Master Machine Learning: Basics, Jobs and Interview Bootcamp

Learn to import three essential libraries for machine learning: numpy for numerical calculations, pandas for structured data handling, and matplotlib for visualizing data, with why they matter in interviews.

Import the dataset into Spyder to train machine learning algorithms; use pandas to create a data frame, and verify file paths, extensions, and missing values.

Explain the matrix of features as the independent variables used to predict the dependent variable, with an example predicting product purchase from country, age, salary, and assembling X for training.

Learn to handle missing data by analyzing datasets, choosing strategies like mean, median, or most frequent values, and applying imputation with relevant libraries.

Learn to preprocess categorical data for machine learning by applying label encoding and one hot encoding, transforming categories into numeric features for improved model performance.

Split the dataset into training and testing sets, train with eighty percent and test with twenty percent to predict whether a user will buy an Amazon product.

Apply feature scaling in preprocessing using normalization and standardization to align training and testing data and enable Euclidean distance-based predictions.

Explore simple linear regression by defining the dependent variable and the independent variable, and show how the regression coefficient and the line predict outcomes such as salary.

Learn how ordinary least squares fits a line to data points by minimizing the sum of squared errors between actual and predicted values.

Learn simple linear regression with coding by performing data preprocessing, preparing the salary dataset, extracting X as experience (independent) and y as salary (dependent), and executing a train/test split.

Step through building a simple regression model with a linear model library, train it on your dataset, and learn when to apply feature scaling to the features.

Train a regression model from scratch, use its predict function on x test to estimate salaries, compare with ordinary least squares results, and tweak parameters while planning visualizations.

visualize linear regression predictions by plotting a blue scatter of actual data and a red regression line, showing salary versus experience and how well the model predicts salaries.

Learn multiple linear regression by predicting profit from multiple independent variables, administration spending, marketing spend, and launch state. See how this extends simple regression to real startup analysis.

Learn to use dummy variables in a linear regression model, categorize states like New York, California, and Los Angeles, and avoid the dummy variable trap by dropping one category.

Learn to build a model by choosing relevant independent variables, discarding irrelevant attributes, and applying backward or forward elimination to improve predictions.

Select a full model with all predictors, then iteratively discard the worst predictors using p-values and a 0.05 significance level to refine backward elimination.

Set up the environment for multiple linear regression, define profit as the dependent variable, identify independent features, and build a feature matrix. Apply backward elimination to select predictors.

Train a multiple regression model after preprocessing and a train-test split, then evaluate predictions and prepare for backward elimination by examining p-values and model significance.

Explore backward elimination in multiple linear regression by fitting a full model, evaluating p-values against a 0.05 significance level, and sequentially removing least significant predictors.

Discover why polynomial regression is needed when linear regression fails to fit exponential-like data. Learn how polynomial regression captures complex trends, with stock market analysis and investment strategy applications.

Practice polynomial regression to model salaries by level, transforming the single feature into polynomial features and comparing linear and polynomial fits to select an appropriate degree.

Master machine learning basics introduces regression trees, their entropy-based splits and information gain, and how to predict outcomes by averaging values within each region.

Explore decision tree regression as a non-linear approach to predict salaries from a dataset, compare it with linear models, fit the model, and visualize predictions, including addressing non-continuous outputs.

Explore random forest regression by combining multiple decision trees and averaging their predictions to improve accuracy, illustrated with an example using jelly beans and sampling steps.

Explore random forest regression by loading a dataset, fitting the model with a chosen number of trees and a random state, and using ensemble averaging to predict and visualize results.

Learn how logistic regression uses probability to categorize outcomes, turning a linear regression into a sigmoid function to predict whether a customer will buy a product based on age.

Apply logistic regression to a social advertising dataset with age, gender, and estimated salary to predict product purchases, after preparing data, splitting into training and testing sets, and scaling features.

Learn to set up a logistic regression model by importing libraries, creating and training a classifier, and making predictions, with a preview of evaluating results via a confusion matrix.

Explore evaluating machine learning models with a confusion matrix, distinguishing true positives, true negatives, false positives, and false negatives. Calculate and report accuracy, such as 89%, for interview-ready model assessments.

Learn logistic regression visualization with two independent variables, training and test evaluation, and sigmoid-based predictions, using a graph to extract insights for targeting customers and boosting sales.

Master machine learning basics with an introduction to k-nearest neighbors, showing how to choose k, compute Euclidean distances, and classify a new point by majority among its five nearest neighbors.

Apply k-nearest neighbor on a labeled dataset by importing libraries, preparing X and Y, using training and testing data, and evaluating with a confusion matrix and accuracy using Euclidean distance.

Explore the support vector machine (svm) as a classification algorithm that creates a maximum-margin hyperplane to separate categories, using support vectors and linear or diagonal boundaries in multi-dimensional data.

Explore how to train a support vector machine (SVM) with a linear kernel to classify data and evaluate results using a confusion matrix.