• What you'll learn
    • 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
  • Requirements
    • Basic knowledge of python programming
    • A system with i3
    • Your dedication
  • 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
Register