Hindi Machine Learning with Python: From Basics to Ensemble Learning

Lesson 1: Hindi Machine Learning Tutorial 1 - What is Machine Learning?

This opening lesson introduces machine learning, deep learning, and common real-life applications. It sets up the series and explains what will be covered next, including writing Python code to solve a simple problem.

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Lesson 2: Hindi Machine Learning Tutorial 2 - Linear Regression Single Variable

This lesson uses linear regression to predict home prices from home area in square feet. It trains a model with the scikit-learn linear regression class and shows how to use the predict method.

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Lesson 3: Hindi Machine Learning Tutorial 3 - Linear Regression Multiple Variables

This lesson extends linear regression to multiple variables. It predicts home prices using area, bedrooms, and age as independent variables, and uses a pandas dataframe to prepare the data.

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Lesson 4: Hindi Machine Learning Tutorial 4 - Gradient Descent and Cost Function

This lesson covers cost function, gradient descent, learning rate, and mean squared error. After the math, it implements gradient descent for linear regression in Python.

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Lesson 5: Hindi Machine Learning Tutorial 5 - Save Model Using Joblib And Pickle

This lesson shows how to save a trained model to a file and load it later for predictions. It uses joblib and pickle so that large models do not need to be retrained every time.

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Lesson 6: Hindi Machine Learning Tutorial 6 - Dummy Variables & One Hot Encoding

This lesson explains how to handle text and categorical data in machine learning. It covers label encoding and one hot encoding, and why one hot encoding is often better for nominal categories.

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Lesson 7: Hindi Machine Learning Tutorial 7 - Training and Testing Data

This lesson explains how to split a dataset into training and test sets using sklearn's train_test_split. It shows why testing on data the model has not seen gives a more honest performance estimate.

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Lesson 8: Hindi Machine Learning Tutorial 8 - Logistic Regression (Binary Classification)

This lesson introduces logistic regression for binary classification. It uses the scikit-learn logistic regression class to predict whether a customer would buy life insurance, and ends with an exercise.

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Lesson 9: Hindi Machine Learning Tutorial 9 - Logistic Regression (Multiclass Classification)

This lesson applies logistic regression to multiclass classification. It predicts handwritten digits using the digits dataset available in scikit-learn.

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Lesson 10: Hindi Machine Learning Tutorial 10 - Decision Tree

This lesson covers decision trees for classification. It explains the theory and then solves an employee salary prediction problem with Python code, followed by an exercise.

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Lesson 11: Hindi Machine Learning Tutorial 11 - Support Vector Machine

This lesson introduces support vector machines for classification. It covers the theory and then uses scikit-learn to classify iris flowers, including parameters such as gamma, regularization, and kernel.

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Lesson 12: Hindi Machine Learning Tutorial 12 - Random Forest

This lesson covers random forests for classification. It explains how multiple decision trees are combined and how a majority vote produces the final prediction.

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Lesson 13: Hindi Machine Learning Tutorial 13 - K Fold Cross Validation

This lesson explains K fold cross validation for comparing model performance. It shows how creating K folds gives a better evaluation than a single train_test_split.

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Lesson 14: Hindi Machine Learning Tutorial 14 - K Means Clustering

This lesson introduces K means clustering, an unsupervised learning technique. It covers the theory and solves an income group clustering problem with scikit-learn, including the elbow method to choose K.

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Lesson 15: Hindi Machine Learning Tutorial 15 - K nearest neighbors classification with python code

This lesson explains how K nearest neighbors classification works and then builds a KNN model with scikit-learn. It ends with an exercise to practice the concepts.

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Lesson 16: Hindi Machine Learning Tutorial 16 - Principal Component Analysis (PCA) with Python Code

This lesson covers principal component analysis, a dimensionality reduction technique. It explains the curse of dimensionality and shows how to apply PCA with Python code.

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Lesson 17: Hindi Machine Learning Tutorial 17 - Bias vs Variance In Machine Learning

This lesson explains bias and variance in machine learning using a house price prediction example. It also covers overfitting and underfitting and the bias variance trade off.

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Lesson 18: Hindi Machine Learning Tutorial 18 Ensemble Learning - Bagging

This lesson introduces ensemble learning with a focus on bagging. It explains how combining multiple models can reduce variance and improve predictions.

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