Data Science and Machine Learning - The-Learners-Community/RoadMaps-and-Resources GitHub Wiki

ROADMAP

Welcome to the Data Science and Machine Learning Roadmap! This guide is designed to take you from a beginner to an expert in Data Science and Machine Learning. Each section covers essential topics and skills you need to become proficient and dangerous.

Checkout roadmap.sh/ai-data-scientist

PROJECTS - Beginner to Master

Beginner Level

1. Data Cleaning and Exploration

  • Description: Take a messy dataset and perform data cleaning operations to prepare it for analysis.
  • Tasks:
    • Handle missing values
    • Remove duplicates
    • Correct data types
  • Technologies: Python, Pandas

2. Exploratory Data Analysis (EDA)

  • Description: Analyze a dataset to discover patterns, spot anomalies, and test hypotheses.
  • Tasks:
    • Descriptive statistics
    • Data visualization
  • Technologies: Python, Pandas, Matplotlib, Seaborn

3. Linear Regression Model

  • Description: Build a simple linear regression model to predict a continuous variable.
  • Tasks:
    • Train-test split
    • Model training
    • Evaluation using metrics like RMSE
  • Technologies: Python, Scikit-learn

4. Classification with Logistic Regression

  • Description: Develop a logistic regression model for binary classification tasks.
  • Tasks:
    • Data preprocessing
    • Model training and evaluation
  • Technologies: Python, Scikit-learn

5. K-Means Clustering

  • Description: Perform clustering on a dataset to identify inherent groupings.
  • Tasks:
    • Choose the optimal number of clusters
    • Visualize clusters
  • Technologies: Python, Scikit-learn

6. Decision Trees

  • Description: Build a decision tree classifier and understand how it makes decisions.
  • Tasks:
    • Feature selection
    • Tree visualization
  • Technologies: Python, Scikit-learn

7. Time Series Analysis

  • Description: Analyze and forecast data that is indexed over time.
  • Tasks:
    • Decompose time series
    • Forecasting using ARIMA models
  • Technologies: Python, Pandas, statsmodels

8. Sentiment Analysis

  • Description: Perform sentiment analysis on text data (e.g., movie reviews).
  • Tasks:
    • Text preprocessing
    • Model training with Naive Bayes
  • Technologies: Python, NLTK, Scikit-learn

9. Recommender System (Basic)

  • Description: Create a simple recommender system using collaborative filtering.
  • Tasks:
    • User-based or item-based filtering
  • Technologies: Python, Pandas, Scikit-learn

10. Image Classification with CNNs (Intro)

  • Description: Build a basic Convolutional Neural Network for image classification.
  • Tasks:
    • Data augmentation
    • Model training and evaluation
  • Technologies: Python, Keras or TensorFlow

Intermediate Level

11. Natural Language Processing with LSTM

  • Description: Develop a model to perform text generation or sentiment analysis using LSTM networks.
  • Tasks:
    • Text preprocessing
    • Sequence modeling
  • Technologies: Python, Keras or TensorFlow

12. Random Forest and Ensemble Methods

  • Description: Improve model performance using ensemble techniques like Random Forest and Gradient Boosting.
  • Tasks:
    • Hyperparameter tuning
    • Feature importance analysis
  • Technologies: Python, Scikit-learn, XGBoost

13. Support Vector Machines

  • Description: Implement SVM for classification tasks with non-linear decision boundaries.
  • Tasks:
    • Kernel trick application
    • Model evaluation
  • Technologies: Python, Scikit-learn

14. Principal Component Analysis (PCA)

  • Description: Perform dimensionality reduction using PCA.
  • Tasks:
    • Data scaling
    • Variance explanation
  • Technologies: Python, Scikit-learn

15. Clustering with DBSCAN and Hierarchical Methods

  • Description: Apply advanced clustering techniques to uncover data structures.
  • Tasks:
    • Parameter selection
    • Dendrogram creation
  • Technologies: Python, Scikit-learn, SciPy

16. Recommender System (Advanced)

  • Description: Build a hybrid recommender system combining collaborative and content-based filtering.
  • Tasks:
    • Feature engineering
    • Model integration
  • Technologies: Python, Surprise library

17. Deep Learning with CNNs

  • Description: Develop an advanced CNN for image recognition tasks like object detection.
  • Tasks:
    • Implement architectures like VGG, ResNet
    • Transfer learning
  • Technologies: Python, TensorFlow or PyTorch

18. Time Series Forecasting with LSTM

  • Description: Use LSTM networks to forecast complex time series data.
  • Tasks:
    • Sequence-to-sequence modeling
    • Multi-step forecasting
  • Technologies: Python, Keras or TensorFlow

19. Deployment of Machine Learning Models

  • Description: Deploy a machine learning model as a REST API.
  • Tasks:
    • Model serialization
    • API development with Flask or Django
  • Technologies: Python, Flask/Django, Docker

20. Reinforcement Learning (Intro)

  • Description: Implement basic reinforcement learning algorithms like Q-learning.
  • Tasks:
    • Environment setup
    • Agent training
  • Technologies: Python, OpenAI Gym

Advanced Level

21. Natural Language Processing with Transformers

  • Description: Use transformer architectures like BERT for NLP tasks.
  • Tasks:
    • Fine-tuning pre-trained models
    • Tokenization with subword algorithms
  • Technologies: Python, Hugging Face Transformers

22. Generative Adversarial Networks (GANs)

  • Description: Develop GANs to generate synthetic data like images.
  • Tasks:
    • Implement generator and discriminator networks
    • Training stabilization techniques
  • Technologies: Python, TensorFlow or PyTorch

23. Recommendation System with Deep Learning

  • Description: Build a recommendation system using deep learning techniques.
  • Tasks:
    • Embedding layers
    • Neural collaborative filtering
  • Technologies: Python, TensorFlow or PyTorch

24. Sequence-to-Sequence Models

  • Description: Implement seq2seq models for tasks like machine translation.
  • Tasks:
    • Encoder-decoder architecture
    • Attention mechanisms
  • Technologies: Python, TensorFlow or PyTorch

25. Object Detection and Segmentation

  • Description: Develop models for object detection (e.g., YOLO, Faster R-CNN) and image segmentation.
  • Tasks:
    • Data annotation
    • Model training and evaluation
  • Technologies: Python, TensorFlow or PyTorch

26. Building a Machine Learning Pipeline

  • Description: Create a full ML pipeline including data ingestion, preprocessing, model training, and deployment.
  • Tasks:
    • Pipeline automation
    • Monitoring and logging
  • Technologies: Python, Apache Airflow, MLflow

27. Time Series Forecasting with Prophet and Advanced Models

  • Description: Use advanced models like Facebook Prophet or DeepAR for time series forecasting.
  • Tasks:
    • Handling seasonality and trends
    • Model comparison
  • Technologies: Python, Prophet, AWS SageMaker

28. Anomaly Detection

  • Description: Implement anomaly detection algorithms for fraud detection or intrusion detection.
  • Tasks:
    • Unsupervised learning techniques
    • Evaluation using ROC curves
  • Technologies: Python, Scikit-learn, PyOD

29. Distributed Machine Learning

  • Description: Train machine learning models on large datasets using distributed computing.
  • Tasks:
    • Use of frameworks like Apache Spark or Dask
    • Parallel model training
  • Technologies: Python, PySpark, Dask

30. AutoML and Hyperparameter Optimization

  • Description: Automate the machine learning process including feature selection and hyperparameter tuning.
  • Tasks:
    • Implement tools like AutoKeras or H2O.ai
    • Bayesian optimization techniques
  • Technologies: Python, AutoKeras, Hyperopt

Happy coding and advancing your data science and machine learning skills!