opencampus.sh Machine Learning Program
  • opencampus.sh Machine Learning Program
  • Course Kick-Off
  • How do I choose a course?
  • FAQ
  • Courses
    • Introduction to Data Science and Machine Learning
      • Conditions for Receiving a Certificate or ECTS
      • Preparation
      • Week 1 - Introduction to Data Science
      • Week 2 - Import and Visualization of Data
      • Week 3 - Versioning with Git (Part 1) and data preparation
      • Week 4 - Versioning with Git (Part 2) and Current Developments in ML
      • Week 5 - Introduction to Machine Learning and Linear Regression
      • Woche 6 - Overfitting und Regularisierung
      • Woche 7 - Neuronale Netze
      • Woche 8 - Fehlende Werte
      • Woche 9 - Zeitreihenanalysen
      • Woche 10 - ProjektprƤsentationen
    • Machine Learning with TensorFlow
      • Requirements for a Certificate of Achievement or ECTS
      • Preparation
      • Week 1 - General Introduction
      • Week 2 - Introduction to TensorFlow,Part I
      • Week 3 - Introduction to TensorFlow,Part II
      • Week 4 - Convolutional Neural Networks, Part I
      • Week 5 - Convolutional Neural Networks, Part II
      • Week 6 - Natural Language Processing, Part I
      • Week 7 - Natural Language Processing, Part II
      • Week 8 - Project Work
      • Week 9 - Sequences, Time Series and Prediction, Part I
      • Week 10 - Sequences, Time Series and Prediction, Part II
      • Week 11 & 12 - Presentation of the Final Projects
    • Intermediate Machine Learning
      • Hello and welcomešŸ˜Š
      • Prequisites
      • Week 1 - Course Introduction
        • Cousera Videos
      • Week 2 - Recap ML Basics, Intro to PyTorch
        • Coursera Videos
      • Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
        • Coursera Videos
      • Week 4 - Convolutional Neural Networks
      • Week 5 - Recurrent Neural Networks
        • Cousera Videos
      • Week 6 - CNN and RNN Applications
        • Cousera Videos
      • Week 7 - Transformers & Hugging Face
      • Week 8-10 - Kaggle competiton sessions
      • Week 11 - Final Presentations
    • From LLMs to AI AgentsšŸ¤–
      • Hello and welcomešŸ¤–
      • Week 1 - Course Introduction
      • Week2 - RAG +Introduction to frameworks(langchain & llamaindex)
      • Week 3 - Prompt Engineering & Demo Chatbot
    • Advanced Time Series Prediction
      • Requirements for a Certificate of Achievement or ECTS
      • Projects & Frameworks
      • Lecture material + YouTube
      • References / Books
      • Week 1 - Intro + Organisation
      • Week 2 - SARIMA(X) + GARCH-Models
      • Week 3 - Labour Day
      • Week 4 - State-Space models // Filtering
      • Week 5 - Dependence concepts: Copula // Gaussian Processes // RMT
      • Week 6 - Extremes // Anomalies // Signatures
      • Week 7 - Tree models: XGBoost // LightGBM // CatBoost
      • Week 8 - (Deep) recurrent architectures for time series data
      • Week 9 - Transformers + TemporalFusionTransformers
      • Week 10 - NBEATS(x) + NHITS
      • Week 11 - LLM for time series problems
      • Week 12 - Final Presentations
      • Week 13 - Final Presentations (Back-Up)
    • Python: Beginner to Practitioner
      • Week 1
      • Week 2
      • Week 3
      • Week 4
      • Week 5
      • Week 6
      • Week 7
      • Final Project
      • Resources
        • Worklabs
        • Solutions
        • Harvard Course
    • Fine-Tuning and Deployment of Large Language Models
      • Requirements for a Certificate of Achievement or ECTS
      • Preparation
      • Week 1 - General Introduction
      • Week 2 - Project Definition and Introduction to Fine-Tuning
      • Week 3 - Fine-Tuning Characteristics
      • Week 4 - Model Evaluation
      • Week 8 - Tokenization for Instruction Tuning
      • Week 9 - Efficient Inference
      • Week 10 - Project Presentations
    • Archive
      • Deep Learning from Scratch
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Week 1 - General Introduction
        • Week 2 - Introduction to Deep Learning and Neural Network Basics
        • Week 3 - Shallow Neural Networks
        • Week 4 - Deep Neural Networks
        • Week 5 - Practical Aspects of Deep Learning
        • Week 6 - Optimization Algorithms
        • Week 7 - Hyperparameter Tuning
        • Week 8 - Machine Learning Strategy 1 & 2
        • Week 9 - Neural Networks Architecture | Project Checkpoint
        • Week 10 - Bonus: most voted topic
        • Week 11 - Presentation of Final Projects, Part I
        • Week 12 - Presentation of Final Projects, Part II
      • Deep Learning for Computer Vision
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Week 1 - General Introduction
        • Week 2 - Foundations of Convolutional Neural Networks
        • Week 3 - Convolution Model Application
        • Week 4 - Residual Networks
        • Week 5 - Transfer Learning
        • Week 6 - Detection Algorithms
        • Week 7 - Project Checkpoint | Image Segmentation
        • Week 8 - Face Recognition
        • Week 9 - Art Generation with Neural Style Transfer
        • Week 10 - CNN Bonus
        • Week 11 - Final Presentation of the Projects
      • Application of Transformer Models
        • Requirements for a Certificate of Achievement or ECTS
        • Week 1 - General Introduction
        • Week 2 - Self-Attention and Prompt Design
        • Week 3 - Introduction to Transformer Models
        • Week 4 - Fine-Tuning Pretrained Models
        • Week 5 - The Datasets Library
        • Week 6 - The Tokenizers Library
        • Week 7 - Main NLP Tasks
        • Week 8 - Presentation of the Final Projects
      • Generative Adversarial Networks
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Motivation - Things you can do with NLP
        • Week 1 - General Introduction to the course
        • Week 2 - Sentiment Analysis with Logistic Regression
        • Week 3 - Sentiment Analysis with NaĆÆve Bayes
        • Week 4 - Vector Space Models
        • Week 5 - Machine Translation and Document Search
        • Week 6 - Autocorrect
        • Week 7 - Part of Speech Tagging and Hidden Markov Models
        • Week 8 - Autocomplete and Language Models
        • Week 9 - Word embeddings with neural networks
        • Week 10 - Final Projects
      • Lehren und Lernen mit KI
        • Woche 1 - EinfĆ¼hrung
        • Woche 2 - Anwendungsbeispiele #twlz
        • Woche 3 - KI-Tools fĆ¼r den Bildungsbereich
        • Woche 4 - Nicht-technische EinfĆ¼hrung in die KI
        • Woche 5 - Kreatives Schreiben
        • Woche 6 - Automatische Klassifizierung von Textantworten
        • Woche 7 - IQSH Handreichung zu CHatGPT
        • Woche 8 - VerƤnderungen in benƶtigten Kompetenzen
        • Woche 9 - PrƤsentation Abschlussprojekte
      • Reinforcement Learning
      • Machine Learning Operations (MLOps)
        • 19-04-2023 - General Introduction
        • 26-04-2023 ML Lifecycle Overview and Model Selection
        • 03-05-2023 Data Definition and Collection
        • 10-05-2023 From Feature Engineering to Data Storage
        • 17-05-2023 Advanced Data Processing & Intro into Model Serving
        • 24-05-2023 Model Infrastructure & Delivery
        • 31-05-2023 Model Monitoring
        • 07-06-2023 Project Presentations
      • Mathematik fĆ¼r maschinelles Lernen
      • TensorFlow Course: Week 10 - Special Issues Considering Your Final Projects
      • Deep Dive into LLMs
        • Week 1 - Introduction
        • Week 2 - Tokens & Embeddings revisted
        • Week 3 - Introduction to Transformers
        • Week 4 - Prompt Engineering
          • Chain of Thought
          • TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks
          • More techniques
        • Week 5 - RAG and Agents
        • Week 6 - Model Evaluation
        • Week 7 - Fine-Tuning I
        • Week 8 - Fine-Tuning II and Model Inference
        • Week 9 - Advisory Session
        • Week 10 - Project Presentations
      • Intermediate Machine Learning (Legacy SS2023)
        • Hello and welcomešŸ˜Š
        • Prequisites
        • Week 1 - Course Introduction
        • Week 2 - Recap ML Basics, Intro to PyTorch
        • Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
        • Week 4 - Convolutional Neural Networks
        • Week 5 - Recurrent Neural Networks
        • Week 6 - CNN and RNN Applications
        • Week 7 - Transformers Part 1
        • Week 8 - Transformers Part 2
        • Week 9 - Vision Transformers
        • Week 10-12 - Projects sessions
        • Week 13 - Project Presentations
        • Week 14+
      • Practical Engineering with LLMs
        • Week 1- General Introduction
        • Week 2 - Prompt Engineering
        • Week 3 - Introduction to LangChain
        • Week 4 - Introduction to Retrieval Augmented Generation
        • Week 5 - Advanced Retrieval Augmented Generation
        • Week 6 - Building User Interfaces with Gradio
        • Week 7 - Evaluation of LLM outputs and structured outputs
        • Week 8 - Open-Source LLMs
        • Week 9 - Project Presentations
      • Python: From Beginner to Practictioner (Legacy WS2023)
        • Course Info
        • Week 1
        • Week 2
        • Week 3
        • Week 4
        • Week 5
        • Week 6
        • Week 7
        • Week 8
        • Week 9
        • Solutions & Materials
      • Machine Learning fĆ¼r die Medizin
        • Bedingungen fĆ¼r ein Leistungszertifikat oder ECTS
        • Vorbereitung
      • Time Series Prediction
        • Requirements for a Certificate of Achievement or ECTS
        • Projects & Frameworks
        • Preparation / YouTube
        • References / Books
        • Week 1 - Intro + Organisation
        • Week 2 - Forecasting basics with trends: AR + MA-models
        • Week 3 - Covering seasonality: From ARMA to SARIMA-models
        • Week 4 - Towards multidimensional settings: SARIMAX + VAR-models
        • Week 5 - Non-Stationary model classes: GARCH + DCC-GARCH
        • Week 6 - Copula Methods
        • Week 7 - Milestone Meeting + Spectral Analysis of Time Series + Kalman-Filtering
        • Week 8 - Supervised Learning I: Trees + Random Forests + Boosting
        • Week 9 - Supervised Learning II: XGBoost + LightGBM + CatBoost
        • Week 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUs
        • Week 11 - Prophet(Facebook) + DeepAR(Amazon) + GPVAR
        • Week 12 - Transformers + TFTs
        • Week 13 - NBEATS(s) + NHITS(x)
        • Week 14 - Final Presentation
      • Python: From Beginner to Practitioner (Legacy 2024S)
        • Course Info
        • Week 1
        • Week 2
        • Week 3
        • Week 4
        • Week 5
        • Week 6
        • Week 7
        • Week 8
        • Week 9
        • Week 10
        • Week 11
        • Week 12
        • Material
      • EinfĆ¼hrung in Data Science und maschinelles Lernen
        • Bedingungen fĆ¼r ein Leistungszertifikat oder ECTS
        • Vorbereitung
        • Woche 1 - EinfĆ¼hrung in Data Science
        • Woche 2 - Import und Visualisierung von Daten
        • Woche 3 - Versionierung mit git (Teil 1) und Datenaufbereitung
        • Woche 4 - Versionierung mit git (Teil 2) und aktuelle Entwicklungen im Bereich ML
        • Woche 5 - EinfĆ¼hrung in das maschinelle Lernen
        • Woche 6 - Overfitting und Regularisierung
        • Woche 7 - Neuronale Netze
        • Woche 8 - Fehlende Werte
        • Woche 9 - Zeitreihenanalysen
        • Woche 10 - ProjektprƤsentationen
      • Python: From Beginner to Practitioner (Legacy 2024W)
        • Course Info
        • Week 1
        • Week 2
        • Week 3
        • Week 4
        • Week 5
        • Week 6
        • Week 7
        • Week 8
        • Week 9
        • Week 10
        • Week 11
        • Final Project
        • Resources
  • Events
    • Coding.Waterkant 2023
    • Prototyping Week
  • Course Projects
    • Choosing a Project
    • How to Start, Complete, and Submit Your Project
  • Additional Resourses
    • Glossary
    • Coursera
    • Selecting the Optimizer
    • Choosing the Learning Rate
    • Learning Linear Algebra
    • Learning Python
    • Support Vector Machines
    • ML Statistics
  • Tools
    • Git
    • RStudio
    • Google Colab
    • Zoom
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  • This week we will...
  • Learning Resources
  • Until next week you should...

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  1. Courses
  2. Introduction to Data Science and Machine Learning

Week 5 - Introduction to Machine Learning and Linear Regression

PreviousWeek 4 - Versioning with Git (Part 2) and Current Developments in MLNextWoche 6 - Overfitting und Regularisierung

Last updated 13 days ago

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This week we will...

cover the following topics:

  • Procedure for training machine learning algorithms

  • Definition of linear regression

  • Cost functions

  • Optimization functions

  • Testing your model on Kaggle

Learning Resources

  • on Linear Regression

Until next week you should...

Watch the videos in the section "" from Week 3 of the course "Supervised Machine Learning: Regression and Classification" on Coursera.

Create an account on (using the same email address you used for EduHub).

Use a simple linear model to make a prediction for the test dataset and evaluate it on Kaggle. (Ensure that the number of rows and the defined IDs match those in the file!)

The problem of overfitting
Kaggle
here
sample_submission
DataCamp Tutorial
8MB
250522_Intro ML and Linear Regression.pptx
527KB
IntroMLandLinReg.ipynb