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