Certified AI Engineering Masterclass: From Zero to AI Hero

Explore why Python powers AI and supports machine learning. Set up Python on Mac and Windows, install Jupyter and VS Code, and learn basic syntax and data types.

Master control flow in Python with conditional statements and loops. Build skills using if, elif, else, for and while, plus break and continue, via prime checks and a menu calculator.

Master defining reusable Python functions with def, handling parameters and return values, and understanding local and global scope; build and import modules, and create custom modules with factorial.

Explore lists, tuples, dictionaries, and sets in Python, mastering creation, access, modification, and common operations like slicing, indexing, and set unions, intersections, and differences.

Explore string manipulation in Python: concatenation, slicing, and formatting with f-strings. Master common methods like split, join, replace, strip; apply regular expressions for pattern matching, palindrome checks, and text cleaning.

Master python file handling by opening files with built-in open, using read, read line, read lines, and write, write lines, with statements for safe closure, and robust exception handling.

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 pandas for data manipulation with series and data frames, loading data from csv or excel, and performing viewing, describing, selecting, filtering, and saving operations.

Learn data cleaning and preparation with pandas, handling missing values and data transformation. Merge, transform, and rename columns to produce clean, analysis-ready datasets.

Explore data aggregation and grouping in pandas using group by, pivot tables, and aggregation functions like mean, max, and min, with custom functions and grouped summary statistics.

Master data visualization with matplotlib and seaborn, building line plots, bar charts, scatter plots, and histograms, with customization and hands-on exercises using iris data.

Explore a day seven eda project that guides data cleaning, missing values, duplicates, transformation, aggregation, and visualization with matplotlib and seaborn to uncover patterns, trends, and correlations in Titanic data.

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 vectors and matrices, perform element-wise and matrix operations with numpy, and study identity, zero, and diagonal matrices through hands-on exercises including determinant and inverse.

Master advanced linear algebra concepts, including determinants, inverses, eigenvalues and eigenvectors, and matrix decomposition with singular value decomposition, using practical NumPy implementations in Python.

Explore derivatives and their role in optimization for machine learning, covering first and partial derivatives, gradients, and gradient descent with Python and SymPy.

Master integrals and optimization for machine learning, covering definite and indefinite integrals, probability distributions, regularization terms, convexity and non-convexity, and SGD-based loss optimization with practical Python examples.

Explore probability basics, conditional probability, and Bayes' theorem, then dive into gaussian, bernoulli, binomial, and poisson distributions with Python demonstrations and machine learning applications.

Explore central tendency and dispersion with mean, median, mode, variance, and standard deviation in Python, then apply hypothesis testing, including two-sample t tests and confidence intervals.

Apply linear algebra, calculus, and statistics to build a linear regression model from scratch, optimize with gradient descent, and evaluate with mean squared error and r-squared.

Master week four of the certified ai engineering masterclass by exploring probability theory, random variables, distributions, estimation, confidence intervals, hypothesis testing, p values, correlation, and regression on real-world data.

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.

Explore statistical inference by estimating population parameters with point and interval estimates, using mean and proportion confidence intervals, t- and z-based methods, plus hands-on Python examples.

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.

Apply probability concepts to real-world data through a statistical analysis project, performing exploratory data analysis, hypothesis testing, and linear regression on the tips dataset with visualizations.

Master machine learning fundamentals and terminology, including supervised learning, regression, polynomial regression, regularization, and classification with logistic regression. Evaluate models with cross-validation and explore KNN in a practical mini project.

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 polynomial regression to model non-linear relationships using higher-degree features, then apply ridge and lasso regularization to curb overfitting and evaluate with mean squared error on California housing data.

Explore classification and binary logistic regression, using the sigmoid function to map inputs to probabilities and define decision boundaries. Apply to real-world use cases in healthcare, finance, retail, and NLP.

Understand regression and classification evaluation metrics—MSE, MAE, RMSE, accuracy, precision, recall, and F1—and apply k-fold and stratified cross-validation with confusion matrices on Iris data using random forest and logistic regression.

Explore the k-nearest neighbors (k-NN) algorithm for classification and regression, including distance metrics, feature scaling, choosing k, and hands-on implementation with the iris dataset and comparisons to logistic regression.

Build an end-to-end supervised learning mini project applying regression and classification models with data preparation, exploratory data analysis, preprocessing, and evaluation on California housing data set and telco churn data.

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.

Explore data scaling and normalization to boost machine learning performance, applying min max scaling and standardization on the iris data set to compare KNN, SVM, and other models.

Explore encoding categorical variables with one hot and label encoding, and address high cardinality via frequency and target encoding. Apply these methods to the Titanic dataset and compare model performance.

Explore feature selection techniques to identify relevant features using filter, wrapper, and embedded methods, applying correlation, mutual information, and tree-based models like random forest on the diabetes dataset.

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.

Explore regression and classification evaluation techniques, including MAE, MSE, RMSE, R squared, accuracy, precision, recall, F1, and roc AUC, with hands-on exercises using iris and California housing datasets.

Master cross-validation, grid search, random search, and feature engineering with scaling, encoding, and selection on Titanic data for logistic regression and random forest.

Advance through ensemble learning by examining bagging, boosting, and gradient boosting, plus XGBoost, LightGBM, and CatBoost, then tackle imbalanced data and compare models on real data.

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.

Explore XGBoost, the fast enhancement of gradient boosting that handles missing data and uses regularization; train, tune with grid search, and compare with gradient boosting on the breast cancer dataset.

Compare Lightgbm, Catboost, and XGBoost with a hands-on Titanic dataset exercise, highlighting histogram-based splitting, leaf-wise growth, and native categorical handling. Catboost shows higher accuracy in the example.

Explore imbalanced data challenges in classification and apply oversampling (smote), undersampling, and class weights, then evaluate with f1 score, roc auc, and precision-recall curves on fraud detection data.

Compare bagging and boosting ensemble methods on a real churn dataset, training random forest, xgboost, and lightgbm, and evaluate with metrics like f1 score and roc auc.

Explore week eight of model tuning and optimization, mastering hyperparameter tuning, grid and random search, Bayesian optimization, regularization, cross validation, model evaluation, and automated tuning with gridsearchcv and Randomizedsearchcv.

Understand parameters versus hyperparameters and why tuning boosts model generalization and efficiency. Practice with a hands-on random forest on the iris dataset, comparing default and tuned hyperparameters and evaluating accuracy.

Learn to use grid search and random search for hyperparameter tuning, compare exhaustiveness and efficiency, and apply cross-validated searches to a random forest on the iris dataset.

Bayesian optimization with Optuna tunes an XGBoost model and compares grid search and random search on the breast cancer dataset.

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.

Explore cross-validation and model evaluation techniques, including k-fold, stratified k-fold, and leave-one-out, to handle imbalanced data and improve hyperparameter tuning and robust performance estimates.

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.

Build and tune a final churn model through end-to-end optimization: data pre-processing, feature engineering, regularization, cross-validation, and hyperparameter tuning with randomized search CV; evaluate with classification metrics and cross-validation.

Explore week nine neural networks and deep learning fundamentals, including forward propagation, activation functions, loss and backpropagation, gradient descent, and hands-on TensorFlow, Keras, and PyTorch projects on CIFAR ten.

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.

Examine forward propagation across layers, computing weighted sums with biases and applying activation functions. Compare sigmoid, tanh, ReLU, and softmax, with a hands-on Python exercise.

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

Explore gradient descent and its variants, including batch, stochastic, and mini-batch, plus Adagrad, RMSprop, and Adam, with hands-on TensorFlow and PyTorch exercises.

Learn to build and train neural networks with TensorFlow and Keras, using sequential and functional APIs, dense and dropout layers, CNNs, and model saving for MNIST digit classification.

Explore building neural networks with PyTorch, introducing tensors, Autograd, and nn.Module, then design, train, and evaluate a MNIST classifier using cross-entropy loss and the Adam optimizer, and save the model.

Build, train, and optimize a CIFAR-10 image classification neural network using convolutional layers, normalization, and one-hot encoding, and tune hyperparameters with Adam, SGD, and RMSProp.