance professional learning, knowledge application, and innovation. Develop awareness of cognitive resources, framing techniques, and conceptual flexibility for improved practice and timely learning.

• MIT OpenCourseWare
• On-Demand
• Free Online Course

Data Analysis for Social Scientists

Learn data analysis techniques for social sciences, including probability, statistics, regression, experiments, and machine learning, with real-world applications and R programming instruction.

• MIT OpenCourseWare
• On-Demand
• Free Online Course

Matrix Calculus for Machine Learning and Beyond

Explore advanced matrix calculus techniques for machine learning, optimization, and large-scale computing. Learn holistic matrix approaches, generalized derivatives, and reimagined differentiation formulas.

• MIT OpenCourseWare
• On-Demand
• Free Online Course

Mathematics of Big Data and Machine Learning

Explore the Dynamic Distributed Dimensional Data Model (D4M) to tackle Big Data challenges using graph theory, linear algebra, and databases. Apply this innovative approach to real-world problems in various fields.

• MIT OpenCourseWare
• On-Demand
• Free Online Course

Data Analysis for Social Scientists

Master methods for analyzing data to answer cultural, social, economic, and policy questions using R, probability, statistics, and machine learning techniques.

• MITx Online
• 14 weeks, 12-14 hours a week
• On-Demand
• Free Online Course