The Complete Linear and Logistic Regression Course in Python

Explore linear regression with the equation y equals m x plus b to predict y from input x, and visualize slope and intercept on a graph in python.

Implement simple linear regression using numpy, pandas, and matplotlib to model area to price with noise, compute W and b, and plot predicted versus actual prices, preparing for linear regression.

Explore multivariate linear regression, predicting multiple outputs from several inputs using a linear model y = W x + b, and optimize with gradient-based loss functions in TensorFlow.

Implement simple linear regression with TensorFlow Keras by building a single dense layer perceptron, normalizing inputs, training to predict house price from area, and evaluating with plots.

Implement multiple and multivariate linear regression in python using a keras normalization layer to scale data to zero mean and unit variance, select seven features, and visualize relationships with seaborn.

Normalize input data with a Keras layer, then build a two-hidden-layer ReLU network trained with Adam for 100 epochs, with a 0.2 validation split, and plot training versus validation loss.

Implement multiple and multivariate linear regression in python, evaluate predictions on unseen test data, plot errors, and extend to multivariate outputs with a dense layer matching the number of variables.

Learn the difference between classification and regression, with examples like handwritten digit recognition and house price prediction, and introduce decision boundaries and the sigmoid function in preparation for logistic regression.

Explore logistic regression for classification by modeling event probability with a sigmoid function and estimating weights and bias using maximum likelihood, cross-entropy loss, and multi-class extensions.

implement logistic regression on mnist with a single dense layer of ten units and sigmoid activation, flattening 28x28 to 784, normalizing to 0–1, and loading data for 50 epochs.

Train for 50 epochs with an 80/20 split using Adam and sparse categorical cross entropy, then plot loss to detect overfitting and explore hidden layers and handwritten digits visualization utilities.

Implement the final logistic regression utilities, visualize predictions and true labels alongside class probabilities for handwritten digits, and explore dense layers and softmax activations to improve performance.

demonstrate implementing advanced linear and logistic regression on housing data with TensorFlow, pandas, and numpy, including loading the uci housing dataset and splitting into train and test sets.

Build a linear regression model for housing data using input functions and feature columns, train with mean squared error, evaluate for overfitting, and visualize results with TensorBoard.

builds and evaluates a regression model for housing data, adds interaction features like average rooms and low-income proportion, and monitors loss to improve fit and generate predictions.

Transform a Keras model into a tf.estimator, enabling automated, scalable deployment on cloud and various hardware while combining Keras and estimator workflows.

Implement a housing data model by defining feature columns and a dense feature layer with batch normalization. Convert the Keras model to a TF estimator for scalable training and evaluation.

Explore how loss functions shape convergence and predictions in housing data linear regression, comparing L1 and L2 losses through MSE and MAE in a practical Keras workflow.

Explore lasso and ridge regression as regularization methods for linear models, using Keras L2 regularization to limit feature influence and compare training and test performance.

Apply elastic net regression on the housing data by combining L1 and L2 regularization, compare with ridge and lasso results, and conclude the housing project before moving to logistic regression.

Implement logistic regression to predict breast cancer probability using the Wisconsin dataset, train on 0.8, apply sigmoid activation with binary cross-entropy and L2 regularization, achieving 0.95 accuracy.

Implement the dieback project with logistic regression in Google Colab using scikit-learn, train-test split, and evaluate accuracy around 78% on the diabetes dataset.

Continue practicing to strengthen your deep learning fundamentals, set goals to become a successful deep learning engineer, and download Kaggle and UCI Respiratory datasets to develop and share a model.