Difference between revisions of "Category:Machine Learning"

Revision as of 23:52, 30 March 2017

Machine learning is the sub-field of computer science that gives computers the ability to learn without being explicitly programmed.

Courses

Udemy

• (The Numpy Stack in Python) — Free
• Linear Regression in Python — $30 ($120)
• Logistic Regression in Python — $30 ($120)
• (Supervised Machine Learning in Python) — $30 ($120)
• (Bayesian Machine Learning in Python: A/B Testing) — $30 ($120)
• Deep Learning in Python — $30 ($120)
• Practical Deep Learning in Theano and TensorFlow — $30 ($120)
• (Supervised Machine Learning in Python 2: Ensemble Methods) — $30 ($120)
• Convolutional Neural Networks in Python — $30 ($120)
• (Easy NLP) — $30 ($120)
• (Cluster Analysis and Unsupervised Machine Learning) — $30 ($120)
• Unsupervised Deep Learning — $30 ($120)
• (Hidden Markov Models) — $30 ($120)
• Recurrent Neural Networks in Python — $30 ($120)
• Artificial Intelligence: Reinforcement Learning in Python — $45 ($180)
• Natural Language Processing with Deep Learning in Python — $30 ($120)
• Machine Learning A-Z™: Hands-On Python & R In Data Science — $200; 37.5 hours

Deep Learning

What Deep Learning is good at

• Examples of common Deep Learning algorithms
  • Analysis
  • Prediction
  • Recommendation
  • Dectection
  • Classification
  • Segmentation
  • Natural Language Processing (NLP)
• Use cases
  • Image/video classification
  • Speech recognition
  • NLP
  • Cancer cell detection
  • Drug discovery
  • Video captioning
  • Content-based search
  • Real-time translation
  • Face recognition
  • Video surveillance
  • Cyber security
  • Pedestrian detection
  • Lane tracking
  • Recognize traffic signs

NumPy / Pandas / matplotlib / SciPy

pip install -U numpy scipy matplotlib pandas ipython

• See:
  • NumPy
  • SciPy
  • matplotlib
  • pandas
  • scikit-learn
  • scikit-image