updated tools on ML zoomcamp

Author: kavaivaleri

_posts/2023-08-17-machine-learning-zoomcamp.md

@@ -14,7 +14,7 @@ date: 2025-09-01
 ---
 
 <figure>
-<img src="/images/posts/2023-08-17-machine-learning-zoomcamp/ml_zoomcamp_overview_horizontal.png" alt="ML Zoomcamp course overview showing progression from ML algorithms (Python, NumPy, Pandas, Scikit-learn) to deployment (Docker, Flask, Kubernetes)" />
+<img src="/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/ml_zoomcamp_overview_horizontal_2025.png" alt="ML Zoomcamp course overview showing progression from ML algorithms (Python, NumPy, Pandas, Scikit-learn) to deployment (Docker, FastAPI, Kubernetes)" />
 <figcaption>Complete ML Zoomcamp curriculum: from machine learning fundamentals to production deployment</figcaption>
 </figure>
 
@@ -61,10 +61,10 @@ Tools you'll learn:
 - NumPy and Pandas for data manipulation
 - Matplotlib and Seaborn for visualization
 - Scikit-Learn for ML algorithms
-- TensorFlow and Keras for deep learning
+- TensorFlow and PyTorch for deep learning
 
 <figure>
-<img src="/images/posts/2023-08-17-machine-learning-zoomcamp/image1.png" alt="Overview of Python libraries and tools covered in ML Zoomcamp: NumPy, Pandas, Scikit-learn, TensorFlow" />
+<img src="/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/vertical1.png" alt="Overview of Python libraries and tools covered in ML Zoomcamp: NumPy, Pandas, Scikit-learn, TensorFlow" />
 <figcaption>Key technologies and libraries covered in Part 1 of ML Zoomcamp</figcaption>
 </figure>
 
@@ -75,17 +75,17 @@ Part 2 focuses on model deployment, which involves putting machine learning mode
 
 You'll learn to deploy models using:
 
-- **Flask, Pipenv, and Docker:** machine learning models deployment, enabling you to move 
+- **FastAPI, Pipenv, and Docker:** machine learning models deployment, enabling you to move 
 your models from notebooks to services and applications.
-- **AWS Lambda and TensorFlow Lite:** serverless deep learning, understanding how to 
+- **AWS Lambda and ONNX Runtime:** serverless deep learning, understanding how to 
 efficiently operate within this paradigm.
-- **Kubernetes and TensorFlow Serving:** automating deployment, scaling, and management of 
+- **Kubernetes and TensorFlow Serving** automating deployment, scaling, and management of 
 containerized applications.
 - **KServe (optional):** an additional topic for those seeking advanced knowledge, offering 
 insights into further enhancing deployment capabilities.
 
 <figure>
-<img src="/images/posts/2023-08-17-machine-learning-zoomcamp/image3.png" alt="Deployment tools and frameworks in ML Zoomcamp: Flask, Docker, AWS Lambda, Kubernetes" />
+<img src="/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/vertical2.png" alt="Deployment tools and frameworks in ML Zoomcamp: FastAPI, Docker, AWS Lambda, Kubernetes" />
 <figcaption>Deployment technologies used in Part 2 of ML Zoomcamp for putting ML models into production</figcaption>
 </figure>
 

_posts/2023-11-18-data-engineering-zoomcamp.md

@@ -1,7 +1,7 @@
 ---
 authors:
 - valeriiakuka
-description: "Free Data Engineering Bootcamp 2026: Master data engineering with hands-on training in Python, SQL, dbt, Kafka, and Spark. Join DataTalks.Club's comprehensive 9-week course covering modern data engineering tools, batch and stream processing, and real-world projects. Perfect for beginners and experienced developers."
+description: "Master data engineering with hands-on training in Python, SQL, dbt, Kafka, and Spark. Join DataTalks.Club's comprehensive 9-week course covering modern data engineering tools, batch and stream processing, and real-world projects. Perfect for beginners and experienced developers."
 image: images/posts/2023-11-18-data-engineering-zoomcamp/cover.jpg
 layout: post
 subtitle: "Master Modern Data Engineering: From Basics to Advanced Pipeline Development"

_posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club.md

@@ -33,7 +33,7 @@ DataTalks.Club offers a range of free online courses in Machine Learning, Data E
 ## Machine Learning Zoomcamp
 
 <figure>
-<img src="/images/posts/2023-08-17-machine-learning-zoomcamp/ml_zoomcamp_overview_horizontal.png"  />
+<img src="/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/ml_zoomcamp_overview_horizontal_2025.png"  />
 <figcaption>Technologies used in the Machine Learning Zoomcamp</figcaption>
 </figure>
 
@@ -53,15 +53,15 @@ The course is divided into two parts:
 - Decision trees and ensemble learning
 - Neural networks and deep learning
 
-**Key tools:** Python, NumPy, Pandas, Scikit-Learn, TensorFlow
+**Key tools:** Python, NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch
 
 #### Part 2: Model Deployment
-- Web services with Flask
+- Web services with FastAPI
 - Containerization with Docker
 - Cloud deployment on AWS Lambda
 - Orchestration with Kubernetes
 
-**Key tools:** Flask, Docker, AWS Lambda, Kubernetes, TensorFlow Serving
+**Key tools:** FastAPI, Docker, AWS Lambda, Kubernetes, TensorFlow Runtime
 
 <div style="text-align: center; margin: 2em 0;">
     <div style="display: inline-block; background: #28a745; padding: 0.5em 2em; border-radius: 8px; box-shadow: 0 4px 6px rgba(50, 50, 93, 0.11), 0 1px 3px rgba(0, 0, 0, 0.08); transition: all 0.15s ease;">
@@ -153,7 +153,7 @@ The course guides you step-by-step through each stage of the MLOps cycle, starti
 Technologies you'll learn:
 
 - MLFlow
-- Flask
+- FastAPI
 - AWS
 - Mage
 - Evidently AI

_posts/2025-08-16-ultimate-list-of-20-free-online-courses-on-machine-learning.md

@@ -41,7 +41,7 @@ University open-courseware is another category: these are completely free and of
 5. **Duration:** ~4 months (live cohort starting September 2025) or self-paced
 6. **Certificate:** Available for free upon completion for the live cohort; not issued for the self-paced track
 
-[Machine Learning Zoomcamp](https://datatalks.club/blog/machine-learning-zoomcamp.html){:target="_blank"} is a practical, end-to-end ML engineering course that takes learners from core foundations to production deployment. You’ll cover regression and classification, evaluation and cross-validation, trees and gradient boosting, and deep learning (CNNs, transfer learning). A deployment track focuses on packaging and serving models (Flask APIs, Docker, cloud, serverless, TensorFlow Serving, Kubernetes, optional KServe) plus monitoring and CI/CD. The program centers on building: a midterm end-to-end project and a capstone production system, emphasizing reproducible code, system design, and documentation, supported by a structured homework cadence and an active community.
+[Machine Learning Zoomcamp](https://datatalks.club/blog/machine-learning-zoomcamp.html){:target="_blank"} is a practical, end-to-end ML engineering course that takes learners from core foundations to production deployment. You'll cover regression and classification, evaluation and cross-validation, trees and gradient boosting, and deep learning (CNNs, transfer learning). A deployment track focuses on packaging and serving models (FastAPI APIs, Docker, cloud, serverless, ONNX Runtime, Kubernetes, optional KServe) plus monitoring and CI/CD. The program centers on building: a midterm end-to-end project and a capstone production system, emphasizing reproducible code, system design, and documentation, supported by a structured homework cadence and an active community.
 
 Examples of the final projects:
 
@@ -95,7 +95,7 @@ Examples of the final projects:
 5. **Duration:** ~2 months at ~10 hours/week (≈94 hours total)
 6. **Certificate:** Audit free; optional certificate available (paid)
 
-[“Machine Learning Specialization” by DeepLearning.AI](https://www.coursera.org/specializations/machine-learning-introduction){:target="_blank"} is a beginner-friendly, three-course program (taught by Andrew Ng) covers the fundamentals of modern machine learning and how to apply them in practice. You’ll build models in Python with NumPy and scikit-learn; implement supervised learning for regression and classification (linear/logistic regression, neural networks with TensorFlow, decision trees and tree ensembles); apply best practices for evaluation and data-centric improvement; and use unsupervised methods such as clustering and anomaly detection. The final course adds recommender systems (collaborative filtering, content-based deep learning) and an introduction to deep reinforcement learning.
+["Machine Learning Specialization" by DeepLearning.AI](https://www.coursera.org/specializations/machine-learning-introduction){:target="_blank"} is a beginner-friendly, three-course program (taught by Andrew Ng) covers the fundamentals of modern machine learning and how to apply them in practice. You'll build models in Python with NumPy and scikit-learn; implement supervised learning for regression and classification (linear/logistic regression, neural networks with PyTorch, decision trees and tree ensembles); apply best practices for evaluation and data-centric improvement; and use unsupervised methods such as clustering and anomaly detection. The final course adds recommender systems (collaborative filtering, content-based deep learning) and an introduction to deep reinforcement learning.
 
 ## 6. StanfordOnline: Statistical Learning with Python
 
@@ -224,7 +224,7 @@ Recorded from a live Caltech broadcast, [Yaser S. Abu-Mostafa’s Machine Learni
 5. **Duration:** Self-paced (project-based; varies by learner)
 6. **Certificate:** freeCodeCamp "Machine Learning with Python" Certification (upon completing required projects)
 
-["Machine Learning with Python" by FreeCodeCamp](https://www.freecodecamp.org/learn/machine-learning-with-python/#how-neural-networks-work){:target="_blank"} teaches practical machine learning with Python and TensorFlow. You’ll build several neural networks and explore core and advanced topics including how neural networks work, CNNs, RNNs/LSTMs, natural language processing, and an introduction to reinforcement learning. Instruction combines a TensorFlow course by Tim Ruscica (“Tech With Tim”) with conceptual videos by Brandon Rohrer. To earn the certificate, you complete hands-on projects such as Rock-Paper-Scissors, a Cat/Dog image classifier, a KNN book recommender, a linear-regression health-costs calculator, and an SMS text classifier, demonstrating applied skills across computer vision, NLP, recommendation, and regression.
+["Machine Learning with Python" by FreeCodeCamp](https://www.freecodecamp.org/learn/machine-learning-with-python/#how-neural-networks-work){:target="_blank"} teaches practical machine learning with Python and PyTorch. You'll build several neural networks and explore core and advanced topics including how neural networks work, CNNs, RNNs/LSTMs, natural language processing, and an introduction to reinforcement learning. Instruction combines a PyTorch course by Tim Ruscica ("Tech With Tim") with conceptual videos by Brandon Rohrer. To earn the certificate, you complete hands-on projects such as Rock-Paper-Scissors, a Cat/Dog image classifier, a KNN book recommender, a linear-regression health-costs calculator, and an SMS text classifier, demonstrating applied skills across computer vision, NLP, recommendation, and regression.
 
 ## 16. Practical Deep Learning for Coders by [Fast.ai](http://fast.ai){:target="_blank"}