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fundamental

Introduction to GitHub Actions

Math for Machine Learning

Introduction to Docker

Introduction to Generative AI

Introduction to Git and Version Control

Data Visualization with Python

Data Wrangling with Python

Data Governance

Excel Fundamentals

Introduction to Linux

SQL Fundamentals

Python Fundamentals

Intermediate

AI Automation

Data Structure and Algorithm

Introduction to dbt

GCP Fundamental

Advanced Python

Introduction to Kubernetes

Data Visualization with Power BI

Data Visualization with Tableau

Azure Fundamental

AWS Fundamental

Introduction to NLP

Introduction to Computer Vision

Modern Data Warehouse

Machine Learning

advanced

Data Streaming

Data Migration

Data Lake Architecture

ETL, ELT & Reverse ETL Pipelines

Introduction to Large Language Model

Introduction to MLOps

Big Data for Data Science

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LEARNING TRACK

Artificial Intelligence (AI) Engineering Track

Advanced

Online

Self-paced

This track builds your MLOps foundation, preparing you to deploy, scale, and manage AI models. You’ll gain expertise in MLOps and LLMOps, mastering the full lifecycle of Al models from training to deployment and monitoring.

About the Track

Embark on your Al Engineering journey with the Al Engineering Track, designed for individuals seeking to build production-grade Al solutions. This track begins with foundational programming in Python and data wrangling, followed by essential machine learning and deep learning concepts grounded in mathematical foundations. You’ll dive into specialized domains such as Natural Language Processing (NLP), Large Language Models (LLMs), and Computer Vision, applying advanced models to real-world use cases.

Top data skills program voted by students and partners

Courses in this track

Python Fundamentals

Fundamental

By the end of this course, participants will be able to:

Comprehend Python syntax and foundational programming concepts.
Effectively use data structures and perform file operations.
Implement control flow statements to create logic-driven programs.
Design and use functions to promote code reusability and modular programming.
Apply object-oriented programming principles to design and implement classes.
Handle exceptions to build robust, error-resistant Python applications.

Data Wrangling with Python

Fundamental

By the end of this course, participants will be able to:

Work with Pandas Series, DataFrames, and Index objects
Read, write, filter, and transform structured data using Pandas
Clean data to ensure quality and integrity, including handling missing values and outliers
Reshape, pivot, and organize data for effective analysis
Merge, join, and group datasets using core data manipulation techniques
Apply data wrangling skills to real-world problems through hands-on projects

Math for Machine Learning

Fundamental

By the end of this course, participants will be able to:

Demonstrate proficiency in key statistical distributions and utilize them effectively in real-world applications
Apply appropriate sampling techniques and interpret the results
Conduct hypothesis testing using both basic and advanced methods
Perform vector and matrix operations relevant to machine learning
Optimize machine learning models using calculus-based technique

Machine Learning

Intermediate

By the end of this course, participants will be able to:

Understand and apply core machine learning concepts.
Leverage mathematical principles to solve machine learning problems and perform exploratory data analysis.
Preprocess and transform data through cleaning, feature engineering, and dimensionality reduction.
Implement, validate and optimize machine learning models. Explore advanced applications.

Introduction to NLP

Intermediate

By the end of this course, participants will be able to:

Explain the architecture and working principles of transformers and attention mechanisms.
Implement transformer encoders and decoders from scratch.
Develop and fine-tune large language models like BERT and GPT for various NLP tasks.
Evaluate and deploy LLMs effectively in real-world applications.
Utilize RAG techniques to enhance the capabilities of LLMs in information retrieval and generation tasks.

Introduction to Large Language Models

Advanced

By the end of this course, participants will be able to:

Explain the architecture and working principles of transformers and attention mechanisms.
Implement transformer encoders and decoders from scratch.
Develop and fine-tune large language models like BERT and GPT for various NLP tasks.
Evaluate and deploy LLMs effectively in real-world applications.
Utilize RAG techniques to enhance the capabilities of LLMs in information retrieval and generation tasks.

Introduction to Computer Vision

Intermediate

By the end of this course, participants will be able to:

Understand Image Fundamentals on how digital images are formed, represented, and transformed.
Apply Classical Image Processing Techniques by implementing methods for geometric transformations, intensity adjustments, filtering, morphological operations, and color processing.
Explain the principles of CNN and implement them for image recognition tasks.
Evaluate Image Classification Models by using CV architectures
Apply CV techniques to solve real-world problems (e.g., Object Detection and Semantic Segmentation).

Introduction to Linux

Fundamental

By the end of this course, participants will be able to:

Understand the fundamentals of Linux operating systems and distributions.
Create and manage virtual machines
Use essential Linux commands
Basic Shell scripting and debugging

Introduction to Git and Version Control

Fundamental

By the end of this course, participants will be able to:

Understand the fundamentals of Git and version control systems.
Manage local Git repositories and track changes effectively.
Collaborate on projects using GitHub and remote repositories.
Apply advanced Git techniques for collaboration using GitFlow and GitHub Flow.

Introduction to Docker

Fundamental

By the end of this course, participants will be able to:

Understand the principles of containerization and its advantages.
Use Docker to create, manage, and deploy containers.
Create and push images to Docker Hub.
Configure and deploy multi-container applications using Docker Compose

AWS Fundamentals

Intermediate

By the end of this course, participants will be able to:

Understand core AWS services and cloud computing principles.
Set up and manage secure AWS accounts.
Deploy virtual servers and manage configurations with EC2.
Store and manage data securely with S3.
Build serverless applications using AWS Lambda and API Gateway.
Monitor, log, and troubleshoot with CloudWatch.

Introduction to MLOps

Advanced

By the end of this course, participants will be able to:

Understand MLOps principles, Python tools, and the ML pipeline lifecycle.
Use Ray, SageMaker, MLflow, and DVC for training, tracking, and versioning.
Package models with Pickle/ONNX and manage features with modern data tools.
Deploy models using serverless tech and build CI/CD pipelines.
Monitor model performance, detect drift, and automate retraining.
Explore LLMOps workflows, prompt engineering, and RAG pipelines.

Introduction to LLMOps

Coming Soon

This Learning Track include:

Multiple courses in one track — build skills step by step
Engaging video lessons + hands-on quizzes
Capstone projects to strengthen your skills
Certificate of Completion
Full access with your active subscription

Not the right path for you?

We also offer personalized and customized path that suits your learning and career goals. Talk to our advisors.

student success

What our graduates are saying

Celio Oliveira

Reviewed in 2019 | Overall ⭐⭐⭐⭐⭐

This program is fantastic! I didn’t have a coding background and the way they prepared each module made it easier to understand from basic concepts to Advanced SQL, Python, ML and Data Science that I just finished. It is all very exciting. They really do prepare you and offer great support (includes problem sets, individually and in group and quizzes). Teachers are well connected and help you, no matter the prior experience. When you graduate, you have a portfolio of projects, a very good literature and also hands on practise what is important as they showcase your writing capability.

OUR ALUMNI ARE WORKING AT

Common Questions

Find answers to your questions about the Learning Track

What is a Learning Track?

A Learning Track is a curated series of courses, projects, and assessments designed to help you master a specific skill set or career path.

How is a Learning Track different from a single course?

Learning Tracks include multiple courses plus capstone or portfolio projects, offering a more comprehensive and structured learning experience.

How long will I have access to the Learning Track?

Access is tied to your subscription. As long as your subscription is active, you can continue learning.

Do I get hands-on projects?

Yes, each Learning Track includes hands-on labs and a final capstone project to build your portfolio.

Will I get a certificate?

Absolutely. Once you complete the entire Learning Track, you’ll receive a certificate of completion.

Still have questions?

If you have other queries or specific concerns about our Learning Track Subscription, don’t hesitate to let us know. Your feedback is important to us, and we aim to provide the best support possible.

Your Learning Journey Awaits 🚀

Grow your skills, build projects you’ll be proud of, and unlock new opportunities — all at your pace.

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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