Machine Learning Courses

Notes & Notebooks of Machine Learning courses I've followed.

📗 Courses

• [Coursera] AI For Medicine Specialization - Set of courses aimed at applying machine learning to concrete problems in medicine. Offered by deeplearning.ai.

  1. AI For Medical Diagnosis - How can AI be applied to medical imaging to diagnose diseases ?
  2. AI For Medical Prognosis - AI approaches to prognostic tasks.
  3. AI For Medical Treatment - Using AI to estimate treatment effects and extract information from medical reports.

• [Coursera] Data Science in Stratified Healthcare and Precision Medicine - Machine learning in healthcare.

  • Summary : This course aims at understanding the vast variety of applications of Data Science in the medical field. It also helps us understand the different challenges of these applications through multiple interviews of experts in the field.
  • Topics : Machine learning, Image visualization (analysis of DICOM images), NLP and Graph Data (RDF).

• [Coursera] Generative Adversarial Networks (GANs) Specialization (ongoing) - Introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Offered by deeplearning.ai.

  1. Build Basic Generative Adversarial Networks (GANs) - Understand, explore and implement multiple GAN architectures.
  2. Build Better Generative Adversarial Networks (GANs) - GAN Evaluation, Disadvantages and Improvements.
  3. Apply Generative Adversarial Networks (GANs) - Explore and implement the applications of GANs.

• [Coursera] How To Win A Data Science Competition - Learn to analyze and solve competitively predictive modelling tasks.

  • Summary : This course aims at understanding how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.
  • Topics : Feature Preprocessing & Generation, EDA, Validation Strategies, Data Leakage, Metrics & Hyperparameter Optimization, Ensembling.

• [ESILV] Engineering School - Courses touching different aspects of machine learning.

  • Advanced Machine Learning for NLP - Sentiment analysis, Text Processing and Deep Learning architectures (LSTM, CNN, RNN ...).
  • Python for Data Analysis - This course makes us go through a variety of Python applications.
    • Topics : Web Scrapping (BeautifulSoup, Selenium), Data analysis (pandas, seaborn ...), Machine learning (scitkit-learn, keras), Deep Learning (LSTM, CNN, RNN), Django API.

• [Coursera] Deep Learning Specialization (ongoing) - Set of courses on Deep learning offered by deeplearning.ai.

  1. Neural Networks & Deep Learning - Introducing the fundations of deep learning.
  2. Improving Deep Neural Networks - Hyperparameter tuning, Regularization and Optimisation.
  3. Structuring Machine Learning Projects - How to build a successfull machine learning project.
  4. Convolutional Neural Networks - Building & Applications.
  5. Sequence Models - Building & Applications.

• introtodeeplearning - MIT's introductory course on deep learning methods and applications.

  • Topics : Deep Learning architectures (CNN, RNN, LSTM ...) , Text Generation, Handwritten Digit Classification.

• [Coursera] Deep Learning with PyTorch - Develop deep learning models using Pytorch. Offered by IBM.

  • Topics : Linear/Logistic Regression, Deep Neural Networks, Dropout, Batch Normalization, GD with momentum, CNNs.

• mlcourse.ai - Global machine learning course.