AI & Python Development Megaclass - 300+ Hands-on Projects

Master week two of data science essentials by exploring numpy, pandas, and visualization with matplotlib and seaborn, plus an exploratory data analysis project.

Master NumPy for numerical computing by creating and manipulating arrays, performing element-wise and mathematical operations, and reshaping data for AI and data science workflows.

Master advanced NumPy operations, including broadcasting, aggregation, boolean indexing, and random data generation with seeds for reproducibility across arrays and matrices.

Explore pandas basics for data manipulation with series and data frames. Import data from csv, Excel, or dictionaries, save results, and perform core operations like head, describe, and filtering.

Explore data cleaning and preparation with pandas, including handling missing values, data transformation, renaming columns, and merging data frames using concat, merge, and join in hands-on exercises.

Learn to visualize data with matplotlib and seaborn by creating line, bar, scatter plots and histograms, customizing with titles and labels, plus heat maps and pair plots.

Engage in day seven EDA by cleaning, transforming, and aggregating data; uncover patterns with matplotlib and seaborn, generate summary statistics, and explore Titanic survival by class.

Explore week three mathematics for machine learning, covering linear algebra fundamentals and advanced concepts, calculus with derivatives and integrals, probability, statistics, and a linear regression from scratch mini project.

Explore linear algebra fundamentals, including vectors and matrices, and practice element-wise operations, scalar and matrix multiplication, and identity, zero, and diagonal matrices with numpy.

Explore advanced linear algebra concepts, compute determinants and inverses, analyze eigenvalues and eigenvectors, and apply singular value decomposition with NumPy in Python for AI workflows.

Explore derivatives, partial derivatives, and gradients, and apply gradient descent with Python and SymPy to optimize loss functions in machine learning, including linear regression.

Explore integrals and optimization in machine learning, including definite and indefinite integrals, probability density functions, cost functions, Bayesian weighting, and SGD variants with Python examples.

Explore probability basics, conditional probability and Bayes theorem, and implement these ideas in Python while modeling Gaussian, Bernoulli, binomial, and Poisson distributions and their machine learning applications.

Explore statistics fundamentals: central tendency and dispersion with mean, median, mode, variance, and standard deviation. Learn hypothesis testing, confidence intervals, and t tests with numpy and scipy to interpret data.

Build a linear regression model from scratch using gradient descent, including core math like y_hat = x theta, and evaluate mean squared error and r squared on synthetic data.

Learn probability theory, random variables, and distributions for machine learning, then master statistical inference, estimation, confidence intervals, and hypothesis testing, including correlation and regression analysis, and a real-world data project.

Explore probability foundations, including sample space, events, conditional probability, and independence, then model discrete and continuous random variables with PMF, PDF, and moments using Python and dice examples.

Explore common probability distributions used in machine learning, including Gaussian, binomial, Poisson, and uniform, visualize their properties and variations, and review key ML applications.

Learn statistical inference by estimating population parameters from samples, using point and interval estimates, and constructing 95% confidence intervals with Python.

Learn to perform hypothesis testing and interpret p-values, distinguishing null and alternative hypotheses, choosing alpha, and applying one- and two-sample t tests.

Day five of the AI and Python development megaclass covers hypothesis tests, including t tests, chi square, and anova, with practical Python examples of one-sample, two-sample, and paired t tests.

Explore correlation and regression analysis in Python, including Pearson and Spearman metrics, linear regression concepts, r squared, slope and intercept, and hands-on exercises with iris data.

Perform exploratory data analysis, hypothesis testing (t-tests, chi-square), and linear regression on real-world data using the tips dataset. Visualize distributions and interpret regression metrics.

Explore machine learning basics and terminology, supervised learning, regression (polynomial and regularization), classification with logistic regression, model evaluation with cross-validation, and the KNN algorithm through a supervised learning mini project.

Explore machine learning basics and terminology, including supervised, unsupervised, and reinforcement learning, with hands-on exercises in features, target, training, testing, and visualization.

Explore supervised learning and regression models using labeled data to predict unseen outputs. Implement linear regression, cost functions, and evaluation with mse and r-squared.

Master polynomial regression to model non-linear relationships and apply ridge and lasso regularization to prevent overfitting, using Python and scikit-learn on California housing data.

Explore classification and logistic regression, including binary, multi-class, and multi-label setups, with use cases in healthcare, fraud detection, churn, and sentiment analysis. See logistic regression via sigmoid and thresholds.

Explore regression and classification evaluation metrics, including MSE, Mae, and RMSE, and learn cross validation techniques such as K-fold cross validation, stratified k fold, and Loocv through iris data experiments.

Explore the k-nearest neighbors algorithm and its use for classification and regression, including distance metrics, feature scaling, and hands-on implementation with iris data and model comparisons.

Engage in an end-to-end supervised learning mini project that combines regression and classification models, using EDA, preprocessing, cross-validation, and model evaluation on California housing 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 in machine learning by transforming data into meaningful inputs, handling categorical, numerical, and ordinal features with one-hot encoding, scaling, and feature selection, using the Titanic dataset.

Day two covers data scaling and normalization as essential pre-processing, showing min-max scaling and standardization on the iris dataset with a KNN classifier, comparing performance before and after scaling.

Explore one hot encoding and label encoding for categorical features. Apply frequency encoding and target encoding for high cardinality features, and compare their effect on Titanic model performance.

Explore feature selection techniques across filter, wrapper, and embedded methods, using correlation and mutual information, then apply a random forest to diabetes data for feature importance.

Develop feature creation and transformation techniques by deriving date-based features (day of week, month, year), applying polynomial transformation, and comparing model performance on a bike sharing dataset.

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

Apply end-to-end machine learning workflow with cross-validation, feature engineering through scaling, encoding, and selection, and grid-search hyperparameter tuning to improve a Titanic survival model using logistic regression and random forest.

Explore advanced machine learning algorithms through ensemble learning, including bagging, random forests, boosting, gradient boosting, XGBoost, LightGBM, and CatBoost, and apply them to a real data ensemble project.

Explore ensemble learning, combining multiple models to reduce variance and bias via bagging, boosting, stacking, and voting classifiers, with a hands-on iris dataset exercise.

Understand bagging and random forests, reducing variance via bootstrap sampling and feature randomness. Apply a random forest to breast cancer data, tune n estimators, max depth, max features, and evaluate accuracy.

Learn boosting and gradient boosting with theory and hands-on practice, tune learning rate and estimators, and compare gradient boosting to random forest on the breast cancer dataset.

Explore how XGBoost speeds gradient boosting with parallel processing, missing-data handling, and L1/L2 regularization, then tune hyperparameters via grid search and compare with gradient boosting on binary classification data.

Explore Lightgbm and Catboost in a hands-on Titanic dataset exercise, compare gradient boosting frameworks, and learn when to use GPU training, histogram-based splitting, and native categorical handling.

Explore imbalanced data challenges, including bias toward the majority class, and learn to apply SMOTE oversampling, undersampling, and class weights for fraud detection, with metrics like F1 and ROC AUC.

Train and compare multiple ensemble models on a real churn dataset, analyze bagging versus boosting, and balance data with smote while evaluating with roc auc and f1.

Explore week eight of model tuning and optimization, covering hyperparameter tuning, grid and random search, Bayesian optimization, regularization, cross-validation, and automated tuning with GridSearchCV and RandomizedSearchCV.

Explore the difference between parameters and hyperparameters, and see how tuning choices like learning rate, max depth, and n_estimators affect model performance on the iris dataset with a random forest.

Explore grid search and random search for hyperparameter tuning, compare their efficiency on the iris dataset, and learn practical guidelines for selecting search ranges and parameters.

Explore Bayesian optimization for hyperparameter tuning with Optuna, balancing exploration and exploitation to tune an XGBoost model on the breast cancer dataset, and compare with grid and random search.

Explore overfitting and underfitting and apply L1, L2, and elastic net regularization to a linear regression on the California housing data, comparing MSE and coefficients.

Learn cross-validation techniques, including k-fold and stratified k-fold, for robust model evaluation on imbalanced data, with hands-on credit card fraud analysis and hyperparameter tuning.

Explore automated hyperparameter tuning with GridSearchCV and RandomizedSearchCV, integrating cross-validation to optimize gradient boosting and support vector machine models on the iris dataset.

Apply end-to-end optimization techniques: data preprocessing, feature engineering, regularization, and hyperparameter tuning with randomized and grid search, using cross-validation to build and evaluate a final churn model.

Explore week nine neural networks and deep learning fundamentals, covering forward propagation, activation functions, loss and backpropagation, gradient descent, and building with TensorFlow, Keras, and PyTorch for CIFAR-10 image classification.

Introduce deep learning and neural networks, contrast with machine learning, and practice automatic feature extraction, forward and backpropagation with gradient descent using TensorFlow and PyTorch on mnist, cifar-10, imagenet.

Explore forward propagation and activation functions in a practical, hands-on neural network, implementing a forward pass in Python with sigmoid, tanh, ReLU, and softmax, and visualizing their behavior.

Explore loss functions and backpropagation, including mean squared error and cross-entropy, and learn how gradients guide weight updates through forward and backward passes.

Master gradient descent and its variants from batch, stochastic, to mini-batch, and compare optimizers like Adagrad, Rmsprop, and Adam in TensorFlow and PyTorch exercises.

Build, train, compile, evaluate, and save a neural network with TensorFlow and Keras, featuring convolutional layers, dropout, and softmax on the MNIST digit dataset.

Build and train neural networks with PyTorch, using tensors, Autograd, nn.Module, cross-entropy loss, and Adam, then load MNIST data, evaluate accuracy, and save or load the model.

Build, train, and optimize a convolutional neural network for CIFAR-10 image classification, with preprocessing, dropout, hyperparameters, and optimizers to improve accuracy.

Introduce convolutional neural networks (CNNs), cover convolutional layers and filters, pooling and dimensionality reduction, build CNNs with Keras, TensorFlow, PyTorch, and image classification project on fashion mNIST or CIFAR ten.

Explore convolutional neural networks for image processing, including convolutional layers, pooling, and fully connected classifiers, with hands-on TensorFlow and PyTorch setup using the CIFAR-10 dataset.

Explore convolutional operations, filters, and feature maps to understand edge detection and feature extraction. Implement and visualize these concepts with TensorFlow and PyTorch, adjusting kernel size, stride, and padding.

Explore pooling layers and dimensionality reduction in CNNs, learning how max pooling and average pooling downsample feature maps while preserving key information. Practice implementing these techniques with TensorFlow and PyTorch.

Build, compile, train, and evaluate CNN architectures with Keras and TensorFlow, using convolutional, pooling, and dense layers with dropout for image classification on CIFAR-10 and MNIST.

Learn to build convolutional neural network architectures with PyTorch using nn modules, define convolutional, pooling, and fully connected layers, perform forward passes, train, evaluate, and tune hyperparameters on CIFAR-10.

Explore overfitting in CNNs and apply regularization and data augmentation to improve generalization, using dropout, batch normalization, and image transformations like rotation, flipping, scaling, and cropping in a TensorFlow hands-on.

Build and optimize a CNN for CIFAR-10 or Fashion-MNIST image classification, applying deeper architectures, regularization, and augmentation to improve accuracy and generalization, and analyze performance with loss and confusion matrix.