Data Science Learning Library
I am curating resources for data science and sometimes about data engineering, following an ultralearning curriculum approach.
LinkedIn Saved Posts: https://www.linkedin.com/my-items/saved-posts/ (Personal Collection)
📚 Completed Courses
✅ MIT Free Course: Introduction To Probability And Statistics
Source: MIT OpenCourseWare
Status: Completed
Topics: Probability theory, statistical inference, hypothesis testing
✅ DeepLearning AI: Serverless LLM Apps with Amazon Bedrock
Source: DeepLearning.AI
Status: Completed
Topics: Large Language Models, AWS Bedrock, Serverless architecture
🍁 Google Cloud Skills Boost
Source: Google Cloud Skills
Status: Ongoing
Topics: Cloud computing, data engineering, machine learning on GCP
💡 Featured Learning Resources
Prompt Engineering Mastery
✅ Youssef Hosni’s Prompt Engineering Course
Repository: GitHub - Prompt Engineering for Instruction-Tuned LLM
Status: Completed
📖 Essential Articles & Papers
Neural Networks Innovation
“Kolmogorov-Arnold Networks: the latest advance in Neural Networks, simply explained”
Source: Towards Data Science
Topic: Advanced neural network architectures
LLM Deployment Comprehensive Guide
“Deploying LLMs: Top Learning & Educational Resources to Get Started”
Source: LinkedIn Article
1. Local Deployment
- 5 Free Tools to Run LLMs Locally: Guide
2. Demo Deployment
- Streamlit LLM App Building: Tutorial
- Generative AI with Gradio: Course
- FastAPI LLM Endpoints: Guide
- Flask ML Deployment: 60-Minute Tutorial
3. Server Deployment
- Amazon Bedrock Serverless Apps: Course
- HuggingFace LLM Containers: Documentation
- Philschmid’s Blog: Advanced Deployment
- Azure NVIDIA NeMo: Workshop
4. Edge Deployment
- Consumer Device LLMs: Research
- Android Hardware Acceleration: Tutorial
- TinyChat Edge Computing: Project
- NVIDIA IGX Orin: Developer Guide
- Mobile GenAI (Phi-2/Phi-3): Implementation
🛠️ Core Libraries & Tools
AutoGluon (Amazon’s AutoML)
Homepage: https://auto.gluon.ai/stable/index.html
Key Features:
- Quick Prototyping: Build ML solutions on raw data in few lines of code
- State-of-the-art Techniques: Automatically utilize SOTA models without expert knowledge
- Easy Deployment: Move from experimentation to production with cloud predictors
- Customizable: Extensible with custom feature processing, models, and metrics
Use Cases:
- Rapid ML prototyping
- Automated model selection
- Production-ready deployments
- Custom ML pipelines
🎯 Learning Paths
Beginner Track
Perfect for those starting their data science journey with no prior experience.
Phase 1: Foundations (4-6 weeks)
- Statistics Fundamentals
- Descriptive statistics
- Probability distributions
- Hypothesis testing
- Confidence intervals
- Python Programming Basics
- Variables and data types
- Control structures
- Functions and modules
- File handling
Phase 2: Data Manipulation (6-8 weeks)
- Pandas Library
- DataFrames and Series
- Data cleaning techniques
- Grouping and aggregation
- NumPy for Numerical Computing
- Array operations
- Mathematical functions
- Broadcasting
Intermediate Track
For learners with basic programming knowledge and statistics background.
Machine Learning Fundamentals (8-10 weeks)
- Supervised Learning
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines
- Unsupervised Learning
- K-means clustering
- Hierarchical clustering
- Principal component analysis
Advanced Track
Designed for experienced practitioners seeking specialization.
Deep Learning & NLP (10-12 weeks)
- Deep Learning with TensorFlow/Keras
- Neural network architectures
- Convolutional neural networks
- Recurrent neural networks
- Natural Language Processing
- Text preprocessing
- Sentiment analysis
- Transformer models
📊 Practical Projects
Beginner Projects
- Exploratory Data Analysis
- Dataset: Titanic survival data
- Skills: Pandas, Matplotlib, Seaborn
- Predictive Modeling
- Dataset: House prices
- Skills: Regression, feature engineering
Intermediate Projects
- Image Classification
- Dataset: CIFAR-10
- Skills: CNNs, transfer learning
- Natural Language Processing
- Dataset: Movie reviews sentiment
- Skills: Text processing, classification
Advanced Projects
- Recommendation System
- Dataset: MovieLens
- Skills: Collaborative filtering, matrix factorization
- Time Series Forecasting
- Dataset: Stock prices or weather data
- Skills: ARIMA, LSTM, Prophet
🎓 Career Guidance
Job Roles in Data Science
- Data Analyst
- Entry level position
- Skills: SQL, Excel, basic Python/R
- Data Scientist
- Mid-level position
- Skills: ML algorithms, statistics, programming
- Machine Learning Engineer
- Technical role
- Skills: Software engineering, MLOps, cloud
- Data Engineer
- Infrastructure focus
- Skills: Big data tools, cloud platforms
🤝 Community & Resources
Online Communities
- Reddit: r/MachineLearning, r/datascience
- Discord: Data Science Community servers
- Stack Overflow: Programming help
- Kaggle Forums: Competition discussions
Recommended Platforms
- Coursera: Machine Learning Specialization (Stanford)
- edX: MIT Introduction to Data Science
- Kaggle Learn: Free micro-courses
This library is continuously updated with new resources, tutorials, and industry trends. Check back regularly for the latest content and learning opportunities.
🇹🇷 Data Science öğrenme kaynaklarının Türkçe versiyonu için tıklayın