VIDEO in Danish
VIDEO in English
WHY I’M EXCITED ABOUT DTU’S APPLIED MACHINE LEARNING & BIG DATA COURSE 🚀
When I first heard about 62533/62T22 – Applied Machine Learning & Big Data at DTU, I knew it was something I had to be part of. As someone fascinated by AI, data science, and real-world problem-solving, this course offers exactly what I’ve been looking for—a perfect mix of statistics, mathematical modeling, and computational methods to tackle massive data challenges.
WHY THIS COURSE STANDS OUT 🔥
What makes this course special is how theory meets practice. It’s not just about reading concepts from a book; it’s about building models, coding in Python, and seeing AI in action. From recognizing handwritten digits to working with real-world datasets, everything is designed to give hands-on experience.
📌 Key Highlights:
✅ Learning machine learning algorithms that power AI applications
✅ Getting hands-on with Python, TensorFlow, and data science tools
✅ Understanding the math behind the models (because ML isn’t magic, it’s math!)
✅ Working on projects that mimic real-world AI problems
✅ Gaining insights from industry trends & research
WHAT I’M MOST EXCITED ABOUT 🚀
Honestly, what excites me the most is the systematic approach to ML. The course is structured like an actual machine learning pipeline, taking us through:
1️⃣ Data preparation – Because raw data is messy!
2️⃣ Model training & optimization – Making machines “learn” efficiently
3️⃣ Evaluation & deployment – Testing models before they go live
This structure makes learning feel like building something real rather than just memorizing formulas.
WHY DTU IS THE PERFECT PLACE FOR THIS COURSE
DTU is known for its cutting-edge research and focus on practical engineering. The learning environment is hands-on, encouraging students to experiment, fail, debug, and improve—exactly the mindset needed for AI & ML.
Plus, DTU provides access to high-performance computing clusters for training complex models. And if you don’t have a powerful GPU? Google Colab to the rescue!
LOOKING FORWARD TO THE EXAMS & PROJECTS 🎯
This isn’t just a “study-and-forget” kind of course. The projects are designed to test how well we apply what we’ve learned. The final exam won’t just be about definitions—it’s about understanding how ML works in real-world applications.
I see this course as more than just a class—it’s a step toward mastering AI, Big Data, and Data Science.
If you’re also taking this course, let’s connect and exchange ideas! What part of ML excites you the most? Drop a comment below! 👇🔥
SUMMARY OF THE FIRST LECTURE (EASY VERSION) 🚀
WHAT IS ARTIFICIAL INTELLIGENCE (AI)?
- AI is when computers think and act like humans to solve problems.
- AI is growing fast—by 2025, it will generate $126 billion globally.
- AI is used in e-commerce (recommendations), chatbots, cars (self-driving), and science.
WHAT IS MACHINE LEARNING (ML)?
- ML is a part of AI where computers learn from data instead of being directly programmed.
- Example: Handwriting recognition – a model learns to read numbers from images.
HOW ML WORKS (IN SIMPLE STEPS)
- Get Data – Example: Images of handwritten numbers.
- Process Data – Convert images into numbers a computer can read.
- Build Model – Create a learning system using Python (like a recipe).
- Train Model – Feed it lots of data so it improves.
- Test Model – Check how well it recognizes new numbers.
COURSE STRUCTURE
- Lectures 📖 → Learn concepts & coding.
- Projects 💻 → Apply ML to real-world problems.
- Python 🐍 → The main language for ML coding.
HOW TO FOLLOW THE COURSE
✅ Do:
- Understand the math & code behind ML.
- Solve quizzes and practice coding.
❌ Don’t:
- Skip lectures & expect to learn ML without coding!
MACHINE LEARNING SYSTEMS
- ML Pipeline → A step-by-step process:
- Collect Data → Train Model → Optimize → Test → Deploy 🚀
- Tools Used → Jupyter Notebook, Python, TensorFlow.
PRACTICAL TASKS TO GET STARTED
- Install Python & Jupyter from Anaconda.
- Set up VSCode for coding.
- Clone the course repository from GitHub.
CONCLUSION
This lecture is a starting point for understanding ML. You’ll learn how to make computers “think” using data! 🔥
Let me know if you need any simpler explanations or coding help! 😊
SUMMARY OF THE FIRST LECTURE (EASY VERSION) 🚀
WHAT IS ARTIFICIAL INTELLIGENCE (AI)?
- AI is when computers think and act like humans to solve problems.
- AI is growing fast—by 2025, it will generate $126 billion globally.
- AI is used in e-commerce (recommendations), chatbots, cars (self-driving), and science.
WHAT IS MACHINE LEARNING (ML)?
- ML is a part of AI where computers learn from data instead of being directly programmed.
- Example: Handwriting recognition – a model learns to read numbers from images.
HOW ML WORKS (IN SIMPLE STEPS)
- Get Data – Example: Images of handwritten numbers.
- Process Data – Convert images into numbers a computer can read.
- Build Model – Create a learning system using Python (like a recipe).
- Train Model – Feed it lots of data so it improves.
- Test Model – Check how well it recognizes new numbers.
COURSE STRUCTURE
- Lectures 📖 → Learn concepts & coding.
- Projects 💻 → Apply ML to real-world problems.
- Python 🐍 → The main language for ML coding.
HOW TO FOLLOW THE COURSE
✅ Do:
- Understand the math & code behind ML.
- Solve quizzes and practice coding.
❌ Don’t:
- Skip lectures & expect to learn ML without coding!
MACHINE LEARNING SYSTEMS
- ML Pipeline → A step-by-step process:
- Collect Data → Train Model → Optimize → Test → Deploy 🚀
- Tools Used → Jupyter Notebook, Python, TensorFlow.
PRACTICAL TASKS TO GET STARTED
- Install Python & Jupyter from Anaconda.
- Set up VSCode for coding.
- Clone the course repository from GitHub.
CONCLUSION
This lecture is a starting point for understanding ML. You’ll learn how to make computers “think” using data! 🔥
Let me know if you need any simpler explanations or coding help! 😊
🛠 STRUKTUREN AF ANACONDA
🛠 STRUKTUREN AF ANACONDA
🔹 Anaconda → Hovedplatformen, der samler alt 📦
- 📌 Indeholder Python
- 📌 Har et brugervenligt interface (Anaconda Navigator)
🔹 Programmer under Anaconda: 1️⃣ Jupyter Notebook 📒 → Bruges til at skrive og køre Python-kode i små blokke (perfekt til data science & ML)
2️⃣ VS Code 🖥 → En avanceret kodeeditor, god til Python & større projekter
3️⃣ Git 🔄 → Versionsstyring og samarbejde (GitHub bruges ofte til ML-projekter)
🔹 Python Libraries (til ML & Data Science):
- NumPy → Matematiske beregninger
- Pandas → Datahåndtering
- Matplotlib → Visualiseringer
- TensorFlow → Machine Learning
- Scikit-learn → Klassisk ML
🛠 HVORDAN VIRKER DET?
✅ Anaconda styrer miljøer → Du kan installere specifikke versioner af Python & pakker uden at ødelægge dit system
✅ Du vælger dit program (Jupyter, VS Code, Terminal) → Afhængigt af hvad du vil lave
✅ Alt arbejder sammen → F.eks. kan du åbne Jupyter Notebook fra Anaconda eller køre Python i VS Code
🚀 HVAD SKAL DU BRUGE?
1️⃣ Hvis du laver Machine Learning & Data Science → Brug Jupyter Notebook
2️⃣ Hvis du laver generel Python-udvikling → Brug VS Code
3️⃣ Hvis du arbejder med Git & versionsstyring → Brug GitHub
💡 Sammenligning:
Tænk på Anaconda som en værktøjskasse – den giver dig alle værktøjer, men du vælger selv, hvilke du bruger! 🔥
Har du brug for hjælp til at starte et specifikt projekt eller køre en kode? 😊
You said:
ChatGPT said:
FORSKELLEN MELLEM GIT & GITHUB 🤔
Git og GitHub bliver ofte forvekslet, men de er ikke det samme! Her er forskellen forklaret simpelt:
🔹 GIT = Versionsstyringsværktøj (Lokal)
- Git er et program, der hjælper dig med at gemme, spore og håndtere ændringer i din kode.
- Det fungerer lokalt på din computer.
- Hjælper med at gå tilbage til tidligere versioner af din kode.
- Bruges med terminal-kommandoer, f.eks.:
👉 Kort sagt: Git er et versionsstyringsværktøj, der arbejder på din egen computer.
🔹 GITHUB = Online Lagerplads (Cloud)
- GitHub er en hjemmeside (cloud service), der hoster Git-projekter online.
- Gør samarbejde nemt, så flere udviklere kan arbejde på samme projekt.
- Bruges til backup af din kode og til at dele med andre.
- Arbejder sammen med Git, så du kan “pushe” din kode online:
👉 Kort sagt: GitHub er en online platform, hvor du kan gemme og dele dine Git-projekter.
📌 HURTIG SAMMENLIGNING
| 🔥 Funktion | 🖥 Git (Lokalt) | ☁️ GitHub (Online) |
|---|---|---|
| Versionsstyring | ✅ Ja | ❌ Nej (bruger Git) |
| Arbejder lokalt? | ✅ Ja | ❌ Nej |
| Arbejder online? | ❌ Nej | ✅ Ja |
| Del kode med andre? | ❌ Nej | ✅ Ja |
| Samarbejde i teams? | ❌ Nej | ✅ Ja |
| Backup af kode? | ❌ Nej | ✅ Ja |
🔹 HVORDAN BRUGES GIT & GITHUB SAMMEN?
1️⃣ Opret et Git-projekt på din computer (git init)
2️⃣ Spor ændringer med Git (git add . → git commit -m "ændring")
3️⃣ Upload (push) til GitHub (git push origin main)
4️⃣ Hent (pull) ændringer fra GitHub (git pull origin main)
🚀 HVAD BØR DU BRUGE?
✅ Hvis du kun arbejder lokalt → Brug Git
✅ Hvis du vil dele dit projekt eller samarbejde med andre → Brug Git + GitHub
Vil du have hjælp til at opsætte Git + GitHub i Anaconda eller VS Code? 😊
