MAchine Learning Engineer Roadmap
Key Competencies and Knowledge
for a Successful AI Engineering Career
for a Successful AI Engineering Career

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ML Fundamentals
Watch Now
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 9h 15m
Github
Watch Now
- 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
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- 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
Advanced Python
(Pandas & NumPy)
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- 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
Azure
PySpark
Watch Now
- 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
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- 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
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- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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
Watch Now
- 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 Roadmap or Path is a structured learning program that is designed to help learners build their skills in machine learning algorithms, data modeling, and AI-driven solutions. It covers foundational concepts, includes hands-on projects, and advanced techniques to prepare you for real-world ML challenges.
This path is perfect for:
- Software developers and data scientists who are looking to expand their ML knowledge
- IT professionals who are interested in exploring AI roles
- Students and beginners who are eager to learn machine learning from the basics
- Anyone who is passionate about mastering AI and ML technologies.
A basic level understanding of Python programming and familiarity with basic math and statistics. This course is beginner friendly since it starts with foundational topics.
You’ll master:
- Supervised and Unsupervised Learning
- Neural Networks and Deep Learning
- Reinforcement Learning
- Evaluation Metrics for ML Models
- Feature Engineering and Model Optimization
- Hands-on experience with Azure Machine Learning Studio, PySpark, and Databricks
The program takes 6 to 7 months to complete, which depends on your pace and schedule.
Yes, the path includes real-world ML projects from which you can build predictive models, optimize algorithms, and apply AI to practical scenarios. The roadmap for machine learning helps you develop a strong portfolio.
You’ll benefit from:
- Mentorship sessions with industry experts
- Access to a peer community for collaboration
- Weekly technical calls and Q&A sessions
- Guest lectures and live discussions
The program is completely online, and it offers:
- Live interactive classes
- On-demand video tutorials
- Reading materials and downloadable resources
- Real-time mentoring for a personalized learning experience
You will receive a certification from BotCampus AI to validate your expertise in machine learning. This machine learning roadmap for beginners certification increases your career opportunities in AI and data science.
Visit the Machine Learning Path page, select your plan, and enroll! Now you can start your journey to become a machine learning expert!