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AI Engineering Buildcamp is project-driven.
You learn AI engineering by building.
15 projects you can build during the course 👇🏼
(You get lifetime access to the course if you sign up)
Alexey Grigorev
7,456 posts
Alexey Grigorev
@Al_Grigor
Founder @DataTalksClub | Teaching engineers to build production AI systems | AI agents, LLMs, ML, data engineering | 100,000+ learners
- Learning path to mastering Data Science: 🔸 Python 🔸 Git 🔸 SQL 🔸 NumPy 🔸 Pandas 🔸 Scikit-Learn 🔸 Flask 🔸 Docker 🔸 AWS 🔸 TensorFlow 🔸 Linear Algebra 🔸 Machine Learning basics What else?
- When learning machine learning, focus on these algorithms first: 🔸 Linear regression 🔸 Logistic regression 🔸 Decision trees 🔸 Random forest 🔸 Gradient boosting 🔸 Neural networks (also CNN) In this order Knowing them will cover 95% of applied ML cases
- Tools that cover 90% of data science use cases 🔸 Git 🔸 Bash 🔸 SQL 🔸 NumPy 🔸 Pandas 🔸 Scikit-Learn 🔸 Flask 🔸 Docker Focus on them first
- Roadmap for learning ML Engineering: 🔸 Linear regression 🔸 Logistic regression 🔸 Evaluation metrics 🔸 Docker, web services, cloud 🔸 Model deployment 🔸 Tree-based models 🔸 Neural networks 🔸 Kubernetes Learn it in this order and you'll be ready for an ML engineering job
- 200+ Data Science interview questions 🔸 Supervised machine learning (linear models, trees, neural nets) 🔸 Feature selection, parameter tuning 🔸 Unsupervised learning (clustering, dim reduction) 🔸 Recommenders and search 🔸 SQL 🔸 Coding (Python), algorithms With answers 👇
- My onsite interview for ML engineering with a FAANG company: 🔸 Behavioral 🔸 Coding round 1 (two problems) 🔸 Coding round 2 (two problems) 🔸 System design 🔸 ML case study Here are the questions I got👇
- Registration to Machine Learning Zoomcamp 2022 is open! Learn ML Engineering in 4 months in a free online course: - Linear and logistic regression - Tree-based models - Neural networks - Deployment with AWS, Serverless, Kubernetes 👉 github.com/alexeygrigorev…
- I just started a Data Engineering community here on Twitter Since the communities on Twitter is a new thing, it's invite-only currently Reply if you need an invite
- The most useful online courses I took when learning machine learning 🔸 Machine Learning (coursera) 🔸 Statistical Learning (edx) 🔸 Analytics Edge (edx) 🔸 Learning from Data (caltech) I took a lot of courses. But these four were the best
- Most candidates cannot solve this interview problem: 🔸 Input: "aaaabbbcca" 🔸 Output: [("a", 4), ("b", 3), ("c", 2), ("a", 1)] Write a function that converts the input to the output I ask it in the screening interview and give it 25 minutes How would you solve it?
- Learning paths to mastering: 🔸 Data science 🔸 Data engineering 🔸 Machine learning engineering 🔸 MLOps All in one mega-thread! 👇 (Make sure to check the replies!)
- Want to get into Machine Learning? 🔸 If you want to learn — learn mathematics 🔸 if you want to earn — learn Scikit-Learn
- Learning path to mastering MLOps: 🔸 Linux 🔸 Python 🔸 Docker 🔸 AWS 🔸 Terraform 🔸 Kubernetes 🔸 Prometheus 🔸 Grafana 🔸 Kubeflow What else?