AI Engineer Roadmap
Your Guide to Becoming an AI Expert
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Python
- Variables and basic data types
- Loops, and functions
- Error handling and exceptions
- File input/output operations
- Using Python modules and libraries
- Python classes and objects
- Python virtual environments
- Basic debugging techniques in Python
- 9h 15m
Github
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Data Science
- Introduction To Statistical Analysis
- Using Python For Data Cleaning
- Machine Learning Algorithms
- Data Visualization Techniques
- Manipulating Datasets Using Pandas
- Using Numpy For Numerical Data
- Data Importing/Exporting With Python
- Exploratory Data Analysis Techniques
- Introduction To Time Series Analysis
- Clustering And Classification Methods
- 9h 15m
Artificial Intelligence
- Definitions And Scope Of Ai
- Basic Models Of Machine Learning
- Natural Language Processing
- Ethical Considerations In AI
- Ai In Real-World Applications
- Simple Neural Networks
- Problem-Solving With AI
- Introduction To AI Programming Tools
- Basic Computer Vision Techniques
- AI Deployment Challenges
- 9h 15m
Azure Machine Learning Studio
- Azure Ml Studio
- Navigating The Interface
- Experiments With Data Sets
- Understanding Model Deployment
- Basics, Auto Ml, Ml Ops, Pipeline
- 9h 15m
Deep Learning & Neural Networks
- Architecture of Neural Networks
- Understanding CNN’s And RNN’s
- Deep Learning Frameworks
- 9h 15m
Advanced Python
(Pandas & NumPy)
- Data Manipulation With Pandas
- HPC With Numpy
- Using Pandas For Time Series
- Complex Data Transformations
- Memory Management In Python
- Optimization For Code Performance
- Integrating Python With Databases
- Matplotlib And Seaborn
- Scripting For Automation
- Advanced Error Handling Techniques
- 9h 15m
Data Science
- Introduction To Statistical Analysis
- Using Python For Data Cleaning
- Machine Learning Algorithms
- Data Visualization Techniques
- Manipulating Datasets Using Pandas
- Using Numpy For Numerical Data
- Data Importing/Exporting With Python
- Exploratory Data Analysis Techniques
- Introduction To Time Series Analysis
- Clustering And Classification Methods
- 9h 15m
Artificial Intelligence
- Definitions And Scope Of Ai
- Basic Models Of Machine Learning
- Natural Language Processing
- Ethical Considerations In AI
- Ai In Real-World Applications
- Simple Neural Networks
- Problem-Solving With AI
- Introduction To AI Programming Tools
- Basic Computer Vision Techniques
- AI Deployment Challenges
- 9h 15m
Azure Machine Learning Studio
- Azure Ml Studio
- Navigating The Interface
- Experiments With Data Sets
- Understanding Model Deployment
- Basics, Auto Ml, Ml Ops, Pipeline
- 9h 15m
Deep Learning & Neural Networks
- Architecture of Neural Networks
- Understanding CNN’s And RNN’s
- Deep Learning Frameworks
- 9h 15m
Advanced Python
(Pandas & NumPy)
- Data Manipulation With Pandas
- HPC With Numpy
- Using Pandas For Time Series
- Complex Data Transformations
- Memory Management In Python
- Optimization For Code Performance
- Integrating Python With Databases
- Matplotlib And Seaborn
- Scripting For Automation
- Advanced Error Handling Techniques
- 9h 15m
Artificial Intelligence
- Definitions And Scope Of Ai
- Basic Models Of Machine Learning
- Natural Language Processing
- Ethical Considerations In AI
- Ai In Real-World Applications
- Simple Neural Networks
- Problem-Solving With AI
- Introduction To AI Programming Tools
- Basic Computer Vision Techniques
- AI Deployment Challenges
- 9h 15m
Azure Machine Learning Studio
- Azure Ml Studio
- Navigating The Interface
- Experiments With Data Sets
- Understanding Model Deployment
- Basics, Auto Ml, Ml Ops, Pipeline
- 9h 15m
Deep Learning & Neural Networks
- Architecture of Neural Networks
- Understanding CNN’s And RNN’s
- Deep Learning Frameworks
- 9h 15m
Python
- Variables and basic data types
- Loops, and functions
- Error handling and exceptions
- File input/output operations
- Using Python modules and libraries
- Python classes and objects
- Python virtual environments
- Basic debugging techniques in Python
- 9h 15m
Github
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Data Science
- Introduction To Statistical Analysis
- Using Python For Data Cleaning
- Machine Learning Algorithms
- Data Visualization Techniques
- Manipulating Datasets Using Pandas
- Using Numpy For Numerical Data
- Data Importing/Exporting With Python
- Exploratory Data Analysis Techniques
- Introduction To Time Series Analysis
- Clustering And Classification Methods
- 9h 15m
Artificial Intelligence
- Definitions And Scope Of Ai
- Basic Models Of Machine Learning
- Natural Language Processing
- Ethical Considerations In AI
- Ai In Real-World Applications
- Simple Neural Networks
- Problem-Solving With AI
- Introduction To AI Programming Tools
- Basic Computer Vision Techniques
- AI Deployment Challenges
- 9h 15m
Azure Machine Learning Studio
- Azure Ml Studio
- Navigating The Interface
- Experiments With Data Sets
- Understanding Model Deployment
- Basics, Auto Ml, Ml Ops, Pipeline
- 9h 15m
Deep Learning & Neural Networks
- Architecture of Neural Networks
- Understanding CNN’s And RNN’s
- Deep Learning Frameworks
- 9h 15m
AI Engineer Level Achieved!
AI Engineer
Average Salary
$1,41,757 year
Concept BASED Learning
Frequently Asked Questions
The AI Engineer Roadmap is a structured learning path designed to guide professionals through the essential skills and knowledge required to become proficient AI engineers. It covers foundational concepts, advanced AI technologies, and practical application through hands-on projects.
The AI Engineer Roadmap is perfectly suited for a diverse range of professionals looking to expand their expertise in artificial intelligence and machine learning, including Managers across various sectors, Data Scientists, HR & Recruiters, Software Developers, Finance Professionals, Logistics Industry Professionals, Data Management Professionals, IT and Software Engineers, Sales and Marketing Professionals, Operations and Admin Staff, Engineers, Students and Academics, Supply Chain Professionals, Chartered Accountants, and Freelancers. This program is designed to cater to individuals eager to integrate AI into their existing roles or those pursuing new career opportunities in the evolving field of AI and machine learning.
The AI Engineer Roadmap is designed for individuals who have basic programming knowledge. The program covers everything from the ground up, starting with fundamental data types and extending to advanced AI and ML concepts, all paired with practical, real-world applications. This comprehensive approach ensures that participants not only learn theoretical aspects but also gain hands-on experience applying these technologies effectively.
The AI Engineer Roadmap starts with Python programming basics, including syntax and object-oriented concepts, and progresses to advanced data handling with Pandas and NumPy. You'll learn essential machine learning algorithms and apply AI in practical scenarios. The course also addresses ethical considerations in AI. Hands-on projects throughout the program ensure you gain practical experience and readiness for real-world applications.
The roadmap is typically completed in 3 to 5 months, depending on your pace of study and previous experience.
Yes, the roadmap includes several projects that allow you to apply what you've learned in real-world scenarios. These projects are designed to build your portfolio and enhance your practical AI engineering skills.
Participants receive support through mentorship sessions with industry experts, access to peer collaboration forums, and technical assistance for project work. A weekly technical call with experts would be allocated to the participants.
The program is delivered online, featuring interactive content such as live lectures, workshops, and webinars, along with self-paced video tutorials and reading materials.
Upon successful completion, you will receive a certification of completion from BotCampus AI, which can enhance your professional profile and credibility in the field of AI engineering.
You can Enroll directly through the BotCampus AI website. Visit our Enrollment page, choose the AI Engineer Roadmap, and follow the instructions to register.
