Artificial Intelligence and Big Data Course

Lesson 1 • Key Roles and Career Paths

Maps professional roles such as data engineer, ML engineer, and AI researcher to required skills. Helps students align course goals with career objectives.

Lesson 2 • AI and Big Data Intersection

Explains how large datasets fuel AI model training and how AI extracts value from Big Data. Connects both fields as mutually reinforcing disciplines.

Lesson 3 • Core Concepts in Big Data

Defines the five Vs of Big Data and explains data velocity, variety, and volume challenges. Grounds students in the scale problems that motivate Big Data infrastructure.

Lesson 4 • History and Evolution of AI

Traces AI from symbolic reasoning to deep learning milestones. Establishes the historical context needed to understand current techniques covered later.

Lesson 1 • Building Scalable Data Pipelines

Teaches batch and streaming ingestion patterns using pipeline orchestration concepts. Students design end-to-end flows that feed AI models reliably at scale.

Lesson 2 • Data Quality and Validation

Covers completeness, consistency, and accuracy checks applied before model training. Directly reduces downstream errors in AI pipelines built in later chapters.

Lesson 3 • Data Sources and Acquisition

Surveys structured, semi-structured, and unstructured data sources including APIs and web scraping. Prepares students to identify and access relevant data for any project.

Lesson 4 • Data Storage Architectures

Compares relational databases, NoSQL stores, data lakes, and data warehouses. Students select appropriate storage based on data type and query patterns.

Lesson 1 • Exploratory Data Analysis

Uses statistical summaries and visualizations to uncover distributions, correlations, and anomalies. Guides feature selection decisions made in subsequent sections.

Lesson 2 • Data Cleaning Techniques

Addresses missing values, outliers, and noise using imputation and filtering methods. Clean data is the prerequisite for all feature engineering steps that follow.

Lesson 3 • Feature Engineering for Big Data

Scales preprocessing workflows to distributed environments using parallel processing frameworks. Bridges single-machine techniques to production-scale Big Data pipelines.

Lesson 4 • Encoding and Transformation

Converts categorical variables and scales numerical features for algorithm compatibility. Ensures features meet the mathematical assumptions of ML models introduced next.

Lesson 1 • Scaling ML to Big Data

Adapts ML workflows to distributed computing frameworks for large-scale training. Connects single-machine ML concepts to the Big Data infrastructure introduced earlier.

Lesson 2 • Supervised Learning Algorithms

Covers regression, decision trees, SVMs, and ensemble methods with their mathematical intuition. Provides the algorithmic toolkit applied to labeled datasets throughout the chapter.

Lesson 3 • Unsupervised Learning Techniques

Introduces clustering, dimensionality reduction, and anomaly detection for unlabeled data. Expands the student's toolkit beyond labeled scenarios covered in supervised learning.

Lesson 4 • Model Training and Optimization

Explains loss functions, gradient descent variants, and hyperparameter tuning strategies. Students configure training loops that converge efficiently on quality solutions.

Lesson 1 • Recurrent and Sequence Models

Introduces RNNs, LSTMs, and GRUs for time-series and natural language tasks. Extends deep learning to sequential dependencies not captured by feedforward networks.

Lesson 2 • Training Deep Networks at Scale

Addresses batch normalization, dropout, learning rate scheduling, and distributed GPU training. Students apply these techniques to stabilize and accelerate large model training.

Lesson 3 • Convolutional Neural Networks

Covers convolution, pooling, and feature map hierarchies for image and spatial data. Builds on neural network basics to handle high-dimensional visual inputs.

Lesson 4 • Neural Network Fundamentals

Explains perceptrons, activation functions, backpropagation, and network topology. Provides the mathematical foundation required for all deep learning architectures ahead.

Lesson 1 • Distributed Computing Principles

Covers MapReduce, fault tolerance, data partitioning, and cluster resource management. Provides the conceptual model underlying all distributed frameworks used in this chapter.

Lesson 2 • Message Queues and Data Streaming

Explains publish-subscribe messaging, topic partitioning, and consumer group patterns. Enables students to connect streaming sources to processing frameworks reliably.

Lesson 3 • Integrating ML with Big Data Frameworks

Combines Spark MLlib and streaming inference to deploy models within distributed pipelines. Unifies the ML and Big Data skills developed across previous chapters.

Lesson 4 • Batch Processing with Spark

Uses Apache Spark's RDD and DataFrame APIs for large-scale batch transformations. Applies distributed computing principles to real data transformation tasks.

Lesson 1 • CI/CD for Machine Learning

Applies continuous integration and delivery pipelines to automate model testing and release. Reduces manual errors and accelerates the path from experiment to production.

Lesson 2 • Model Packaging and Serving

Covers containerization, REST API serving, and model serialization formats for deployment. Transforms trained models into production-ready services accessible by applications.

Lesson 3 • Experiment Tracking and Reproducibility

Uses metadata logging, artifact versioning, and environment management to reproduce results. Supports collaborative model development and regulatory auditability requirements.

Lesson 4 • Model Monitoring and Drift Detection

Tracks data drift, concept drift, and performance degradation using statistical monitoring tools. Ensures deployed models remain accurate as real-world data distributions shift.

Lesson 1 • Emerging Trends in AI and Big Data

Surveys federated learning, neuromorphic computing, and AI-native data platforms. Prepares students to anticipate and evaluate technologies shaping the next decade.

Lesson 2 • Generative AI and Large Language Models

Examines pre-training, fine-tuning, and prompt engineering for large language models. Equips students to leverage and customize generative AI for enterprise applications.

Lesson 3 • Reinforcement Learning Fundamentals

Covers Markov decision processes, reward design, and policy optimization algorithms. Extends supervised learning skills to sequential decision-making and autonomous agents.

Lesson 4 • AI Strategy and Business Alignment

Frames AI investment decisions, ROI measurement, and organizational readiness assessment. Enables students to translate technical capabilities into strategic business value.