Supervised Machine Learning: Complete Masterclass [2023]

Explore the spectrum of artificial intelligence from narrow ai to general (strong) ai and super ai, and examine functionalities like reactive machines, limited memory, theory of mind, and self-awareness.

Explore AI applications across real world domains, including ecommerce with personalized shopping and smart content, education with automated administration and personalized learning, robotics, and autonomous vehicles.

Explore the five AI domains—machine learning, deep learning, data science, computer vision, and natural language processing—and how they transform raw data into outputs.

Explore supervised, unsupervised, semi supervised, and reinforcement learning, and learn how labeled data, cross validation, and clustering drive accurate classification, with real world uses like spam filtering and object detection.

Explore trends in machine learning by contrasting supervised, unsupervised, and reinforcement learning. Observe how supervised use slows while reinforcement gains prominence, with semi-supervised emerging.

Learn supervised learning algorithms, including classification with support vector machines and nearest neighbors, and regression with linear and logistic models, plus neural networks and random forests for binary outcomes.

Explore real-time uses of supervised learning, including image and object recognition, sentiment analysis from Twitter data, predictive stock and weather analytics, and spam filtration with unsupervised or semi supervised methods.

Identify the supervised learning algorithms used for classification and regression, including k-nearest neighbor, decision trees, neural networks, SVM, and logistic regression, with labeled data guiding future predictions.

Master multiclass classification in supervised learning, where labeled data predicts among multiple classes. Save trained models for future predictions and test unseen inputs, like fruit types.

Explore multi-label classification in supervised learning, where models predict and label multiple objects per frame or image—such as person, table, chair, street light—across hundreds of classes.

Learn simple linear regression, modeling a single dependent variable from a single independent variable for predictive analysis. Use regression equation y equals beta0 plus beta1 x and recognize overfitting risk.

Explore multi linear regression using multiple independent variables to predict a single dependent variable, and learn why linear data yields a single regression line while non-linear data breaks this approach.

Learn polynomial regression, modeling the x and y relationship with a higher-degree polynomial, and see how increasing degree shapes the curved predictor versus simple and multi linear regression.

Explore the graph of simple linear regression, highlighting the least-squares line and training data points. Learn how residuals to the line quantify error and how r-squared reflects model accuracy.

Demonstrates polynomial regression by fitting a curved line to non-linear data, improving prediction accuracy where the least-squares line from linear regression fails.

Code and train regression models in python using scikit-learn, numpy, pandas, and matplotlib. Perform simple linear regression, load data, and explore with data analysis and one-hot encoding.

Learn data preprocessing for supervised learning by converting a data frame to X and Y, extracting features, and splitting data into training and testing sets for model training.

Compute the R-squared score manually by deriving SSR and SST from y predictions and the mean of y, iterating with Python code, and covering data loading, preprocessing, and scatterplot visualization.

Learn to perform simple linear regression using scikit-learn, numpy, and pandas, visualize results with matplotlib, and load data from a CSV in a notebook while troubleshooting path errors.

apply simple linear regression on a real time data set with attributes like gender, age, head size, and brain weight, using numpy, pandas, seaborn, and scikit-learn.

Plot a simple linear regression using seaborn regplot to visualize head size versus brain weight, and check for missing values with data.info, imputing missing values with the mean if needed.

Explore data preprocessing by selecting features (independent variables) and the target (dependent variable) with slicing, convert to arrays, and prepare X and Y for supervised learning.

Split data into train and test sets, typically 75/25 or 80/20, to evaluate a linear regression model by predicting on the test data and plotting the regression line.

Use a label encoder to convert gender to numeric, prepare X and Y, and split data into training and testing sets with random_state 25, then apply min max scalar.

Develop a k-nearest neighbors classifier by selecting k, fitting on X_train and y_train, predicting X_test, and evaluating with confusion matrix and accuracy, with distribution plots.

Use scikit-learn's GridSearchCV to tune a k-nearest neighbors model with a parameter grid and cross-validation, selecting the best k. Apply kNN regression to predict a target from features.

Discover how to code a decision tree in Python using the mushrooms dataset, including data loading, label encoding, feature preparation, and a train-test split for classification.

Implement a decision tree classifier, train on x_train and y_train, evaluate accuracy, predict x_test, and visualize the entropy criterion tree with plot_tree.

Discover how data visualization uses charts, graphs, and animations to turn raw data into accessible insights, and learn to use Python libraries like matplotlib and seaborn for effective storytelling.

Set up the plotting environment, create a figure and axis, generate data with numpy linspace, and plot the sine of the data against the original values using matplotlib.

Plot and compare sine and cosine curves from a 0 to 10 value array, demonstrating sign values, and render multiple lines in a single plot using numpy.

Learn to plot multiple lines in a single plot using multiple plot functions, with color changes as A ends and B starts, from 0 to 10 to 0 to 20.

Master how to set line colors and styles in plots using color names, short codes (r, g, b, k, y), grayscale values, hex codes, rgb tuples, and HTML color schemes.

Explore decision trees, a supervised learning algorithm for classification and regression, using entropy and information gain, with a Python mushroom dataset demonstration of training and plotting a classifier.