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I'm kinda confused about machine learning is classification in machine learning is algorithm amd is suprivied and unsupervised is algorithms or type of ML? What are some machine learning algorithms?
Didnt understand your question, but some ML algorithms are:
Linear Regression
Logistic Regression
Neural Networks
Support Vector Machines
Desicion Trees
K-Nearest Neighbor
K-Means
Principal Component Analysis
And some more....
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Can someone please give me some suggestions on which feature selection techniques for gene classification should I use?
The major problem to work with gene expression data, with a large number of dimensions and small sample size. Instead of standard feature extraction/selection algorithms, generally, kernel-based feature selection algorithms are applied to gene expression data such as KBMTL(kernelized Bayesian multitask learning), NDR(nonlinear dimensionality reduction) or regularized linear methods such as LASSO and Elastic-net.
You can check these papers to learn more about how to make efficient feature selection on gene expression data.
paper1
paper2
paper3
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From my understanding, artificial intelligence can be divided into two subsets, deep learning and machine learning. Which one of those categories does the minimax algorithm fall into when developing an AI to play chess?
1- AI is much wider than Machine Learning. ML is a subset of Learning, Learning is a subset of AI
2- Deep Learning is a platform for ML( if not a subset of ML), to help do the automatic feature selection at the same time as training.
3- There is not really such a boundary of which algorithm belongs to which part of AI
4- A major part of AI named "Problem Solving" in the AI modern approach book. MinMax has been discussed there if I am not mistaken
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I have just completed Machine learning course from Andrew ng and would like to proceed further.
I also want the python implementation of Machine Learning from beginning so that i can practice on Kaggle.
Also, is there any better book or tutorial or some resource like that so that i can proceed further without wasting any time searching such resources.
The best book unequivocally that has implementation of Machine Learning algorithms in Python is the "Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas C. Müller. Machine Learning algorithms in Python can be used from a package called scikit-learn. This package has everything you need for Machine Learning. All the algorithms, scaling, cross validation. And that book is written by the chief developer of scikit-learn itself.
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I want to train an output vector(which is from deep learning model) like fixed vector. Hence, I chose a cosine similarity between two vectors as the objective function. However, I don't know if that is a correct approach for my need.
No. The cosine similarity is a measure of how similar two items (samples in your dataset) are.
In contrast, the objective function when training a neural network should be a definition of the current estimation error over the data - so they are different things.
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My product is made in Python and I need Semantic Analysis for classification of sentences into questions, complaints, etc. Which is the best framework for the same?
I think the best approach would be, try several methods, cross-validated each method using a separate validation data set (or using K-Fold cross-validation) and pick the best one.
So as a starting point you can try:
Simple Logistic Regression using scikit-learn
Random Forest or Gradient Boosting Tree
Recurrent Neural Networks using Keras library.