Machine Learning Engineering Bootcamp (Self-paced)

Combine self-paced materials with mentor sessions for a comprehensive and flexible learning experience

Inquire today to become a machine learning engineer with WeCloudData. Our Self-Paced Machine Learning Engineer Bootcamp is tailored for individuals aspiring to advance in Machine Learning and AI careers. Our comprehensive curriculum focuses on state-of-the-art ML algorithms, hands-on project implementation, and real client project experiences. With one-on-one career mentorship included, our program prepares you to secure machine learning engineer positions. Join WeCloudData, consistently ranked among top coding bootcamps, and embark on your journey to become a proficient machine learning engineer.

Explore our Program Package to find:
SELF-PACED LEARNING

Access our comprehensive ✨self-paced✨ learning materials 24/7

Learn on your own terms and schedule. Our self-paced approach allows you to delve into the world of data without the pressure of deadlines. Take control of your learning journey, progressing at a pace that suits your lifestyle.

Learn Anytime, Anywhere

Learn at your own pace without deadlines, tailoring your journey to fit your lifestyle.

Interactive Modules

Dive deep into data concepts through videos, quizzes, and hands-on exercises that bring theory to life.

TA Support

Get dedicated support from experienced teaching assistants for guidance whenever needed.

Real-World Applications

Our course content is rooted in practical, industry-relevant scenarios, providing insights directly applicable to your professional endeavors

OVERVIEW

About the Program

The Self-Paced Machine Learning Engineer Diploma Program (ML Engineer Bootcamp) is designed for data scientists, software engineers, and career switchers seeking to enter the field of Machine Learning Engineering. This program offers flexible learning with no fixed duration, including weekly 1-on-1 mentor sessions, on-demand TA and review sessions, and comprehensive after-graduation support. WeCloudData is committed to guiding learners through their career transitions with tailored assistance. If you aim to advance in AI/ML and MLOps, this self-paced program provides the necessary experience and expertise to achieve your goals effectively.

What you will get
Top data bootcamp voted by students and partners
4.9/5
4.9
4.9/5
4.95
4.8/5
4.8
5/5
5.0
LEARNING EXPERIENCE

A Seamless and Enriching Learning Experience

1
Start Learning Anytime

Engage with self-paced materials designed for learning anytime, anywhere, and at your own pace.

2
Get TA Support

Teaching Assistants (TAs) are readily available on Slack to assist you with any questions during the bootcamp.

3
Review Session

Participate in live, interactive workshops with instructors for dynamic review and reinforcement of the covered materials in each module.

 

4
Personalized Mentorship

Book a weekly meeting with a mentor who will guide you through your learning and career journey, providing personalized support.

CURRICULUM

Explore Our Comprehensive Curriculum

WeCloudData Bootcamps are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends. 

  • Neural Networks & Deep Learning
    Module 1
    Neural Networks & Deep Learning
    The first module in this course begins with a quick machine learning review, and then introduces the basics of neural networks and commonly used deep learning frameworks such as PyTorch, Torch Lightning, and Tensorflow.
    LEARNING OUTCOMES
    • Review Classical ML algorithms such as Linear Regression, Decision Trees, XGBoost, Random Forest, and K-Means Clustering
    • Review Feature Engineering techniques and ML Training Pipeline
    • Understand neural networks internals such as MLP (backdrop), optimization (SGD, Adam, etc.), regularization (batch normalization, dropout)
    • Learn and apply neural networks using PyTorch
    • Train and tune basic deep neural networks for classification and regression problems
    KEY SKILLS
    • Deep Neural Networks
    • MLP
    • Optimization
    • ML Pipeline
    • Model Training
    • Model Tuning
    Tools:
    PyTorch
    Lightning
    Tensorflow
    Colab
    Pandas
    Numpy
  • Computer Vision
    Module 2
    Computer Vision
    This module focuses on the Computer Vision applications of deep learning. It covers the fundamentals of Convolutional Neural Networks and different CNN architectures, teaches image augmentation and processing using TorchVision and OpenCV, and introduces common CV tasks such as image classification, object detection, semantic segmentation, image augmentation, transfer learning, and generative models such as neural style transfer.
    LEARNING OUTCOMES
    • Learn the fundamentals of deep convolutional neural networks
    • Get hands-on with various CNN architectures such as AlexNet, VGG, Inception, RestNet, and Xception
    • Apply CNN to solve image classification problems
    • Apply YOLO and R-CNN to solve object detection problems
    • Apply FCN and DeepLab algorithms to solve semantic segmentation problems
    • Learn how to label and augment image data using various tools
    KEY SKILLS
    • CNN
    • Computer Vision
    • Convolutional Neural Networks
    • Image Augmentation
    • Image Classification
    • Object Detection
    • Semantic Segmentation
    • Instance Segmentation
    • Neural Style Transfer
    Tools:
    OpenCV
    PyTorch
    Detectron
    HuggingFace
  • NLP (Natural Language Processing)
    Module 3
    NLP (Natural Language Processing)
    This module teaches SOTA NLP methods and applications. Students will learn some of the most exciting new development in modern NLP such as Transformers and GPT-3.
    LEARNING OUTCOMES
    • Learn basic text-processing techniques
    • Become familiar with traditional NLP methods such as N-gram, topic modelling, text clustering, NER
    • Learn word embedding techniques such as skip-gram, word2vec
    • Sequence-to-Sequence models with recurrent neural networks (RNN) and LSTM
    • Apply state-of-the-art (SOTA) attention models such as BERT transformers for transfer learning
    • Apply generative models such as GPT-3 for text generation and Question-Answer systems
    • Build a mini-ChatGPT application using GPT-3 and PyTorch
    KEY SKILLS
    • Text Processing
    • Natural Language Processing
    • Large Langage Models (LLM)
    • Topic Modeling
    • Transformers
    • BERT
    • GPT-3
    • Roberta
    • Text Classification
    • Sentiment Analysis
    Tools:
    HuggingFace
    PyTorch
    GPT-3
  • ML Infrastructure
    Module 4
    ML Infrastructure
    This module introduces the infrastructure tools required for building scalable and robust machine learning solutions in production. Topics include Linux, Docker, AWS, and Kubernetes.
    LEARNING OUTCOMES
    • Get comfortable with Linux operating systems and command line
    • Launch Compute instances on Cloud infrastructure such as AWS EC2
    • Learn the basics of virtualization and docker containers
    • Build docker images using docker compose
    • Learn Kubernetes and container orchestration fundamentals
    KEY SKILLS
    • Docker
    • Linux
    • Machine Learning Infrastructure
    • Cloud
    • AWS
    • SageMaker
    • Kubernetes
    • Serverless
    Tools:
    AWS
    Ubuntu
    Linux
    Docker
    Kubernetes
  • Model Engineering
    Module 5
    Model Engineering
    This module teaches students how to build machine learning training and evaluation pipelines. Students will refresh their ML knowledge and learn how to build baseline models and detect issues in the model/feature pipelines early on, and then work with model experiment frameworks such as MLflow and Weights & Biases. Model interpretation and validation will be covered extensively before students learn how to package models using various formats.
    LEARNING OUTCOMES
    • Understand end-to-end machine learning pipelines
    • Gain hands-on experience with custom feature transformers
    • Learn how model tracking and monitoring tools work in real life
    • Learn how to build baseline models and detect issues in data and models
    • Learn how to package machine learning models using different formats such as ONNX
    KEY SKILLS
    • Model packaging
    • ML Pipeline
    • Model Experimentation
    • Model Validation
    • Model Monitoring
    • Baseline Model
    • Feature Transformer
  • ML Software Engineering
    Module 6
    ML Software Engineering
    This module teaches students the necessary software engineering skills for model deployment. Students will learn the basics of web applications, REST APIs, model serving and inference. Students will not only learn how to create inference APIs but also how to deploy the prediction services in a local docker container, AWS Lambda, Sagemaker, as well as AWS ECS/Fargate. The scaling part will be introduced at a later module.
    LEARNING OUTCOMES
    • Learn the fundamentals of web applications and Microservices
    • Learn how to build and deploy basic Python-based applications using FastAPI and Flask
    • Learn how to create an inference API using FastAPI
    • Learn how to package and structure ML projects
    • Learn how to deploy ML Models in different types of infrastructures
    KEY SKILLS
    • Model Serving
    • Prediction Service
    • Inference API
    • Model Deployment
  • ML Operations
    Module 7
    ML Operations
    This module teaches students the DevOps part of MLOps and ML Engineering. Students will learn the basics of Data Version Control, CI/CD, infrastructure scaling, and infrastructure automation.
    LEARNING OUTCOMES
    • Learn the principles of MLOps
    • Learn how CI/CD pipelines work in the Machine Learning context
    • Version control data for machine learning using DVC
    • Practice infrastructure automation and scaling using Terraform, AWS Cloud Formation, and Kubernetes
    • Learn how to build and maintain feature stores using Feast

     

     

    KEY SKILLS
    • Continuous Integration
    • Continuous Deployment
    • CI/CD
    • Infrastructure as Code
    • Automation
    • Data Version Control
    • DVC
    • Feast
    • Feature Store
    • SageMaker
    • Vertex AI
    • Terraform
    • Kubernetes
  • Career Preparation & Mentorship
    Module 8
    Career Preparation & Mentorship
    Before entering the 1-1 career mentoring, students will learn about the ML Engineering job market and build job search skills. Career coaches will teach graduates how to structure resumes, apply for jobs, and ace the interviews. Students work in groups for peer mock interview practice.
    LEARNING OUTCOMES

    Career services included in the bootcamp include

    • Resume workshops
    • Group interview practice
    • Portfolio project mentoring
    • Coding interview practice and additional resources (Leetcode/hackerrank)
    • Peer programming practice and code reviews

    Career services included after graduation (6 months)

    • One-on-one career mentoring sessions with data scientists and ML Engineers for 6 months after graduation
    • One-on-one resume critique
    • One-on-one mock interview sessions with career mentors
    • Job referrals and networking sessions
    KEY SKILLS
    • Leetcode
    • Data Structure & Algorithms
    • System Design
    • Communications
    • Presentation Skills
    • Business Acumen
Mentorship

Nail your job search journey via 1-on-1 mentoring

From the beginning of your self-paced bootcamp, our experienced mentors are here to guide you every step of the way. Have questions about the materials? Need career advice to navigate your chosen field? Your dedicated mentor is ready to provide insights, support, and valuable guidance. Together, we’ll shape your learning experience and pave the way for your successful career

Tailored Acceleration and Support:

Your mentor is there to address questions promptly, ensuring a tailored and efficient learning journey

Enhanced Motivation:

Stay motivated and engaged with your studies as your mentor provides encouragement, feedback, and a supportive presence throughout your self-paced bootcamp

Industry Insights:

Gain valuable career advice, insights, and industry knowledge from your mentor, enhancing your understanding and preparing you for success in your chosen field.

Real Client Projects

Gain Hands-on Experience with Real-Client Projects ✨

One of the best ways to get the experience needed for a data & tech career is to start with a project. WeCloudData is one of the few companies who offers this opportunity. In our bootcamp, we’ll give you an opportunity that many graduates don’t have: work on something meaningful and important right away. You will be able to even contribute ideas or solutions that make an impactful change!

*Please note that the Real Client Project is now optional and not included in the Self-paced bootcamp tuition.

Interested in Real Client Project?
Portfolio Projects

Build real project experience to differentiate

We also have a capstone project that gives our students the chance to synthesize their learning and build a portfolio piece they can showcase on their resume or LinkedIn profile. This helps them stand out from other applicants when applying for jobs or opportunities.

OUR ALUMNI ARE WORKING AT

Tuition and Scholarship

Scholarship

We believe in investing in the future of our industry and supporting individuals in their professional journeys. That’s why WeCloudData is proud to offer scholarships for individuals looking to pursue professional development or make a career change. 

The Women in Tech scholarship supports and empowers women pursuing careers in the technology industry. This scholarship aims to address gender diversity and underrepresentation in tech by providing financial assistance, mentorship, and access to our comprehensive bootcamp. We celebrate the achievements of aspiring women technologists and equip them with the skills to thrive in the tech sector.

The Laid-off Support is a special offering designed to provide financial assistance and support to individuals who have recently been laid off from their jobs. We understand the challenges and uncertainties that come with job loss, and we want to help those affected continue their professional development in the technology sector. This provides reduced pricing and access to our comprehensive range of courses, allowing laid-off individuals to acquire new skills, enhance their knowledge, and increase their chances of finding new opportunities in the competitive job market.

The Fresh Grads Scholarship is a scholarship program that offers financial assistance to outstanding individuals who have recently completed their undergraduate or postgraduate studies within 12 months and are looking to kickstart their careers in data and tech. The scholarship aims to bridge the gap between academia and industry, empowering fresh graduates to become competent data professionals through an intensive and comprehensive bootcamp.

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.

5/5
5/5

Let WeCloud Accelerate Your Career in Tech

Start your career today!

Want more details about this program? Unsure about which path to take? Apply now to reserve a spot or make an appointment with our learning advisor. 

View our Machine Learning Engineering Bootcamp (Self-paced) course package

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Machine Learning Engineering Bootcamp (Self-paced)

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View our Machine Learning Engineering Bootcamp (Self-paced) course package