Applied Machine Learning

Johns Hopkins University via Coursera Specialization

Overview

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This specialization is intended for post-graduate students seeking to develop practical machine-learning skills applicable across various domains. Through three comprehensive courses, learners will explore core techniques including supervised learning, ensemble methods, regression analysis, unsupervised learning, and neural networks. The courses emphasize hands-on learning, providing you with the opportunity to apply machine learning to real-world problems like image classification, data feature extraction, and model optimization. You will dive into advanced topics such as convolutional neural networks (CNNs), reinforcement learning, and apriori analysis, learning to leverage the PyTorch framework for deep learning tasks. By the end of the specialization, you will be well-equipped to handle complex machine learning challenges in fields like computer vision and data processing, making you a valuable asset in industries requiring advanced predictive modeling, AI-driven solutions, and data-driven decision-making. This specialization is designed to build both theoretical knowledge and practical skills to thrive in the ever-evolving tech landscape.

Syllabus

Course 1: Applied Machine Learning: Techniques and Applications
- Offered by Johns Hopkins University. The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of ... Enroll for free.

Course 2: Advanced Methods in Machine Learning Applications
- Offered by Johns Hopkins University. The course "Advanced Methods in Machine Learning Applications" delves into sophisticated machine ... Enroll for free.

Course 3: Mastering Neural Networks and Model Regularization
- Offered by Johns Hopkins University. The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and ... Enroll for free.

Courses

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19 hours 58 minutes
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The course "Advanced Methods in Machine Learning Applications" delves into sophisticated machine learning techniques, offering learners an in-depth understanding of ensemble learning, regression analysis, unsupervised learning, and reinforcement learning. The course emphasizes practical application, teaching students how to apply advanced techniques to solve complex problems and optimize model performance. Learners will explore methods like bagging, boosting, and stacking, as well as advanced regression approaches and clustering algorithms. What sets this course apart is its focus on real-world challenges, providing hands-on experience with advanced machine learning tools and techniques. From exploring reinforcement learning for decision-making to applying apriori analysis for association rule mining, this course equips learners with the skills to handle increasingly complex datasets and tasks. By the end of the course, learners will be able to implement, optimize, and evaluate sophisticated machine learning models, making them well-prepared to address advanced challenges in both research and industry.
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19 hours 24 minutes
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The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. This course stands out for its balance between foundational concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.
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16 hours 3 minutes
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The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.