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