MAchine Learning Engineer Roadmap
Key Competencies and Knowledge
for a Successful AI Engineering Career
for a Successful AI Engineering Career
Trusted by
You Know
ML Fundamentals
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 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
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
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Azure Machine Learning Studio
- Basics of Azure Machine Learning Studio
- Create, Train and Deploy Models
- Using automated machine learning,
- ML Ops
- Working with Data Sets
- What are Data Sources
- Model Stats
- Understanding feature engineering
- Model life cycle management
- 9h 15m
AWS SageMaker
- Getting Started with SageMaker
- Building Models with SageMaker
- Deploying Machine Learning Models
- SageMaker Automatic Model Tuning
- Integration with Jupyter Notebooks
- Managing Data in SageMaker
- Using Built-in Algorithms
- SageMaker for Deep Learning
- Model Monitoring and Debugging
- Scaling and Performance Optimization
- 9h 15m
ML Fundamentals
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 9h 15m
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Azure Machine Learning Studio
- Basics of Azure Machine Learning Studio
- Create, Train and Deploy Models
- Using automated machine learning,
- ML Ops
- Working with Data Sets
- What are Data Sources
- Model Stats
- Understanding feature engineering
- Model life cycle management
- 9h 15m
AWS SageMaker
- Getting Started with SageMaker
- Building Models with SageMaker
- Deploying Machine Learning Models
- SageMaker Automatic Model Tuning
- Integration with Jupyter Notebooks
- Managing Data in SageMaker
- Using Built-in Algorithms
- SageMaker for Deep Learning
- Model Monitoring and Debugging
- Scaling and Performance Optimization
- 9h 15m
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Azure Machine Learning Studio
- Basics of Azure Machine Learning Studio
- Create, Train and Deploy Models
- Using automated machine learning,
- ML Ops
- Working with Data Sets
- What are Data Sources
- Model Stats
- Understanding feature engineering
- Model life cycle management
- 9h 15m
AWS SageMaker
- Getting Started with SageMaker
- Building Models with SageMaker
- Deploying Machine Learning Models
- SageMaker Automatic Model Tuning
- Integration with Jupyter Notebooks
- Managing Data in SageMaker
- Using Built-in Algorithms
- SageMaker for Deep Learning
- Model Monitoring and Debugging
- Scaling and Performance Optimization
- 9h 15m
Machine Learning Engineer Level Achieved!
ML Engineer
Average Salary
$1,45,245 year
Concept BASED Learning
Frequently Asked Questions
The Machine Learning Engineer Path is a comprehensive training program aimed at developing core competencies in machine learning engineering. It covers essential ML concepts, algorithm application, model tuning, and deployment tailored for real-world usage.
This path is designed for individuals with fundamental programming knowledge who are interested in advancing their career by mastering machine learning. It's suitable for beginners in the field as well as professionals from various sectors seeking to implement ML solutions in their work.
The only prerequisite is a fundamental understanding of programming. The program is structured to teach everything else from the ground up, making it accessible even to those new to machine learning.
Participants will learn to process and analyze data, apply various machine learning algorithms, and evaluate and optimize models. The path also covers practical aspects of deploying models to production. You’ll gain hands-on experience with popular ML tools and frameworks through project-based learning.
Typically, the path can be completed in 3 to 5 months, depending on your pace of study and prior experience.
Yes, the path includes practical projects that challenge you to apply your learning on real-world datasets, enhancing both your understanding and your portfolio.
Support includes mentorship from experienced industry professionals, access to peer collaboration forums, and technical assistance for projects. Regular live sessions provide further opportunities for learning and interaction.
The program is delivered online, combining interactive live sessions, self-paced video tutorials, and comprehensive reading materials, providing flexibility to learn according to your schedule.
Upon completion, you will receive a BotCampus AI certification, acknowledging your expertise as a Machine Learning Engineer, which is a valuable addition to your professional profile.
To enroll, visit the BotCampus AI website, navigate to the enrollment page, select the Machine Learning Engineer Path, and follow the registration instructions.
