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This one trains the DNN on iris and also create confusion matrix. Comments will help you know what is happening. === #imports from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np import pandas as pd from sklearn.model_selection import train_test_split # Load data # Download it from here : https://archive.ics.uci.edu/ml/datasets/iris. Ensure that the categories are #labeled as Iris-setosa, Iris-virginica, Iris-versicolor and their column name as Species. iris = pd.read_csv("irisdata.csv") # explore what is loaded #print(iris.shape) #print(iris.head) # Convert the data type of columns to float32 type #print(iris.dtypes) #print(iris.iloc[:,0:4]) iris.iloc[:,0:4] = iris.iloc[:,0:4].astype(np.float32) #print(iris.dtypes) # encode the classes to numeric values iris["Species"] = iris["Species"].map({"Iris-setosa":0,"...

Some interesting and must read starting points for Artificial Intelligence. http://www.aaai.org/home.html http://aitopics.net/NewsFinderInfo And specifically Natural Language Processing (NLP) :  http://aitopics.net/NaturalLanguage Machine Learning:  http://aitopics.net/MachineLearning

Machine Learning Machine Learning (ML) is a subfield of artificial intelligence (AI) that is concerned with the design and development of algorithms and techniques that allow computers to "learn". ML is one of the active areas of research in computer science and currently has a large repository of practically useable techniques and algorithms for a wide range of tasks. Typical ML problems include automatic clustering of a set of items, automatic classification (spam mail, documents, etc.), automatic learning/refinement of rules for a diagnostic system, predictive modelling, etc. ML methods have evolved from various domains such as Statistics, Information theory, Biology and Control theory. ML has a wide spectrum of applications including natural language processing (NLP), pattern recognition, search engines, medical diagnosis, bioinformatics and chemical informatics, fraud detection, stock market analysis, speech and handwriting recognition, robotics, intelligent computer gam...

If you are interested in developing applications using machine learning algorithms but you are tired of coding the Learning algorithms then here is All-in-one formula for you -WEKA.Weka is collection of machine learning algorithms. It does everything for you, you are responsible for the just data and feature extraction, keep everything else for the Weka. It really simplifies your work and saves time.It also does evaluation of the built model on the same or provided data as per your settings. You can download it form here . Here you will get documentation and all the needed information regarding using the weka. Weka provides number of ML algorithms like ANN, SMO, Decision Trees etc. So experience the Difference!