The Complete Machine Learning Bootcamp
Natural Language Processing (NLP) allows computers to understand and generate text. Pre-trained models can automate tasks like writing articles, reports or marketing copy based on prompts.
Masters machine learning and NLP concepts from basic to advanced topics. Develop an in-depth understanding of ML algorithms’ mathematical principles as well as tools available for optimizing them.
MLOps
The MLOps Bootcamp is a free nine-week course covering every stage of machine learning’s pipeline – from experimentation and model selection, deployment and monitoring through experimentation, deployment and monitoring. Each week includes video lectures, slides, code examples and community notes that cover these areas of application.
MLOps (Machine Learning Ops) is a core function of AI engineering that facilitates taking machine learning models into production and maintaining them for ongoing use. This task often requires collaboration from data scientists, devops engineers, IT teams and data security specialists.
MLOps requires an effective framework for collaboration, scalable operations and documentation. MLOps uses tools and processes that ensure real-world insights can be fed back into ML development processes – such as model lineage and versioning – as well as automation to increase speed of innovation while decreasing human error. Responsibilities may include creating CI/CD pipelines, optimizing model training processes and developing workflows to support rapid change; furthermore it also aids organizations in detecting problems like model drift: when accuracy deteriorates due to changes in data underlying data over time. MLOps helps organizations detect such problems while helping organizations track problems like model drift from which accuracy diminishes over time due to changes in data, helping catch issues like model drift occurring due to changes underlying data changes over time and help organizations catch any problems such as model drift, helping organizations detect problems like model drift being missed over time due to changes underlying data changes over time due to changes that impact on data underlying iterations processes used. MLOps utilizes tools and processes used for this task include model lineage versioning; along with automation which can improve speed innovation while decreasing human error. Its duties also include developing CI/CD pipelines optimizing training process optimization, as well as creating workflows to allow rapid change more easily catch problems such as model drift which cause accuracy loss due to changes.
AI Security
AI Security is an emerging field at the crossroads between cybersecurity and artificial intelligence, helping organizations develop robust AI infrastructure, protect training data, and ensure human-AI interaction. This course equips students with skills necessary to identify, assess, and address threats and vulnerabilities posed by AI systems; additionally it equips students to work alongside cybersecurity professionals in creating secure systems.
This bootcamp explores MLOps and AI Security, offering participants hands-on experience with tools, best practices, and technologies that enable AI integration. Participants gain knowledge in how to set up an MLOps environment, automate ML workflows, monitor models effectively and implement essential security measures.
The Mastering AI Security bootcamp is a three-day course designed for technical users interested in exploring the intersection between AI and cybersecurity. Participants will learn various aspects of AI in cybersecurity, such as threats and vulnerabilities, defense mechanisms, forensics, incident response for AI systems and future trends in AI security. Teachers can utilize these skills to strengthen their AI/Cybersecurity curriculum and prepare their students for careers in this industry.
Model Management
Model management refers to a set of processes and tools designed to ensure models created by data science engineers behave consistently throughout their lifespan. Without model management, teams often rely on manual, error-prone practices that are inflexible and difficult to scale. Model management automates and ensures best-practice behaviors related to logging, versioning, tagging, compliance tracking and performance monitoring – giving teams greater scalability, reproducibility and scaleability. In this phase, model management includes setting up a central model registry and metadata system; supporting online performance tracking using logs, dashboards, summaries and alerts; as well as monitoring through logs, dashboards summaries and alerts using open-source tools like Prometheus Seldon Core MLRun; this enables Machine Learning engineering teams to focus on pipeline operationalization while creating business value.
Deep Learning
Discover how to create and deploy deep learning models for computer vision (image recognition) and natural language processing (text classification). Understand the core architecture of neural networks – neurons, layers and activation functions. Use data augmentation techniques to boost accuracy and performance of models.
Master the tools of Artificial Intelligence, including Anaconda and Jupyter Notebook, TensorFlow and Keras. Create and apply machine learning algorithms on real data using project-based curriculum in 26 weeks online for real world application – earning a certificate upon completion!
Ideal for career changers, professionals looking to upgrade their AI knowledge and programmers with programming/data experience alike, this intensive course teaches participants how to solve complex business challenges through hands-on projects. Consider your existing skillset, career goals and learning style prior to enrolling; arrange a call with our Student Advisor to see if the Fullstack Academy Part Time AI & Machine Learning Bootcamp is an appropriate match.
