Machine Learning Training

COURSE CONTENT OF MACHINE LEARNING

     Revisiting Python
     Python Libraries for ML
     What is ML? Why ML?
     Introduction to Supervised ML
     Introduction to Unsupervised ML
     Mathematical Background for ML- Matrix ops Probability Theory (Bayes' Theorem)
     tatistical knowledge for ML- Mean, Median, Mode , Z-scores, bias -variance dichotomy
     Tools required for development - Anaconda, Jupyter NB
     ML libraries Explained: Scipy, Numpy, Matplotlib
     ML Glossary- Variable types, k-fold CV, AUC ,
     F1 score,Overfitting/Underfitting, Generalization,
     Data split & hyper parameter training
     Data wrangling using Pandas
     Exploratory Data analysis using Visualization
     Scikit-learn Library for ML
     Code Exercises
     Supervised learning - Regression
     Different types of Regression-Linear and Logistic
     Decision tree Algorithms
     Real-world code exercises
     Supervised Learning- Classification
     Naive- Bayes' Classification
     KNN Classification
     Real-world code exercises
     Clustering Introduction
     k-means clustering
     Code Exercises
     Advanced topics-Curse of Dimensionality-PCA algo
     SVM Classification Introduction
     Real-time mini project
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