Machine Learning Masterclass

explores essential math symbols for machine learning, optimization, and data science, covering limits, derivatives, gradients, Hessians, integrals, transforms, vectors, matrices, probability, and KL divergence.

Master essential math symbols for data analysis, including mu, sigma, variance, covariance, rho, and distributions, with arg max and arg min, set operations, and real spaces.

Explore absolute value, distance, floor and ceiling functions, infinity, isomorphism, and algorithmic complexity notations: Big-O, Theta, and Omega, for limits, rounding, and growth in computational contexts.

Learn how machine learning builds models by learning from data through supervised, unsupervised, and reinforcement techniques, with deep neural networks enabling speech, image, and language tasks, training, and evaluation.

Explore the history of machine learning from the perceptron and the AI winter to modern deep learning and reinforcement learning breakthroughs like td-gammon, AlphaGo, and large language models.

Explore how machine learning powers real-world impact across healthcare, finance, retail, manufacturing, and beyond, from disease detection and drug discovery to fraud protection, forecasting, and personalized experiences.

Explore supervised, unsupervised, and reinforcement learning, and learn when to apply each to tasks like image classification, clustering, and autonomous vehicles, using labeled data, unlabeled data, and rewards.

Explore the machine learning pipeline from data sources to deployment, detailing data ingestion, preprocessing, cleaning, feature engineering, feature selection, train-test split, model training, validation, tuning, evaluation, deployment, serving, and monitoring.

Master essential Python libraries for machine learning, including scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, CatBoost, NumPy, Pandas, SciPy, Dask, Keras, fastai, and Jax.

Explore vectors and vector operations, including magnitude, direction, addition, subtraction, scalar multiplication, dot product, and cosine similarity, and see their role in data representation and gradients in machine learning.

Explore eigenvalues and eigenvectors and how a matrix transforms space. Learn to compute them with the characteristic equation and applications in PCA, quantum mechanics, control systems, and Markov chains.

Explore the fundamentals of functions, including domain and range, and injective, surjective, and bijective mappings. See how these ideas support feature transformation and loss functions in machine learning.

Master derivatives from power, product, quotient, and chain rules; apply to gradient descent, backpropagation, and activation functions in machine learning.

Explore the foundations of probability theory, including experiments, sample spaces, events, and probability axioms. Learn about classical, frequentist, and subjective interpretations, conditional probability, independence, distributions, Bayes' theorem, and random variables.

Explore discrete probability distributions such as binomial, Poisson, and geometric, and continuous distributions including normal, exponential, gamma, beta, uniform, and Weibull, with real-world examples in quality control, arrivals, and reliability.

Explore Bayes' theorem as a framework for updating beliefs with new evidence, blending priors and likelihood into the posterior, with examples in spam filtering, medical tests, and machine learning.

Form null and alternative hypotheses, set alpha, compute a test statistic, and decide to reject H0 using p-values across z, t, anova, chi-square, and nonparametric tests.

Master gradient descent, an optimization method that minimizes the cost function (mean squared error or cross-entropy loss) by iteratively updating theta with learning rate alpha toward convergence.

Rmsprop accelerates and stabilizes training of neural networks by adapting per-parameter learning rates via an exponentially decaying average of squared gradients, addressing non-stationary objectives and preventing exploding or vanishing gradients.

Adapting the learning rate for each parameter by accumulating squared gradients, Adagrad enables effective updates for sparse features and shrinking updates as training converges.

Explore the Adagrad update in Python using numpy, accumulating squared gradients to adapt learning rates per parameter, with epsilon for numerical stability and a step-by-step update example.

Apply gradient descent to a linear regression problem in Python, using simulated data, a mean squared error cost, and iterative weight updates. Explore learning rate and iteration effects on convergence.

Explore stochastic gradient descent, unlike batch gradient descent, updating weights with a single random data point per iteration for faster learning on large datasets, with noisier but converging cost curves.

Master mini-batch gradient descent by updating weights with shuffled mini-batches, achieving faster, less noisy convergence than batch or stochastic methods and better handling saddle points for large datasets.

Explore Python, an interpreted, easy-to-read programming language used in data science, AI, web development, and automation, with a vast standard library, cross-platform portability, and a thriving open source community.

Master Anaconda with Conda and libraries like NumPy, pandas, and scikit-learn, manage Python environments, use Jupyter notebooks for interactive analysis and teaching, and connect them with Visual Studio Code extensions.

Explore Google Colab’s browser-based Python execution with free GPU and TPU access, Google Drive integration, and compare it to VSCode, Anaconda, and Jupyter for machine learning workflows.

Install Python, Anaconda, and Visual Studio Code using stable releases, configure Python to path, verify with Python version, and set up the Python extension in Visual Studio Code.

Explore Python syntax and basic operations, including variables, data types, type conversions, and dynamic typing, with arithmetic, comparison, and logical operators, plus operator precedence.

Explore data structures in Python, focusing on lists, tuples, and sets to organize and manipulate data efficiently, noting that lists are mutable, tuples are immutable, and sets store unique items.

Learn Python control structures, including conditional statements (if, elif, else) and loops (for, while) with range, nesting, and break and continue, to write clear, efficient code.

Explore functions and basic functional programming in Python, covering def definitions, higher-order functions, lambda, and data transformations with map, filter, and reduce to improve readability and reusability.

Explore intermediate functions such as recursion, tail recursion, currying, partial functions, closures with state, decorators, and generators for modular, memory management in Python code.

Master Python dictionaries, a flexible key-value data structure with unique, immutable keys. Learn to access, add, update, remove, iterate, and build dictionaries and comprehensions, including swapping keys and values.

Learn to organize Python code with modules and packages, import libraries like numpy and pandas, and apply core data operations including handling missing data with dropna and fillna.

Explore Python file handling, including opening, reading, writing, and closing files, with text, csv, and pandas workflows for data loading, filtering, and exporting.

Explore exception handling in Python to build robust code that gracefully manages errors with try, except, else, and finally, including custom exceptions, input validation, and zero division handling.

Explore object oriented programming in Python, mastering encapsulation, inheritance, polymorphism, and the class–object relationship, including attributes, methods, constructors, and multiple inheritance.

Explore advanced Python list operations, including list comprehensions, enumerate, and zip, to create, filter, and pair data succinctly and write cleaner, more efficient code.

Improve data quality by profiling, cleaning, and validating data to ensure accuracy, completeness, consistency, timeliness, and relevance.

Learn data cleaning techniques to fix missing values, duplicates, outliers, and inconsistencies; standardize formats, validate data, reduce noise, and build repeatable preprocessing pipelines for reliable analytics.

Learn to handle missing values in data preprocessing using deletion and imputation, from mean/median/mode to knn imputation and advanced methods, with flagging.

Identify outliers and global, contextual, collective types; use boxplot, histogram, and scatter plots with z-score and interquartile range, plus DBscan and isolation forest, to decide removal, transformation, capping, or imputation.

Explore feature scaling and normalization to ensure equal feature contributions and faster convergence for scale-sensitive algorithms, using min-max, standardization, robust scaling, and L1/L2 normalization.

Standardization transforms data to zero mean and standard deviation equal to one, ensuring equal feature contribution and faster convergence for models like logistic regression, SVM, and neural networks.

Encode categorical data into numeric form to enable machine learning models to learn effectively. Explore nominal versus ordinal variables and techniques such as label, one-hot, binary, target, and frequency encoding.

Feature engineering transforms raw data into features to improve model performance, guiding problem understanding, EDA, feature construction, transformation, selection, and encoding across text data, time series data, and structured data.

Master dimensionality reduction by understanding when to apply PCA, LDA, t-SNE, and autoencoders to reduce features while preserving variance and enabling visualization, with attention to information loss and computational costs.

Explore descriptive statistics to summarize data, covering data types from nominal to ratio, central tendency (mean, median, mode), variability (range, variance, standard deviation, iqr), and shape (skewness, kurtosis).

Master Python data visualization with matplotlib and seaborn to create line plots, bar charts, and histograms, and learn customization and palettes for clear graphics.

Explore multivariate statistical analysis, including multivariate regression, principal component analysis, factor analysis, cluster analysis, discriminant analysis, canonical correlation, and MANOVA, with practical steps and applications.

Examine how wages vary over time and by education level using group-by means and boxplots to reveal a flat trend 2003–2009 and higher, more variable salaries with higher education.

Treat stock moves as a classification task using five years of SP 500 data with lag one to lag five, showing direction is not easily predictable and needs additional features.

Discover how learning in machine learning uses features like TV, radio, and newspaper spending to predict sales with linear regression, then explore nonlinear patterns via polynomial regression.

Explore why predicting f matters by linking y to x, minimize avoidable error, and use inference to understand how inputs influence outcomes in real-world data.

Discover how the curse of dimensionality makes nearest-neighbor estimates unstable in high-dimensional data, and why using multiple nearby points or dimensionality reduction stabilizes estimates.

Explore how to predict f from training data using parametric and non-parametric methods, including linear models, feature inputs, and the tradeoffs of underfitting and overfitting.

Explore the trade-off between prediction accuracy and model simplicity, comparing linear models with flexible approaches like support vector machines and neural networks, and learning when interpretability matters.

Explore the core distinction between regression and classification in machine learning, including when to predict continuous values versus category labels, and how numeric and categorical variables influence models.