Machine Learning Course

What you will learn

  • Introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
  • Linear regression predicts a real-valued output based on an input value
  • This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
  • How linear regression can be extended to accommodate multiple input features. Practices for implementing linear regression.
  •  Designed to help you understand how to implement the learning algorithms in practice
  • Introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
  • Introduce regularization, which helps prevent models from overfitting the training data.
  • Neural Networks: Representation
  • Introduce the backpropagation algorithm that is used to help learn parameters for a neural network.
  • Advice for Applying Machine Learning
  • How to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
  • The idea and intuitions behind SVMs and discuss how to use it in practice.
  • We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
  • Show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
  • How a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
  • Recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
  • How to apply the machine learning algorithms with large datasets.
  • How to apply the machine learning algorithms with large datasets.

After completing each course in specialization and complete the hands-on project, you’ll earn a Certificate.

Course Rating : 4.9

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