Johns Hopkins University
Applied Machine Learning Specialization
Johns Hopkins University

Applied Machine Learning Specialization

Master Applied Machine Learning Techniques. Master advanced machine learning techniques to solve real-world problems in data processing, computer vision, and neural networks

Erhan Guven

Instructor: Erhan Guven

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Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Master data preprocessing techniques for machine learning applications.

  • Evaluate and optimize machine learning models for performance and accuracy.

  • Implement supervised and unsupervised learning algorithms effectively.

  • Apply advanced neural network architectures like Convolutional Neural Networks (CNNs) in computer vision tasks.

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Taught in English
Recently updated!

September 2024

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Specialization - 3 course series

What you'll learn

  • Understand and implement machine learning techniques for computer vision tasks, including image recognition and object detection.

  • Analyze data features and evaluate machine learning model performance using appropriate metrics and evaluation techniques.

  • Apply data pre-processing methods to clean, transform, and prepare data for effective machine learning model training.

  • Implement and optimize supervised learning algorithms for classification and regression tasks.

Skills you'll gain

Category: Data Pre-Processing
Category: Feature Engineering
Category: Supervised Learning
Category: Practical Application
Category: Model Evaluation

What you'll learn

  • Understand and apply ensemble methods to improve model accuracy and robustness by combining multiple learning algorithms.

  • Explore advanced regression techniques for predicting continuous outcomes and modeling complex relationships in data.

  • Apply unsupervised learning algorithms for clustering, dimensionality reduction, and pattern recognition in unlabeled data.

  • Understand and implement reinforcement learning techniques and apriori analysis for decision-making and association rule mining.

Skills you'll gain

Category: Ensemble Learning
Category: Unsupervised Learning
Category: Reinforcement Learning
Category: Apriori Analysis
Category: Advanced Regression Techniques

What you'll learn

  • Build neural networks from scratch and apply them to real-world datasets like MNIST.

  • Apply back-propagation for optimizing neural network models and understand computational graphs.

  • Utilize L1, L2, drop-out regularization, and decision tree pruning to reduce model overfitting.

  • Implement convolutional neural networks (CNNs) and tensors using PyTorch for image and audio processing.

Skills you'll gain

Category: PyTorch Proficiency
Category: Regularization Techniques
Category: Neural Network Implementation
Category: Convolutional Neural Networks (CNNs)
Category: Back-Propagation Mastery

Instructor

Erhan Guven
Johns Hopkins University
3 Courses14 learners

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