21 Days Masterclass on Machine Learning

✅Day 1: Python refresher (functions, OOP, error handling)
✅Day 2: NumPy (arrays, indexing, operations)
✅Day 3: Pandas (DataFrames, CSV/Excel import/export, filtering, grouping)
✅Day 4: Data Cleaning (handling missing values, duplicates, outliers)
✅Day 5: Data Visualization (Matplotlib, Seaborn – plots, histograms, heatmaps)
✅Day 6: Exploratory Data Analysis (EDA) on a dataset (Kaggle Titanic dataset)
✅Day 7: ML basics (supervised vs unsupervised, train/test split, accuracy)
✅Day 8: Sale Prediction using LOGISTIC REGRESSION
✅Day 9:Handwritten Digit Recognition using SUPPORT VECTOR MACHINE
✅Day 10: Digit recognition using RANDOM FOREST
✅Day 11: Plant leaf Iris detection using DECISION TREE
✅Day 12: Salary estimation using K-NEAREST NEIGHBOUR
✅Day 13: Titanic Survival prediction using NAIVE BAYES
✅Day 14: Evaluating Classification model Performance
✅Day 15: House price prediction using Linear Regression Single Variable
✅Day 16 : Exam mark prediction using LINEAR REGRESSION – MULTIPLE VALUES
✅Day 17 : Salary Prediction using POLYNOMIAL REGRESSION
✅Day 18: Height Prediction using DECISION TREE REGRESSION
✅Day 19 : Car Price Prediction using RANDOM FOREST REGRESSION
✅Day 20 :Evaluating Regression Model Using R-Squared & Adjusted R-Squared
✅Day 21 : Web Ad Optimization using Upper Confidence Bound – Reinforcement
Learning