Machine learning

The Machine Learning course is a basic understanding of mathematics and statistics at a graduate level. The learner should have an understanding of Python for Data Science and Statistics essential for Data Science, for a better understanding of the Machine Learning course.

Machine learning is a form of artificial intelligence where the focus is to develop computer programs that automate data analysis by learning and adapting through the experience without the need for precise programming. Our course has been designed to help you master machine learning concepts and techniques working with actual data, developing algorithms through supervised and unsupervised learning, performing classification and regression operations and constructing time series models. This course explores in depth the libraries and functionalities the python programming language offers for machine learning techniques in order to draw conclusions from data. The course includes 2 projects to solidify your knowledge and skills you've gained.

COURSE AGENDA

  • Introduction to Machine Learning,
  • Machine Learning Application,
  • Introduction to AI,
  • Different types of Machine Learning - Supervised,
  • Unsupervised,
  • Reinforcement
  • Statistical Inference,
  • Types of Variables,
  • Probability Distribution,
  • Normality,
  • Measures of Central Tendencies,
  • Normal Distribution
  • Machine Learning Projects Checklist,
  • Frame the problem and look at the big picture,
  • Get the data,
  • Explore the data to gain insights,
  • Prepare the data for Machine Learning algorithms,
  • Explore many different models and short-list the best ones,
  • Fine-tune model,
  • Present the solution,
  • Launch, monitor,
  • maintain the system
  • Training a Binary classification,
  • Performance Measures,
  • Confusion Matrix,
  • Precision and Recall,
  • Precision/Recall Tradeoff,
  • The ROC Curve,
  • Multiclass Classification,
  • Multilabel Classification,
  • Multioutput Classification
  • Linear Regression,
  • Gradient Descent,
  • Polynomial Regression,
  • Learning Curves,
  • Regularized Linear Models,
  • Logistic Regression
  • Linear SVM Classification,
  • Nonlinear SVM Classification,
  • SVM Regression
  • Training and Visualizing a Decision Tree,
  • Making Predictions,
  • Estimating Class Probabilities,
  • The CART Training Algorithm,
  • Gini Impurity or Entropy,
  • Regularization Hyperparameters,
  • Regression,
  • Instability
  • Voting Classifiers,
  • Bagging and Pasting,
  • Random Patches and Random Subspaces,
  • Random Forests,
  • Boosting,
  • Stacking
  • The Curse of Dimensionality,
  • Main Approaches for Dimensionality Reduction,
  • PCA,
  • Kernel PCA,
  • LLE,
  • Other Dimensionality Reduction Techniques

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