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:

First Name

Last Name

Email

Country

Phone Number

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.

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

View Full Curriculum
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

View Full Curriculum
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

View Full Curriculum
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

View Full Curriculum
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

Tools:

View Full Curriculum
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

Tools:

View Full Curriculum
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

Tools:

View Full Curriculum
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

Tools:

View Full Curriculum

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.

Tuition and Scholarship

TUITION

Standard Package:

$4,000 USD

Self-paced materials + 30-hour 1-on-1 mentor support

Extensive Package:

$6,000 USD

Self-paced materials + 30-hour 1-on-1 mentor support + 6-month career services

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.

Women in Tech

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.

Laid-off Support

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.

Fresh Grads

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.