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ML Learning Paths

Data Science Learning Path

We present 12 topics in the data science learning path, providing learning objectives, related skills, subtopics, and references/resources for each. The goal is to give graduate students a structured and comprehensive program to acquire data science expertise, including hands-on experience with real-world open-source tools and libraries.

timeline
    title Machine Learning Learning Path
    A. General Data Science : 1. Introduction to Data Science and Machine Learning
                         : 2. Python for Data Science
                 : 3. Ethical Considerations in Data Science
    B. Statistics : 4. Statistical Learning and Regression Models
    C. Classical Machine Learning : 5. Classification Algorithms
                 : 6. Ensemble Methods
                 : 7. Unsupervised Learning
    D. Deep Learning : 8. Introduction to Deep Learning
                : 9. Recurrent Neural Networks and Sequence Models
            : 10. Generative Models
            : 11. Transfer Learning and Fine-tuning
    E. Continuous Integration / Continuous Deployment  : 12. Model Deployment and Productionization

A: General Data Science

1. Introduction to Data Science and Machine Learning

Topic description

Learning Objective: Understand the fundamental concepts of data science and machine learning, and their real-world applications.

Related Skills:

  • Defining and framing data science problems
  • Identifying appropriate machine learning techniques for different tasks
  • Distinguishing between supervised and unsupervised learning

Subtopics:

  • Definition and scope of data science
  • Overview of machine learning algorithms (regression, classification, clustering)
  • Applications of data science in various industries (e.g., healthcare, finance, marketing)
  • Ethical considerations in data science
  • Hands-on introduction to machine learning using Python and scikit-learn

References and Resources:

  • "An Introduction to Statistical Learning" by Gareth James et al.
  • "Machine Learning for Absolute Beginners" by Oliver Theobald
  • Kaggle Learn courses on data science and machine learning fundamentals

2. Python for Data Science

Topic description

Learning Objective: Develop proficiency in using Python for data manipulation, analysis, and visualization.

Related Skills:

  • Mastering Python syntax and data structures
  • Utilizing NumPy for efficient numerical operations
  • Applying Pandas for data ingestion, cleaning, and transformation

Subtopics:

  • Python programming basics (variables, data types, control structures, functions)
  • NumPy arrays and universal functions
  • Pandas DataFrames and Series for data manipulation
  • Data visualization with Matplotlib and Seaborn
  • Integrating Python with data science libraries (scikit-learn, TensorFlow, PyTorch)

References and Resources:

  • "Python for Data Analysis" by Wes McKinney
  • "Python Data Science Handbook" by Jake VanderPlas
  • Datacamp's Python for Data Science Track

3. Ethical Considerations in Data Science

Topic description

Learning Objective: Develop an understanding of the ethical implications and responsible practices in data science.

Related Skills:

  • Identifying and mitigating bias in data and models
  • Ensuring fair and equitable decision-making
  • Protecting privacy and data security

Subtopics:

  • Bias and fairness in machine learning
  • Interpretability and explainability of models
  • Privacy-preserving techniques (differential privacy, federated learning)
  • Data provenance and provenance tracking
  • Responsible AI principles and guidelines

References and Resources:

  • "Ethical Algorithms" by Michael Kearns and Aaron Roth
  • "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
  • Coursera course "AI Ethics" by DeepLearning.AI

B: Statistics

4. Statistical Learning and Regression Models

Topic description

Learning Objective: Understand and apply statistical learning techniques, with a focus on regression models.

Related Skills:

  • Fitting and evaluating linear regression models
  • Applying logistic regression for classification tasks
  • Interpreting model coefficients and making predictions

Subtopics:

  • Simple and multiple linear regression
  • Assumptions and diagnostics of linear regression
  • Logistic regression for binary classification
  • Evaluating model performance (R-squared, accuracy, precision, recall, F1-score)
  • Regularization techniques (Ridge, Lasso, Elastic Net)

References and Resources:

  • "An Introduction to Statistical Learning" by Gareth James et al.
  • "Pattern Recognition and Machine Learning" by Christopher Bishop
  • Coursera course "Machine Learning" by Andrew Ng

C: Classical Machine Learning

5. Classification Algorithms

Topic description

Learning Objective: Acquire knowledge of various classification algorithms and their application in real-world problems.

Related Skills:

  • Implementing and evaluating decision tree classifiers
  • Applying k-nearest neighbors for classification
  • Understanding the principles of support vector machines

Subtopics:

  • Decision tree classification
  • K-nearest neighbors (KNN) algorithm
  • Support vector machines (SVMs)
  • Evaluating classification models (accuracy, precision, recall, F1-score, ROC-AUC)
  • Handling class imbalance (oversampling, undersampling, SMOTE)

References and Resources:

  • "Pattern Recognition and Machine Learning" by Christopher Bishop
  • "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron
  • Udacity course "Intro to Machine Learning"

6. Ensemble Methods

Topic description

Learning Objective: Explore ensemble techniques for improving the performance of machine learning models.

Related Skills:

  • Implementing random forest algorithms
  • Understanding the principles of gradient boosting
  • Applying bagging and boosting techniques to enhance model accuracy

Subtopics:

  • Random forest classification and regression
  • Gradient boosting with XGBoost and LightGBM
  • Bagging and boosting (AdaBoost, Gradient Boosting)
  • Hyperparameter tuning for ensemble methods
  • Feature importance and interpretation in ensemble models

References and Resources:

  • "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron
  • "Introduction to Statistical Learning" by Gareth James et al.
  • Kaggle micro-course on "Advanced Ensembling"

7. Unsupervised Learning

Topic description

Learning Objective: Gain proficiency in unsupervised learning techniques for data exploration and pattern discovery.

Related Skills:

  • Implementing K-means clustering algorithms
  • Applying principal component analysis (PCA) for dimensionality reduction
  • Identifying anomalies and outliers in data

Subtopics:

  • K-means clustering
  • Hierarchical clustering
  • Principal component analysis (PCA)
  • Anomaly detection techniques (Isolation Forest, One-Class SVM)
  • Dimensionality reduction methods (t-SNE, UMAP)

References and Resources:

  • "Pattern Recognition and Machine Learning" by Christopher Bishop
  • "Hands-On Unsupervised Learning Using Python" by Ankur Patel
  • Coursera course "Cluster Analysis in Data Mining" by University of Illinois

D: Deep Learning

8. Introduction to Deep Learning

Topic description

Learning Objective: Develop an understanding of the fundamental concepts and architectures of deep neural networks.

Related Skills:

  • Constructing and training feedforward neural networks
  • Applying convolutional neural networks for image-related tasks
  • Selecting appropriate activation functions and optimization techniques

Subtopics:

  • Artificial neural networks (ANNs) and their structure
  • Activation functions (sigmoid, ReLU, tanh)
  • Feedforward neural networks and their training
  • Convolutional neural networks (CNNs) for image recognition
  • Hyperparameter tuning and optimization techniques

References and Resources:

  • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • "Deep Learning with Python" by François Chollet
  • Coursera course "Deep Learning Specialization" by deeplearning.ai

9. Recurrent Neural Networks and Sequence Models

Topic description

Learning Objective: Understand the principles of recurrent neural networks and their applications in sequence-to-sequence problems.

Related Skills:

  • Implementing LSTM and GRU models for sequence modeling
  • Applying recurrent neural networks for time series forecasting
  • Generating text and other sequential data using RNNs

Subtopics:

  • Recurrent neural networks (RNNs)
  • Long short-term memory (LSTMs)
  • Gated recurrent units (GRUs)
  • Sequence-to-sequence modeling
  • Time series forecasting with RNNs

References and Resources:

  • "Deep Learning for Time Series Forecasting" by Jason Brownlee
  • "Natural Language Processing with Python" by Steven Bird et al.
  • Coursera course "Sequence Models" by deeplearning.ai

10. Generative Models

Topic description

Learning Objective: Explore generative models and their applications in synthesizing new data.

Related Skills:

  • Implementing generative adversarial networks (GANs)
  • Applying variational autoencoders (VAEs) for image and text generation
  • Evaluating the performance of generative models

Subtopics:

  • Generative adversarial networks (GANs)
  • Variational autoencoders (VAEs)
  • Generative modeling for images, text, and other data types
  • Evaluating generative models (Inception Score, FID, BLEU)
  • Applications of generative models (data augmentation, creative generation)

References and Resources:

  • "Generative Adversarial Networks" by Ian Goodfellow et al.
  • "Variational Autoencoders" by Diederik Kingma and Max Welling
  • Deeplearning.ai course "Generative Adversarial Networks (GANs)"

11. Transfer Learning and Fine-tuning

Topic description

Learning Objective: Understand the principles of transfer learning and how to leverage pre-trained models for various tasks.

Related Skills:

  • Applying feature extraction with pre-trained models
  • Finetuning pre-trained models for domain-specific tasks
  • Evaluating the performance of transfer learning approaches

Subtopics:

  • Concept of transfer learning
  • Feature extraction using pre-trained models (e.g., VGG, ResNet, BERT)
  • Finetuning pre-trained models for specific applications
  • Domain adaptation and dataset shift
  • Evaluating transfer learning performance

References and Resources:

  • "Transfer Learning with Deep Learning" by Sebastian Ruder
  • "Practical Deep Learning for Cloud, Mobile, and Edge" by Anirudh Koul et al.
  • Coursera course "Convolutional Neural Networks" by deeplearning.ai

E: Continuous Integration / Continuous Deployment

12. Model Deployment and Productionization

Topic description

Learning Objective: Gain knowledge on how to deploy and maintain machine learning models in production environments.

Related Skills:

  • Containerizing models using Docker
  • Deploying models on cloud platforms (e.g., AWS, GCP, Azure)
  • Monitoring and maintaining production models

Subtopics:

  • Containerization with Docker
  • Cloud deployment on AWS, GCP, and Azure
  • Serving models with Flask, FastAPI, or Streamlit
  • Model monitoring and logging
  • Continuous integration and deployment (CI/CD) pipelines

References and Resources:

  • "Deploying Machine Learning Models" by Abhishek Thakur
  • "Kubernetes in Action" by Marko Lukša
  • Coursera course "Machine Learning Engineering for Production (MLOps)" by deeplearning.ai

Working with different data types.

Next you will find five specialized data science learning paths that branch off from the core topics in the previous section. Each specialized path includes a learning objective, related skills, subtopics, and references/resources.

  flowchart LR;
      A@{shape:processes, label: "Data types"}-->B["`**Working with Numeric and Categorical Data**`"];
      A-->C["`**Computer Vision and Image-based Learning**`"];
      A-->D["`**Time Series Analysis and Forecasting**`"];
      A-->E["`**Natural Language Processing**`"];
      A-->F["`**Speech and Audio Processing**`"];
      click B href "https://github.com/ua-datalab/mlpaths/wiki/Working-with-Numeric-and-Categorical-Data" "Open this in a new tab" _blank
      click C href "https://github.com/ua-datalab/mlpaths/wiki/Computer-Vision-and-Image%E2%80%90based-Learning" "Open this in a new tab" _blank
      click D href "https://github.com/ua-datalab/mlpaths/wiki/Time-Series-Analysis-and-Forecasting" "Open this in a new tab" _blank
      click E href "https://github.com/ua-datalab/mlpaths/wiki/Natural-Language-Processing" "Open this in a new tab" _blank
      click F href "https://github.com/ua-datalab/mlpaths/wiki/Computer-Vision-and-Image%E2%80%90based-Learning" "Open this in a new tab" _blank