Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

What you'll learn

  • Master machine learning on python
  • Make robust machine learning models
  • Use machine learning for personal purpose
  • Handle advanced techniques like dimensionality reduction
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Course Curriculum

Machine Learning Module 1
  • Introduction machine learning module 1
  • Supervised, unsupervised, semi-supervised, reinforcement
  • Train, test, validation split
  • Performance
  • Overfitting, underfitting
  • OLS
  • Linear regression
  • polynomial regression
  • Assumptions R-square adjusted, R-square intro to Scikit-learn, training methodology, hands-on linear regression, ridge regression, logistics regression, precision-recall
Machine Learning Module 2
  • Decision tree, decision tree regressor, cross-validation
  • Bias vs variance, ensemble approach, Bagging, boosting
  • Randon forest, stacking, variable importance
  • XGBoost, hands-on XGBoost, gradient boost, ada boost
Machine Learning Module 3
  • K Nearest Neighbour, k-NN regressor, lazy learners, the curse of dimensionality, k-NN issues
Machine Learning Module 4
  • K-means, hierarchical clustering, DBSCAN
  • Performance measurement, principal component analysis, dimensionality reduction
Machine Learning Module 5
  • Naive Bayes SVM
  • Anamoly detection
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Course Instructor
Vijaya Mekala

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