Certified AI Engineering Masterclass: From Zero to AI Hero

Learn to write clean pythonic code and build a command line task manager with file-backed storage using os and sys, dictionaries, and modular functions.

Dive into week two data science essentials: numpy for numerical computing, advanced numpy operations, pandas for data manipulation and cleaning, and visualizing with matplotlib and seaborn, plus an eda project.

Explore NumPy for numerical computing in data science and AI, creating and manipulating arrays, performing element-wise and mathematical operations, and reshaping, indexing, and basic hands-on exercises.

Master advanced NumPy operations, including broadcasting across arrays, aggregation functions, boolean indexing and filtering, and random number generation with seeds for reproducible AI workflows.

Master week three mathematics for machine learning, covering linear algebra, calculus, probability, and statistics fundamentals. Apply these concepts through a linear regression from scratch project and optimization, derivatives, and integrals.

Explore probability concepts, including sample space, events, conditional probability, independence, and expectation, variance, standard deviation, then model discrete and continuous random variables with Python using dice rolls, pmf, and pdf.

Explore Gaussian, binomial, Poisson, and uniform distributions and their use in machine learning, including Naive Bayes and logistic regression. Visualize skewness and kurtosis with Python demonstrations.

Master hypothesis testing by formulating null and alternative hypotheses, selecting alpha, and interpreting p values; apply t tests and understand type I and II errors in data science.

Day five introduces t tests, including one-sample, independent two-sample, and paired designs, plus chi square tests for independence and goodness of fit, and ANOVA for three or more groups.

Explore correlation and regression analysis fundamentals, including Pearson and Spearman measures and r squared. Apply Python tools, including numpy, scipy, and scikit-learn, to iris data and regression practice.

Explore machine learning basics and terminology, covering supervised, unsupervised, and reinforcement learning, features and targets, data splits, and real-world applications in healthcare, finance, e-commerce, and autonomous systems.

Explore supervised learning and regression models, focusing on linear regression, training with labeled data, and evaluating with mean square error (MSE) and R2 score, including a hands-on scikit-learn example.

Explore feature engineering and model evaluation in week six. Learn data scaling, normalization, encoding categorical variables, feature selection, creating and transforming features, and cross-validation with hyperparameter tuning.

Explore feature engineering that transforms raw data into meaningful inputs, improving model accuracy and interpretability, with hands-on Titanic dataset exercises using categorical, numerical, and ordinal features.

Learn to create and transform features by deriving date-time components such as day of week, month, and year; build interactions and aggregations; and apply polynomial transformations to model non-linear patterns.

Learn how ensemble learning combines predictions from multiple models to improve accuracy, reduce variance and bias, and boost robustness in fraud detection, medical diagnosis, recommendation systems, and predictive analysis.

Explore bagging and random forests, ensembles that reduce variance with bootstrap sampling and feature randomness, demonstrated on the breast cancer data set for classification.

Learn boosting concepts and gradient boosting, sequentially refining weak learners to reduce bias, tune learning rate and estimators, and compare with random forest on the breast cancer data.

Learn regularization techniques for model optimization, including overfitting and underfitting, with L1, L2, and elastic net. Implement a hands-on exercise on the California housing data to compare performance and coefficients.

Master automated hyperparameter tuning with grid search and randomized search in scikit-learn, using cross-validation to compare gradient boosting and support vector machines on the iris dataset.

Explore deep learning and neural networks, compare them to traditional machine learning, and learn automatic feature extraction, network structure, training basics, and applications across vision, NLP, healthcare, and speech.

Explain loss functions and backpropagation, covering mean squared error and binary cross-entropy, and detail forward and backward passes with gradients guiding learning.