Essential Tools for Machine Learning Tutorials
Learn essential tools for machine learning: virtual environments, IDEs, Ubuntu, Anaconda, and TensorFlow. Gain practical skills to set up your development environment and start your ML journey.
Building World-Class NLP Models with Transformers and Hugging Face
Learn to build advanced NLP models using transformers and Hugging Face. Kaggle Grandmasters share insights on question answering, sentiment extraction, and multilingual classification in this comprehensive tutorial.
From Idea to Model: Productionizing Data Pipelines with Apache Airflow
Learn to productionize data pipelines using Apache Airflow, bridging data science and engineering. Explore collaborative workflows, custom operators, and scalable solutions for efficient model deployment and iteration.
Python for Data Science - Data Science Tutorial - Python Tutorial
Learn Python for data science: versatile programming, complex problem-solving, and real-world applications. Covers key concepts, tools, and techniques with hands-on examples and expert guidance.
How I Learned Data Science in 3 Months
Discover a unique memory technique for rapidly learning data science skills, including Python, machine learning, and deep learning, in just three months.
Introducing Kubeflow with TensorFlow and Apache Spark
Explore Kubeflow's role in streamlining machine learning workflows, from model development to production deployment on Kubernetes, with insights on scalability and integration with TensorFlow and Apache Spark.
Deploying ML at Scale with Kubernetes and TFX
Learn to deploy machine learning models at scale using Kubernetes and TensorFlow Extended (TFX), enhancing your ability to manage and scale ML workflows efficiently.
Building and Operating an Open Source Data Science Platform
Develop an end-to-end data analytics pipeline, from exploratory analysis to model deployment. Learn pipeline orchestration, distributed training, automation, and monitoring for scalable machine learning projects.
Introduction to TensorFlow - Hands-on Workshop
Explore TensorFlow through hands-on practice, completing an end-to-end tutorial to gain practical skills in building and deploying machine learning models.
How to Learn Python - Python Roadmap in 2024
Comprehensive Python guide covering basics to advanced topics. Learn essential skills for software development, data science, and machine learning. Ideal for beginners and professionals seeking to enhance their Python expertise.
Real-Time Image Processing AI/ML with Jupyter Notebooks
Explore real-time image processing using AI/ML techniques with Jupiter notebooks, enhancing your skills in cutting-edge technology applications.
Training a Long-Short Term Memory Network for Melody Generation
Build and train an LSTM network for generating folk melodies using Tensorflow and Keras. Learn preprocessing, model architecture, training sequences, and testing the final model.
Emerging Technologies for Quantitative Interpretation of Carbonate Rock Images
Explore machine learning applications for automatic identification of carbonate facies using core images, improving accuracy and consistency in geological classifications.
Expert API LLM Gorilla-7B: Writing Code for APIs Using Large Language Models
Discover how Gorilla-7B, an API-optimized language model, generates complete code sequences from natural language prompts for tasks like image recognition and language translation.
AI on the Edge with Eclipse Kura - From Data Collection to Model Deployment
Discover how to build and deploy machine learning applications on edge devices using Eclipse Kura, from data collection through Kura Wires to model training and inference with Nvidia Triton Server.
Machine Learning Crash Course
Learn to build neural networks and recommendation engines using JavaScript and TensorFlow.js in this beginner-friendly introduction to machine learning.
How to Deploy ML Models with Google Cloud Run
Learn to deploy ML models using Google Cloud Run, from building a TensorFlow app with Flask to containerizing with Docker and final deployment.
Predicting Diabetes with Logistic Regression in TensorFlow.js - Deep Learning for JavaScript Hackers
Learn to build and train logistic regression models in TensorFlow.js for diabetes prediction. Covers data visualization, dataset creation, model evaluation, and advanced techniques.
Jetson Xavier NX Developer Kit - Edge AI Supercomputer Features and Applications
Explore Jetson Xavier NX Developer Kit's edge AI capabilities, software features, and deployment strategies for advanced applications in video analytics, perception, and natural language processing.
How to Use Machine Learning AI in Unity - ML-Agents
Unlock the power of Machine Learning AI in Unity with ML-Agents. Learn installation, agent creation, training, and visualization to enhance your game development skills.
Deploy Tensorflow.Js Object Detection Apps - Computer Vision React.Js
Learn to deploy Tensorflow.js and React.js computer vision apps to IBM Cloud in 20 minutes. Covers app preparation, deployment, environment setup, and real-time object detection implementation.