Introduction to Artificial Intelligence, covering core definitions, the AI spectrum (ANI, AGI, ASI), history, key subfields, and foundational ethical principles

Explanation of the types of AI. Understand the critical differences between Narrow AI (ANI), General AI (AGI), and Superintelligence (ASI).

Clarify the distinctions between AI, Machine Learning, and Deep Learning. Understand the hierarchy, applications, and trade-offs.

Explore the dynamic AI industry landscape, key market trends, and diverse career opportunities. A comprehensive guide for students and professionals.

AI ethics and responsible development. Analyze and mitigate bias, implement fairness, and build accountable AI systems using modern frameworks and tools.

Vector fundamentals for AI engineering. Learn to implement vector addition, dot products, and cosine similarity in Python with NumPy.

Vector spaces, linear independence, and basis in AI. Learn the mathematical foundations and Python for dimensionality reduction and feature engineering.

Fundamental matrix operations in AI. Learn matrix multiplication, addition, and transpose with Python/NumPy examples and real-world applications.

Advanced matrix decomposition (LU, QR, Cholesky) for AI and machine learning. Learn theory, Python implementation, and real-world applications.

Master eigenvalues, eigenvectors, and Principal Component Analysis (PCA). This chapter covers the theory, practical Python implementation.

Calculus foundations of AI, covering derivatives, partial derivatives, the chain rule, and gradient descent with Python examples in TensorFlow.

Multivariable calculus for AI. Learn to implement Gradient Descent, understand optimization landscapes, and use Python to train ML models.

Introduction to probability theory for AI engineering. Learn core concepts, distributions, and Python implementation for machine learning.

Descriptive statistics and hypothesis testing for AI engineers. Learn to analyze data, run t-tests, and use Python for statistical inference.

Bayesian statistics in AI. Learn to implement Bayesian inference, quantify uncertainty, and build probabilistic models in Python using PyMC.

Detailed chapter on Information Theory in AI. Learn to implement and apply entropy, cross-entropy, and KL divergence in Python for machine learning.

Convex optimization, Lagrange multipliers, and KKT conditions for AI. Learn the theory and implement solutions for SVMs and other ML models in Python.

Master data structures, algorithms, OOP, and functional programming of python to build high-performance, scalable AI systems.

NumPy for AI and machine learning. This chapter covers array operations, broadcasting, vectorization, and advanced indexing with practical Python examples.

Pandas for AI and ML Applications. Learn to use DataFrames and Series for data cleaning, transformation, aggregation, and preparation for ML pipelines.

Data visualization for AI. Learn to implement, analyze, and design effective plots for exploratory data analysis and model interpretation.

Learn to use Jupyter, VS Code, Git, and Poetry for reproducible and scalable machine learning projects for AI Engineering.

Learn to gather high-quality data using APIs, web scraping with Python, and database queries. Includes practical code examples and industry best practices.

Data quality assessment and profiling for AI. Learn to implement systematic data validation and profiling using Python, Great Expectations & other tools.

Data privacy in AI. Learn to implement GDPR and CCPA compliance, use anonymization, differential privacy, and federated learning.

Design and implement real-time data streaming architectures for machine learning using Apache Kafka and Apache Storm, with practical Python examples.

Handling missing data in AI engineering. Learn theoretical foundations (MCAR, MAR, MNAR) and practical data cleaning techniques.

Outlier detection and treatment for AI. Learn statistical methods, Isolation Forest, and DBSCAN with Python examples to build robust machine learning models.

Automated data validation and quality assurance pipelines. Learn to use Pandera and Great Expectations to prevent bad data from impacting model performance.

Automated data quality monitoring systems for production ML pipelines. Learn to detect data drift, validate schemas, and build robust MLOps workflows.

Learn to create polynomial features, interaction terms, and temporal features using Python, scikit-learn, and pandas to improve model performance.

Learn statistical and model-based methods like RFE and Lasso, and implement them in Python with scikit-learn to improve model performance.

Learn to implement Deep Feature Synthesis with Featuretools, understand genetic programming, and deploy scalable ML pipelines.

Enterprise feature stores. Learn the architecture, implementation strategies, and business value of centralizing feature management for ML.

Essential data transformation techniques in AI engineering. Learn to implement scaling, normalization, and encoding with Python, scikit-learn.

Master one-hot, ordinal, and target encoding with Python, scikit-learn, and real-world examples to handle high-cardinality data in machine learning.

Dimensionality reduction with PCA and t-SNE. Mathematical foundations, Python implementation, and industry applications for AI engineering and data science.

UMAP and autoencoders using Python, TensorFlow, and umap-learn for data visualization, feature engineering, and generative modeling.

Exploratory Data Analysis (EDA) for AI engineers. Learn to analyze data distributions, correlations, and anomalies to build better machine learning models.

Advanced EDA, covering dimensionality reduction (t-SNE, UMAP), interactive plots, and multidimensional visualization for discovering patterns in complex data.

Robust data pipelines for machine learning. Learn to build scalable batch and real-time ML workflows using Python, Airflow, and modern data engineering tools.

data versioning and experiment tracking in AI engineering. Learn the principles of DVC and MLflow to ensure reproducible and scalable ML systems.

Comparison of ETL and ELT data pipeline patterns. Learn the architecture, trade-offs, and practical implementation with Spark, BigQuery, and dbt.