Understand Decision Tree Learning Decision tree learning is a supervised learning algorithm, which can be used for classification as well as regression tasks. Moreover, it mimics the human’s decision making process and very effective in several applications. In this post you will know that how to construct a decision tree learning model using Gini Index. […]
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Basically, there are four types of machine learning algorithms. 1- Supervised Learning Algorithms- In supervised learning, values of features and labels or classes are given. Further, Supervised learning algorithms are of two types. 1.1- Regression- In regression numerical values are predicted. 1.2-Classification In classification, labels are predicted. 2-Unsupervised Learning Algorithms In unsupervised learning, instances have
Understand classification Problem in Machine Learning Read More »
Panda’s Series- In the last post, I explained how to create a panda’s series. Further, a pandas series has a lot of you often need to analyze, visualize and clean data. In this post, I will be explaining min(), max(), mean(), median(), and mode functions. min() Function- import pandas as pd lst=[2, 4, 6, 8,
Pandas Series Functions min(), max(), mean(), median() and mode() Read More »
Pandas Series Series is like one dimensional array or a column in a table and can be hold any type data. Furthermore, pandas series can be created in two ways. 1- Create a Pandas Series with Default Indices- In this method, you can create a series by passing list inside Series() method. As a result,
Pandas Series a Python’s Library for Data Handling Read More »
K-Means clustering is an unsupervised machine learning algorithm which partitions n instances into k clusters by similarity. As K-Means clustering is an unsupervised learning algorithm, therefore instances will not have labels. As K-Means clustering is an unsupervised learning algorithm, training instances will not have labels. Furthermore, to make you understand K-Means clustering algorithm, I will
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K-Nearest Neighbors algorithm (KNN) K-Nearest Neighbors algorithm (KNN) is a very important supervised machine learning algorithm and one should start from this algorithm. It is easy to understand compare to other algorithms and does not involve complex mathematical concepts. In this post, I will explain k-Nearest Neighbors algorithm using Irish flowers data set. From
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Keras Deep learning framwork is very popular among researchers and developers which is built at top of Tensorflow. Furthermore, Keras is very handy and easy to use, creating layers and connections is matter of few lines of code. In this post, I am going to explain, how you can train a simple Deep learning network
Train a Simple Deep Learning Network using Keras and Iris DataSet Read More »
Linear regression Linear regression is a supervised learning algorithm and tries to learn a linear function from a given data set D={(xi, yi)} where i=1, 2, 3, 4,———–N where xi is feature vector and yi is target output. In other words, you will try to find out relationship between xi and yi. Suppose you want to
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K-nearest neighbors algorithm is a machine learning algorithm which is used for classification and regression, and it can be applied where other machine learning algorithms are applied. See the typical problem setting , Suppose (x1, y1), (x2, y2), (x3, y3), ……………….(xn, yn) pairs in which x’s are attributes and y’s are labels, suppose another instance
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