Home └Databases, Web development and Machine Learning - Computer Science 5
Databases, Web development and Machine Learning - Computer Science 5

Databases, Web development and Machine Learning - Computer Science 5


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Responsible Lorcan Camps
Last Update 24/02/2022
Completion Time 4 days 6 hours 33 minutes
Members 8
Advanced Technical Computer Science
  • Databases (sql)
    • Database Design Course - Learn how to design and plan a database for beginners
    • SQL Tutorial - Full Database Course for Beginners
  • Web application development
    • Python Django Web Framework - Full Course for Beginners
    • Python Django Tutorial: Full-Featured Web App Part 1 - Getting Started
    • Python Django Tutorial: Full-Featured Web App Part 2 - Applications and Routes
    • Python Django Tutorial: Full-Featured Web App Part 3 - Templates
    • Python Django Tutorial: Full-Featured Web App Part 4 - Admin Page
    • Python Django Tutorial: Full-Featured Web App Part 5 - Database and Migrations
    • Python Django Tutorial: Full-Featured Web App Part 6 - User Registration
    • Python Django Tutorial: Full-Featured Web App Part 7 - Login and Logout System
    • Python Django Tutorial: Full-Featured Web App Part 8 - User Profile and Picture
    • Python Django Tutorial: Full-Featured Web App Part 9 - Update User Profile
    • Python Django Tutorial: Full-Featured Web App Part 10 - Create, Update, and Delete Posts
    • Python Django Tutorial: Full-Featured Web App Part 11 - Pagination
    • Python Django Tutorial: Full-Featured Web App Part 12 - Email and Password Reset
    • Python Django Tutorial: Deploying Your Application (Option #1) - Deploy to a Linux Server
    • Python Django Tutorial: How to Use a Custom Domain Name for Our Application
    • Python Django Tutorial: How to enable HTTPS with a free SSL/TLS Certificate using Let's Encrypt
    • Python Django Tutorial: Full-Featured Web App Part 13 - Using AWS S3 for File Uploads
    • Python Django Tutorial: Deploying Your Application (Option #2) - Deploy using Heroku
  • Machine learning
    • Lecture 1 - Stanford CS229: Machine Learning - Andrew Ng (Autumn 2018)
    • Lecture 2 - Linear Regression and Gradient Descent | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 3 - Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 7 - Kernels | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 11 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 12 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 13 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 15 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018)
    • Lecture 20 - RL Debugging and Diagnostics | Stanford CS229: Machine Learning (Autumn 2018)
    • Practical Machine Learning Tutorial with Python Intro p.1
    • Regression Intro - Practical Machine Learning Tutorial with Python p.2
    • Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3
    • Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4
    • Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5
    • Pickling and Scaling - Practical Machine Learning Tutorial with Python p.6
    • Regression How it Works - Practical Machine Learning Tutorial with Python p.7
    • How to program the Best Fit Slope - Practical Machine Learning Tutorial with Python p.8
    • How to program the Best Fit Line - Practical Machine Learning Tutorial with Python p.9
    • R Squared Theory - Practical Machine Learning Tutorial with Python p.10
    • Programming R Squared - Practical Machine Learning Tutorial with Python p.11
    • Testing Assumptions - Practical Machine Learning Tutorial with Python p.12
    • Classification w/ K Nearest Neighbors Intro - Practical Machine Learning Tutorial with Python p.13
    • K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14
    • Euclidean Distance - Practical Machine Learning Tutorial with Python p.15
    • Creating Our K Nearest Neighbors Algorithm - Practical Machine Learning with Python p.16
    • Writing our own K Nearest Neighbors in Code - Practical Machine Learning Tutorial with Python p.17
    • Applying our K Nearest Neighbors Algorithm - Practical Machine Learning Tutorial with Python p.18
    • Final thoughts on K Nearest Neighbors - Practical Machine Learning Tutorial with Python p.19
    • Support Vector Machine Intro and Application - Practical Machine Learning Tutorial with Python p.20
    • Understanding Vectors - Practical Machine Learning Tutorial with Python p.21
    • Support Vector Assertion - Practical Machine Learning Tutorial with Python p.22
    • Support Vector Machine Fundamentals - Practical Machine Learning Tutorial with Python p.23
    • Support Vector Machine Optimization - Practical Machine Learning Tutorial with Python p.24
    • Creating an SVM from scratch - Practical Machine Learning Tutorial with Python p.25
    • SVM Training - Practical Machine Learning Tutorial with Python p.26
    • SVM Optimization - Practical Machine Learning Tutorial with Python p.27
    • Completing SVM from Scratch - Practical Machine Learning Tutorial with Python p.28
    • Kernels Introduction - Practical Machine Learning Tutorial with Python p.29
    • Why Kernels - Practical Machine Learning Tutorial with Python p.30
    • Soft Margin SVM - Practical Machine Learning Tutorial with Python p.31
    • Soft Margin SVM and Kernels with CVXOPT - Practical Machine Learning Tutorial with Python p.32
    • SVM Parameters - Practical Machine Learning Tutorial with Python p.33
    • Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
    • Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35
    • K Means with Titanic Dataset - Practical Machine Learning Tutorial with Python p.36
    • Custom K Means - Practical Machine Learning Tutorial with Python p.37
    • K Means from Scratch - Practical Machine Learning Tutorial with Python p.38
    • Mean Shift Intro - Practical Machine Learning Tutorial with Python p.39
    • Mean Shift with Titanic Dataset - Practical Machine Learning Tutorial with Python p.40
    • Mean Shift from Scratch - Practical Machine Learning Tutorial with Python p.41
    • Mean Shift Dynamic Bandwidth - Practical Machine Learning Tutorial with Python p.42
    • Deep Learning with Neural Networks and TensorFlow Introduction
    • Installing TensorFlow (OPTIONAL) - Deep Learning with Neural Networks and TensorFlow p2.1
    • TensorFlow Basics - Deep Learning with Neural Networks p. 2
    • Neural Network Model - Deep Learning with Neural Networks and TensorFlow
    • Running our Network - Deep Learning with Neural Networks and TensorFlow
    • Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5
    • Preprocessing cont'd - Deep Learning with Neural Networks and TensorFlow part 6
    • Training/Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7
    • Using More Data - Deep Learning with Neural Networks and TensorFlow part 8
    • Installing the GPU version of TensorFlow for making use of your CUDA GPU
    • Installing CPU and GPU TensorFlow on Windows
    • Recurrent Neural Networks (RNN) - Deep Learning with Neural Networks and TensorFlow 10
    • RNN Example in Tensorflow - Deep Learning with Neural Networks 11
    • Convolutional Neural Networks Basics - Deep Learning withTensorFlow 12
    • Convolutional Neural Networks with TensorFlow - Deep Learning with Neural Networks 13
    • TFLearn - Deep Learning with Neural Networks and TensorFlow p. 14
    • Intro - Training a neural network to play a game with TensorFlow and Open AI
    • Training Data - Training a neural network to play a game with TensorFlow and Open AI p.2
    • Training Model - Training a neural network to play a game with TensorFlow and Open AI p.3
    • Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4
    • Intro and preprocessing - Using Convolutional Neural Network to Identify Dogs vs Cats p. 1
    • Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2
    • Training - Using Convolutional Neural Network to Identify Dogs vs Cats p. 3
    • Using our Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 4
    • Introduction - 3D Convolutional Neural Network w/ Kaggle Lung Cancer Detection Competiton p.1
    • Reading Files - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.2
    • Visualizing - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.3
    • Resizing Data - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.4
    • Preprocessing data - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.5
    • Running the Network - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.6
  • Client-side development with react
    • Learn React JS - Full Course for Beginners - Tutorial 2019
  • Distributed computing & systems
    • Lecture 1: Introduction
    • Lecture 2: RPC and Threads
    • Lecture 3: GFS
    • Lecture 4: Primary-Backup Replication
    • Lecture 5: Go, Threads, and Raft
    • Lecture 6: Fault Tolerance: Raft (1)
    • Lecture 7: Fault Tolerance: Raft (2)
    • Lecture 8: Zookeeper
    • Lecture 9: More Replication, CRAQ
    • Lecture 10: Cloud Replicated DB, Aurora
    • Lecture 11: Cache Consistency: Frangipani
    • Lecture 12: Distributed Transactions
    • Lecture 13: Spanner
    • Lecture 14: Optimistic Concurrency Control
    • Lecture 15: Big Data: Spark
    • Lecture 16: Cache Consistency: Memcached at Facebook
    • Lecture 17: COPS, Causal Consistency
    • Lecture 18: Fork Consistency, Certificate Transparency
    • Lecture 19: Bitcoin
    • Lecture 20: Blockstack