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

Instructor: Erhan Guven

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.

    • Skills you'll gain

  •   Applied Machine Learning
  •   Regression Analysis
  •   Deep Learning
  •   Image Analysis
  •   Machine Learning
  •   Reinforcement Learning
  •   Unsupervised Learning
  •   Computer Vision
  •   Feature Engineering
  •   Machine Learning Algorithms
  •   PyTorch (Machine Learning Library)
  •   Decision Tree Learning

    • Specialization - 3 course series

    In this specialization, learners will work on real-world projects, such as predicting suicide rates using datasets from Kaggle. By applying machine learning techniques, learners will preprocess data, identify important features, and develop predictive models. They will work through complex challenges, such as determining whether to apply classification or regression models and fine-tune machine learning algorithms to find strong correlations between variables. Through the use of tools like Jupyter Notebook and PyTorch, learners will gain practical experience, creating functional prototypes that solve authentic, data-driven problems, and preparing them for real-world machine learning applications.

    Advanced Methods in Machine Learning Applications

    Mastering Neural Networks and Model Regularization

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