Python learning plan for AI learning Progress management

Background and purpose

Because learning Python by Chainer seems to be a paragraph I will review the plan

Conclusion

Learn the specialized books introduced below and output applications using machine learning.
I will do my best! !! !!

Contents

** Learned lightly for the time being **

Next study schedule (basically, it seems that you can touch it with a specialized book)

Study with this book image.png

Multiple regression analysis, ridge regression, lasso regression, logistic regression, k-nearest neighbor method, support vector machine, decision tree, random forest, typical algorithm of unsupervised learning, k-means clustering, principal component analysis, typical hyperparameters Adjustment method, grid search, random search, Bayesian optimization, typical evaluation index of classification, correct answer rate, precision rate, recall rate, F value </ b>

Further next step

The following seems to be good if you study appropriately according to the program you want to make, so the order of learning is likely to be in the second half instead of now

Image data, convolutional neural network (CNN), object detection algorithm (R-CNN, YOLO, SSD, etc.), semantic segmentation algorithm, text data, text data feature extraction method (Bag of words, Word2Vec, etc.) , Machine translation algorithms (Seq2Seq, Attention, etc.)
Time-series data (1/1 number of visitors is data that has a context in the data of 100 people) Recurrent neural networks (RNN, LSTM, GRU, etc.) Convolutional Neural Network (CNN)
Table data (data as described in an Excel sheet) Feature engineering Evolving machine learning algorithms (XGBoost, LightGBM, etc.) </ B>

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