Hands-On Machine Learning with Python: Real Projects

Learn data cleaning techniques to improve accuracy and reliability of your datasets by handling missing values with imputation, addressing outliers, standardizing formats, removing duplicates, and enriching data for better analysis.

Explore strategies for missing data, including imputation methods (mean, median, regression, multiple imputation), and statistical techniques like maximum likelihood and Bayesian methods to reduce bias.

Explore methods to encode categorical data for machine learning, comparing label, one-hot, ordinal, and target encoding, and examining embeddings and dimensionality reduction techniques for high cardinality variables.

Apply feature scaling to normalize or standardize features, using min-max scaling or z-score transformation, to improve model performance, stability, and convergence.

Load the Titanic dataset, clean data by removing duplicates and handling missing values, apply one-hot encoding to sex and embarked, and standardize age and fare, then print the preprocessed data.

Learn to model a dependent variable with linear regression, fit the regression line by least squares, and evaluate fit with r squared for simple and multiple regression.

Explore logistic regression for predicting categorical outcomes by modeling probabilities with the logistic (sigmoid) function, estimating coefficients, and applying to healthcare, finance, and marketing.

Discover how decision trees, a supervised learning tool for classification and regression, recursively split data using informative features to form root, internal, and leaf nodes, enabling interpretable and robust predictions.

Load the iris data, split features and labels, train logistic regression, a decision tree, and a linear-kernel SVM in Python using scikit-learn, and visualize results with a scatter plot.

Master k-means clustering, an unsupervised algorithm that partitions data into k clusters using centroids, iterative updates until convergence, with applications in customer segmentation, image segmentation, and anomaly detection.

Explore hierarchical clustering, an unsupervised method that builds a dendrogram by iteratively merging closest clusters using distance measures, flexible in cluster count and data types, with Python scikit-learn examples.

Explore principal component analysis to reduce dimensions, project data onto orthogonal components, and reveal key patterns for visualization, feature selection, and anomaly detection using Python and scikit-learn.

Discover association rule learning as a data mining technique that finds frequent item sets and generates rules with minimum support and confidence using the apriori algorithm.

Load the mall customers dataset with pandas, select age, income, and spending score for clustering, apply k-means to three clusters, and use Ward's method with PCA for dendrogram visualization.

Cross-validation evaluates a model's performance and generalization by repeatedly training on subsets and validating on held-out folds, including k-fold and leave-one-out approaches.

Explore how evaluation metrics measure model performance across domains, including accuracy for binary classification, precision, recall, and the F1 score, plus regression metrics like MSE and r-squared.

Explore hyperparameter tuning to optimize model performance using grid search, random search, Bayesian optimization, and cross-validation across tasks such as logistic regression in Python.

Choose the best model for your data by balancing bias and variance, using cross-validation and metrics such as accuracy, precision, recall, F1, and MSE, with regularization and computational resource considerations.

Load the iris dataset, split 70/30, and train a random forest with five-fold cross-validation, then evaluate accuracy, precision, recall, and F1 score; tune with gridsearchcv and compare to SVM.

Explore neural networks, from neurons and layers to training with backpropagation, and compare feedforward and recurrent architectures for tasks like language modeling and image recognition.

Learn TensorFlow basics, including tensors, CPU and GPU computations, and the Keras API for building neural networks. Install, implement a simple model, and visualize training with TensorBoard.

Build and train neural networks in TensorFlow, understanding layers, neurons, weights, activation functions. Create a model with input, hidden, and output layers, and train it with softmax and categorical cross-entropy.

Explore how convolutional neural networks learn features from images through convolutional and pooling layers, enabling accurate image classification, object detection, and segmentation.

Load the mNIST data with TensorFlow Keras, normalize and one hot encode; build a CNN with conv2d, 32 filters, 3x3 kernel, max pooling, dense 64, and softmax; train, evaluate, tune.

Apply tokenization, case normalization, and stemming and lemmatization to transform raw text into clean data for NLP and machine learning. Remove stopwords and non-textual elements to refine text.

Master the bag of words model, a simple yet powerful natural language processing approach that converts documents into a word-frequency document-term matrix for classification, sentiment analysis, and information retrieval.

Explore word embeddings: dense vectors that encode semantic and syntactic relationships, powering NLP tasks like text classification, sentiment analysis, and translation. Learn skip-gram and cbow training and word analogies.

Explore named entity recognition in language processing to identify and classify people, organizations, locations, and dates using rule-based and machine learning models, with context in unstructured data to improve accuracy.

Preprocess text data by tokenizing, lowercasing, and removing punctuation. Create a bag of words, generate word embeddings with word2vec, and use spaCy for named entity recognition on Amazon and Seattle.

Validate a trained model's performance on test data sets, package it for deployment, provision scalable infrastructure, and implement monitoring, governance, and security for reliable production predictions.

Explore building web applications with Flask, learning to set up a development environment, define routes and views, handle HTTP requests, render Jinja2 templates, and integrate databases with SQLAlchemy.

Explore scalability and production readiness, including vertical and horizontal scaling, caching, load balancing, modular architecture, monitoring, secure deployment practices, and regulatory compliance in real applications.

Explore monitoring and maintenance principles that prevent breakdowns and extend equipment life. Learn preventive, predictive, and corrective maintenance, plus condition monitoring sensors, predictive analytics, and CMS.