Applied ML: A to Z of real-world Data Science

Explore the evolution of data types from raw data to structured and unstructured forms, and examine numerical, categorical, and the ordinal, nominal, interval, and ratio scales.

Explore data preparation with exploratory data analysis and basic preprocessing, covering feature selection, duplicates, missing values, distributions, and normalization or standardization for robust modeling.

Explore advanced data preprocessing techniques for numeric and non-numeric features, including label, one-hot, ordinal, geo encoding, bucketing, hashing, and embeddings, plus feature derivation and label extension.

Balance classes and reduce bias during data sampling for supervised problems, then apply stratified, systematic, or cluster sampling and dimensionality reduction techniques like PCA, LDA, and nonlinear embeddings.

Master supervised learning to predict targets using ground truth labels - human, semi supervised, or silver standards - and apply classification, regression, and time series to real-world use cases.

Explore classification models from traditional parametric and non parametric methods to deep learning, including logistic regression, SVMs, trees, forests, boosting, and transformers; master key evaluation metrics.

Explore regression methods from linear and polynomial models to lasso and principal component regression, evaluate via r-squared metrics, and address bias-variance tradeoff with regularization and ensemble techniques.

Explore time series analysis from moving averages to ARIMA/ARMA, uncover stationarity, decomposition, trend, seasonality, autocorrelation and lag, and compare univariate, multivariate, exogenous variables, and GARCH or deep sequence models.

Use unsupervised learning when you have a new data source or problem without a fixed target. Cluster data, learn embeddings, and reduce dimensions to reveal patterns, anomalies, and domain-spanning similarities.

Explore clustering, an unsupervised technique that groups data by similarity using K-means, GMM, hierarchical or density-based methods, and evaluate with elbow, silhouette, and mutual information.

Explore supervised and unsupervised anomaly detection with fraud and equipment-failure examples. Learn DBSCAN, isolation forest, random cut forest, one-class SVM, and metrics like precision, recall, F1, and local outlier factor.

Explore how recommender systems power search, streaming, and shopping through data space, user and item similarities, and interactions, using content-based and collaborative filtering with embeddings and dimensionality reduction.

Explore reinforcement learning and how agents learn from environment rewards and punishments to act across sequences toward bigger goals, with real-world uses in self-driving cars, games, recommender systems, and robotics.

Explore reinforcement learning by defining the problem and data spaces with an agent, actions, states, rewards, and environment; cover models like MDPs, Q-learning, deep nets, bandits, and exploration-exploitation strategies.

Let data dictate the problem and plan from data exploration through preparation to milestones, enabling data-driven decisions, graph-based designs, and ongoing monitoring for evolving data.

Implement scalable ML pipelines by selecting the right tech stack, data stores, streaming, batch processing, training, and serving across cloud platforms. Emphasize monitoring, versioning, and AutoML versus pre-trained models.

Discover interview tips for data engineering, data science, and ML engineering roles; learn domain focus, foundations, coding rounds, deployment, storytelling, and behavioral skills.

Provide feedback on this end-to-end guideline covering what's involved in a successful data science project and indicate topics you want explored deeper, including areas beyond ML.