Zero-to-AI-Engineer-Roadmap-and-Resources

A comprehensive roadmap and curated resources for aspiring AI Engineers, covering foundational knowledge, core AI concepts, practical experience, projects and how to prepare for these roles.

Table of Contents

🧰 Foundational Knowledge

- [Linear Algebra]() - [Calculus]() - [Probability Theory]() - [Statistics]() - [Decision Theory]() - [Optimization Theory]() - [Information Theory]()

💻 Computer Science Fundamentals

• Linux, Bash
• [Programming Languages]()

- [Python]() - [R]() - [C/C++]() - [Matlab]() - optional

• [Data Structures and Algorithms]()
• [Object Oriented Programming Concepts(OOPS)]()
• [SQL and Databases(DBMS)]()
• [Computer Networks]()
• [Operating Systems]()
• [Computer Architecture]()
• [Compilers]() - optional

🔥 Data Concepts

• [Data Analysis, Manipulation and Cleaning]()

- Data Cleaning Techniques (Handling Missing Values, Outlier Detection) - Automated EDA Tools - SweetViz - ydata-profiling - DataPrep - AutoViz - D-Tale - dabl - QuickDA - Lux - Data Preprocessing (Normalization, Standardization) - Feature Engineering

• [Data Visualization]()

- Data Visualization Libraries (Matplotlib, Seaborn, plotly, ggplot2) - Dashboarding Tools (Tableau, Power BI) - Open-Source Tools: Apache Superset, Redash

• [Data Engineering]()

- Fundamentals of Data Engineering - ETL (Extract, Transform, Load) Processes - ELT (Extract, Load, Transform) Processes - Data Warehousing Concepts - Batch Processing - Spark/PySpark - Data Pipelines and Workflow Management - Apache Airflow - Deploying Data Pipelines in Production - Real-Time Streaming - Apache Kafka - Cloud Computing - AWS - GCP - Azure - DataOps - Docker - Kubernetes - Modern Data Stack - Dbt - Airbyte - Fivetran - 📚 Recommended Books - [Designing Data-Intensive Applications]() - [Fundamentals of Data Engineering]() - [The Data Warehouse Toolkit]()

• [Big Data Technologies]()

- Fundamentals of Big Data - Databases: Relational Databases: MySQL, PostgreSQL, NoSQL: MongoDB, Cassandra - Hadoop Ecosystem (HDFS, MapReduce, Hive, Pig) - Apache Spark (RDDs, Spark SQL, MLlib) - Distributed Computing Concepts - Data Warehouses (Amazon Redshift, Google BigQuery, Snowflake, Azure Synapse)

• [Distributed Systems & Advanced Distributed Systems]()

• [Time Series Analysis]()

🟢 DeepAI Topics & Concepts

• [Machine Learning]()

- [Supervised Learning]() - [Unsupervised Learning]() - [Semi-Supervised Learning]() - [Reinforcement Learning]() - [Swarn Intelligence (ASI)]()

• [Deep Learning]()

- [Neural Networks]() - [Convolutional Neural Networks (CNNs)]() - [ANNs]() - [Recurrent Neural Networks (RNNs)]()

• [Computer Vision/Image Processing]()
• [Natural Language Processing & Transformers]()
• [Large Language Models(LLMs)]()
• [Robotics]()
• [Reinforcement Learning]()
• [Deep Q-Networks]()

- Applications

Practical Projects

- 300+ Practical Data Science Projects to Practice

Advanced Topics and Specialization

• [Distributed & Parallel Computing]()

Interview Preparation for AI Engineer Roles

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