Category: Machine Learning
End to End Project Multiple Disease Detection using ML
Naveen
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Project 1: Heart Disease Detection Machine Learning is used across numerous spheres around the world. The healthcare industry is no exception. Machine Learning can play an essential part in predicting presence/ absence of Locomotor diseases, Heart conditions and further. similar information, if predicted well in advance, can give important perceptivity to doctors who can also…
Read MoreK-nearest Neighbor for Machine Learning
Naveen
- 0
Analogy behind KNN: tell me about your friend (who your neighbors are) and I will tell you who you are. Algorithms Common distance function measure used for continuous variables. KNN Working The k-NN working can be explained on the basis of the below algorithm: Step-1: select the number of K of the neighbors Step-2: Calculate…
Read MoreDecision Tree for Machine Learning
Naveen
- 2
Tree based algorithms are a popular family of related non-parametric and supervised methods for both classification and regression. The decision tree looks like a upside-down tree with a decision rule at the root, from which subsequent decision rules spread out below. Sometimes decision trees are also referred to as CART, which is short for classification…
Read MoreEnd to End Project Laptop Price Prediction using Machine Learning
Naveen
- 0
Machine learning relies on AI to predict the future based on past data. If you’re a data science enthusiast or practitioner, this article will help you build your own end-to-end machine learning project from scratch. In order for a machine-learning project to be successful, there are several steps that should be followed. These steps vary…
Read MoreLogistic Regression for Machine Learning
Naveen
- 0
Logistic Regression is one of the most used Machine learning algorithms among industries and academia. It is a supervised learning algorithm used for classification where the target variable should be categorical. Why not Linear Regression for classification There are mainly two reasons for not fitting a linear regression on classification tasks: When we fit a…
Read MoreGradient Descent for Linear Regression
Naveen
- 70
Gradient Descent is defined as one of the most commonly used iterative optimization algorithm of machine learning to train the machine learning and deep learning models. It helps in finding the local minima of a function. The best way to define the local minima or local maxima of a function using gradient descent is as…
Read MoreNetflix Data Analysis Project using Python
Naveen
- 0
Netflix is one of the most popular streaming services in the world, with a massive subscriber base. In this article we’re going to explore how data scientists can use Python to analyze Netflix data from various perspectives: how you watch Netflix and what you do once it finishes. As we have already worked with Jupyter…
Read MoreSpotify Data Analysis Project using Python
Naveen
- 0
Data analysis is an important field in business, research and many other areas. Among the many uses of this data, there are helping to make decisions and publish research papers. The weather can also be predicted based on data analysis too. You’ll learn how to perform exploratory data analysis by analyzing musical-related data sets within…
Read MoreLinear Regression for Machine Learning
Naveen
- 0
Linear regression is the statistical technique to find relationship between two or more variables. To predict the values of response (target) variable based on that values of predictors (external / independent variables) we can use linear regression. Simple linear regression is having only one external factor while Multiple liner regression is having more than one…
Read MoreWhat is GD, Batch GD, SGD, Mini-Batch GD?
Naveen
- 0
What is Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent? Gradient Descent This algorithm is a general algorithm that is used for optimization and for providing the optimal solution for various problems. It takes parameters in an iterative way and makes the cost function as simple as possible. 1) Define a cost…
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