# Linear Regression in Python with Scikit-Learn

There are two types of supervised machine learning algorithms: Regression and classification. The former predicts continuous value outputs while the latter predicts discrete outputs. For instance, predicting the price of a house in dollars is a regression problem whereas predicting whether a tumor is malignant or benign is a classification problem. In this article we will briefly study what linear regression is and how it can be implemented using the Python Scikit-Learn library, which is one of the most popular […]

# K-Nearest Neighbors Algorithm in Python and Scikit-Learn

The K-nearest neighbors (KNN) algorithm is a type of supervised machine learning algorithms. KNN is extremely easy to implement in its most basic form, and yet performs quite complex classification tasks. It is a lazy learning algorithm since it doesn’t have a specialized training phase. Rather, it uses all of the data for training while classifying a new data point or instance. KNN is a non-parametric learning algorithm, which means that it doesn’t assume anything about the underlying data. This […]

# Decision Trees in Python with Scikit-Learn

Introduction A decision tree is one of most frequently and widely used supervised machine learning algorithms that can perform both regression and classification tasks. The intuition behind the decision tree algorithm is simple, yet also very powerful. For each attribute in the dataset, the decision tree algorithm forms a node, where the most important attribute is placed at the root node. For evaluation we start at the root node and work our way down the tree by following the corresponding […]

# Implementing SVM and Kernel SVM with Python’s Scikit-Learn

A support vector machine (SVM) is a type of supervised machine learning classification algorithm. SVMs were introduced initially in 1960s and were later refined in 1990s. However, it is only now that they are becoming extremely popular, owing to their ability to achieve brilliant results. SVMs are implemented in a unique way when compared to other machine learning algorithms. In this article we’ll see what support vector machines algorithms are, the brief theory behind support vector machine and their implementation […]

# Implementing PCA in Python with Scikit-Learn

With the availability of high performance CPUs and GPUs, it is pretty much possible to solve every regression, classification, clustering and other related problems using machine learning and deep learning models. However, there are still various factors that cause performance bottlenecks while developing such models. Large number of features in the dataset is one of the factors that affect both the training time as well as accuracy of machine learning models. You have different options to deal with huge number […]

# Implementing LDA in Python with Scikit-Learn

In our previous article Implementing PCA in Python with Scikit-Learn, we studied how we can reduce dimensionality of the feature set using PCA. In this article we will study another very important dimensionality reduction technique: linear discriminant analysis (or LDA). But first let’s briefly discuss how PCA and LDA differ from each other. PCA vs LDA: What’s the Difference? Both PCA and LDA are linear transformation techniques. However, PCA is an unsupervised while LDA is a supervised dimensionality reduction technique. […]

# Random Forest Algorithm with Python and Scikit-Learn

Random forest is a type of supervised machine learning algorithm based on ensemble learning. Ensemble learning is a type of learning where you join different types of algorithms or same algorithm multiple times to form a more powerful prediction model. The random forest algorithm combines multiple algorithm of the same type i.e. multiple decision trees, resulting in a forest of trees, hence the name “Random Forest”. The random forest algorithm can be used for both regression and classification tasks. How […]

# The Naive Bayes Algorithm in Python with Scikit-Learn

When studying Probability & Statistics, one of the first and most important theorems students learn is the Bayes’ Theorem. This theorem is the foundation of deductive reasoning, which focuses on determining the probability of an event occurring based on prior knowledge of conditions that might be related to the event. The Naive Bayes Classifier brings the power of this theorem to Machine Learning, building a very simple yet powerful classifier. In this article, we will see an overview on how […]