Machine Learning for Interviews & Research and DL basics

Learn how classification uses discrete targets with fixed categories, illustrated by credit scoring with income and savings. Explore applications across structured and unstructured data.

Learn linear regression basics, including univariate regression with bias and weights and its matrix form, and the mean squared error, gradient descent, and the pulse objective for energy forecasts.

Apply gradient descent to univariate linear regression, a convex optimization, updating theta0 and theta1 to minimize the cost. Use epochs and learning rate alpha toward a local optimum.

Learn the normal equations as an exact analytical method for linear regression, deriving weights with the equation (X^T X)^{-1} X^T y, and compare to gradient descent's iterative approach.

Explore logistic regression as a binary classifier with a sigmoid hypothesis and 0.5 threshold. Minimize negative log-likelihood, note non-convex optimization, and apply one-vs-all or New Balance method for multi-class voting.

Analyze overfitting and underfitting, and apply regularization to reduce overfitting by penalties on weights, including l2 and l1 methods, with dropout and dimensionality reduction, to improve generalization.

Outline the requirements for deep learning, including three hidden layers, and demonstrate learning rate, activation, and l1 regularization using TensorFlow Playground, with attention to training loss and overfitting.

Learn to build a deep neural network by sizing the input layer to features or image pixels, using ReLU in hidden layers, and constructing a Keras sequential model for regression.

Explains overfitting in deep neural networks caused by high variance, and how dropout and regularization reduce training variance; shows training-time neuron removal and test-time averaging to improve generalization.

Apply batch normalization to hidden-layer inputs using batch mean and standard deviation to improve accuracy, reduce overfitting, and complement dropout, though it is not compulsory for deep neural networks.

Explore how residual connections enable deeper networks to run faster than shallow models, as shown by 152-layer ResNet outperforming the 19-layer VGG, using skip connections to ease gradient flow.

Explore how LSTM units use forget, input, and output gates to manage long-term dependencies in time series data, with memory cells and a candidate layer guiding sequence outputs.

Examine a load forecasting case study using smart meter energy data and weather features, detailing time-series preprocessing and a range of models from regression to deep learning.

Learn multivariable linear regression from data loading and exploration to feature selection, model training, and evaluation using mean squared error, mean absolute error, and R-squared.

Explore object oriented programming concepts with a Python example, covering classes, objects, encapsulation, inheritance, polymorphism, and abstraction to organize machine learning pipelines and improve code reuse.