Time to learn Machine Learning

Artificial intelligence

Time to learn Machine Learning

 

Its always been on my bucket list to do more Machine Learning; now its time to dive in, this is just to document my journey; I want to build something, either a back testing platform for Cyrpto or a real-time data visualisation or possibly Google – GRR with Opensource Threat Intel and visuals.

Going to start with these to free sources;

  1. AWS Free Machine Learning Course – https://aws.amazon.com/training/learning-paths/machine-learning/
  2. Crypto Data Analysis – https://medium.com/@eliquinox/cryptocurrency-data-analysis-part-i-obtaining-and-playing-with-data-of-digital-assets-2a963a72703b
  3. IBM – https://www.ibm.com/support/knowledgecenter/DSXDOC/analyze-data/ml-mnist-tutorials.html
  4. GB8ZMQZ3
  5. Datacamp – https://www.datacamp.com
  6. Trading – https://quantra.quantinsti.com/course/sentiment-analysis-in-trading

Machine Learning / AI / Data Analytics

 

SDKs

Data Visualisation

Machine Learning Guide

https://www2.emeritus.org/programs/applied-machine-learning-fb?utm_source=Fb&utm_medium=ad1&utm_content=online&utm_campaign=B-5421_US_FB_AML_MAR_18_Interest

  • Module 1: Regression
    Maximum Likelihood, Least Squares, Regularization
  • Module 2: Bayesian Methods
    Bayes Rule, MAP Inference, Active Learning
  • Module 3: Foundational Classification Algorithms
    Nearest Neighbors, Perceptron, Logistic Regression
  • Module 4: Refinements to Classification
    Kernel Methods, Gaussian Process
  • Module 5: Intermediate Classification Algorithms
    SVM, Trees, Forests and Boosting
  • Module 6: Clustering Methods
    K-Means Clustering, E-M, Gaussian Mixtures
  • Module 7: Recommendation Systems
    Collaborative Filtering, Topic Modeling, PCA
  • Module 8: Sequential Data Models
    Markov and Hidden Markov Models, Kalman Filters
  • Module 9: Association Analysis
  • Module 10: Model Selection
    Model Comparisons, Analysis Considerations

Tutorials

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