Machine Learning & Deep Learning Masterclass in One Semester

Explore the concept of intelligence and why we want machines to be intelligent. Learn machine learning basics: supervised learning with labeled data, unsupervised learning with unlabeled data, and deep learning models and applications.

Explore supervised machine learning, focusing on labeled data, input features (X) and output labels (Y), and its two applications: classification with discrete classes and regression with continuous outputs.

Master Google Colab workflow by mounting the drive, setting the path, and reading datasets or images. Display images with matplotlib and practice reading data in notebooks.

Explore comparison operators—greater than, less than, equal, not equal, and their true/false results—along with and/or logic and the difference between double equals for comparison and assignment.

Learn to create and arrange multiple subplots with matplotlib, control layout, and compare continuous and discrete sine waves across 2x2 and 3x2 grids.

Learn to use matplotlib rc parameters to globally control line width, line style, figure size, and label sizes across multiple subplots and sine wave plots.

Learn to create and manipulate lists in Python, and distinguish them from NumPy arrays. Practice indexing, slicing, length, append, pop, and nested lists to form matrices.

Learn to use Python for loops with range, colon, indentation and NumPy/Matplotlib to repeat tasks, print sequences, and filter numbers with if statements, including building lists and summing ten numbers.

Explore nested for loops, where the outer loop controls rows and the inner loop controls columns, illustrated by 3x6 and 3x3 matrices and a 5x5 checkerboard.

Discover print formatting with strings using print and the format method to fill curly brackets with words or indices. Explore examples showing positional and alphabetical indices to shape sentences.

Explore dictionaries as a data structure by learning how keys index values, create key-value pairs with curly braces, and use methods like keys, values, and items.

Create and call functions in Python using def keyword, input parameters, colon, indentation. Use print or return to output results with examples of add, multiply, prime checks, and argument-free functions.

Learn how to create classes in Python, define the init method and attributes, and distinguish class attributes from object attributes, with examples like a boy class and a sphere class.

Learn data pre-processing essentials, including normalization, min-max scaling, standardization, handling missing values, encoding categorical features, and feature engineering, all implemented in Python across six projects.

learn data standardization in python with numpy and sklearn, build a three-column dataset, and apply fit on training data and transform on test to achieve mean zero and unit variance.

Trace the origin of regression from the straight line to a linear model, detailing slope, intercept, and how the independent variable X and dependent variable Y relate through training data.

Explore target values and predicted values in linear regression, learn to plot targets, train a regression model, and measure error and accuracy when predictions differ.

Learn to evaluate regression with root mean square error, mean absolute error, and R-squared; calculate errors, compare across data, and interpret model performance.

Generate a 1000-sample, three-feature dataset with make_regression, apply multiple linear regression using sklearn, split with train_test_split, and evaluate with root mean square error, mean absolute error, and r-squared.

Learn gradient descent to minimize mean squared error by iteratively updating weights and biases, overcoming least squares limitations like noninvertible X'X and sample-to-feature concerns.

Learn polynomial regression in python with sklearn, transform experience into polynomial features, fit linear regression, and evaluate degrees 1–4 on a salary dataset to optimize performance.

Apply ridge regression, or L2 regularization, by adding a squared penalty on weights to the least-squares objective to reduce overfitting in noisy, correlated data, controlled by lambda.

Evaluate a logistic regression model using a 2x2 confusion matrix, interpreting true positives and negatives, false positives and negatives, and computing accuracy with type one and type two errors.

Learn how to evaluate classifier performance using accuracy, precision, recall, and F1 score, computed from the confusion matrix, with emphasis on bias handling and harmonic mean.

Explore the k nearest neighbor algorithm for classification, learn its intuition and steps, solve a numerical example, discuss pros and cons, and implement it in Python across four projects.

Explore implementing k-nearest neighbors on a two-class dataset built with make_blobs, using numpy, pandas, and sklearn, and visualize results with two scatter plots.

Generate a two feature dataset with make_blobs, visualize separation via scatter plots. Apply a k-nearest neighbors classifier with standardized features, achieving 100% accuracy on data and 92.5 on challenging data.

Learn to find the optimal K in k-nearest neighbors via an error rate plot from K=1 to 30 and pick the first minimum, where error rate is 1 minus accuracy.

Apply k-nearest neighbors to the Titanic dataset in Google Colab, with data cleaning, dummy encoding, feature scaling, and model selection via error-rate analysis to achieve about 82% accuracy.