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How to ace machine learning hyperparameter optimization

Model optimization techniques play a very crucial role in the field of machine learning. These optimization techniques are also used for hyperparameter tuning, leading to better-performing machine learning models for a given dataset. This article shows different ways of hyperparameter tuning of machine learning models in Python.

The previous article was about finding the best-performing machine learning algorithm for the given dataset.

How to find the best performing Machine Learning algorithm

This article shows quick ways of comparing multiple machine learning algorithms for classification or regression.

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These techniques are often the first step after exploratory data analysis to cross-check if the input features in a given dataset have enough prediction power or not. Also, it is an efficient way to explore…

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How to ace Data Visualization

Data visualization techniques are a quick way to identify patterns and understand the complex dataset. As a result, it is widely used in several industries to present the data to stakeholders. This article shows various data visualization techniques which can be helpful for your next data science project.

The human brain responds well and retains more information from simple diagrams or visual content than text or numbers. Therefore, representing a complex dataset in graphical format is an effective way to drive crucial insights and gain more information about the data. Furthermore, the popularity of data visualization techniques can…

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How to deal with an imbalanced dataset

Models trained on imbalanced datasets tend to perform poorly on minority classes because most machine learning algorithms for classification assume the classes are balanced. Not treating the imbalanced datasets correctly and not using correct metrics for model evaluation can cause severe problems if business decisions rely on the model’s outcome. This article shows few tricks when working with such datasets.

Classification algorithms are machine learning techniques that involve categorizing data into classes. It is one of the kinds of supervised machine learning, in which algorithms learn from labeled data. Since algorithms learn from the labeled data, hence the distribution of classes plays an important role. For example, training algorithms on…

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How to find the best performing Machine Learning algorithm

This article shows quick ways of comparing multiple machine learning algorithms for classification or regression.

Imagine a situation where you want to test if the given dataset has sufficient features to train machine learning algorithms or to test different algorithms’ performance on the given dataset. Both cases are pretty common in the field of data science.

Usually, to test the features, one can train models…

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How to ace Exploratory Data Analysis

Exploratory Data Analysis (EDA) is the primary building block of any data-centric project. This article focuses on graphical and numerical ways of performing EDA using Python libraries such as Pandas, Seaborn, Tensorflow data validator, and Lux.

Data Scientists widely use EDA to understand datasets for decision-making and data cleaning processes. EDA reveals crucial information about the data, such as hidden patterns, outliers, variance, covariance, correlations between features. The information is essential for the hypothesis’s design and creating better-performing models.

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End-to-End Machine Learning series: Exploratory Data Analysis

Follow this series to learn about end-to-end ML project building, starting from data analysis to model serving into the cloud. The series shall serve as a pathway to a full-stack data scientist role in which readers will get to know some essential topics such as docker, flask, Heroku, CI/CD pipeline, unit testing, and many others.

A successful Machine Learning (ML) project involves several steps such as gathering data, data preparation, data exploration, feature engineering, model building, and serving out predictions to the end-users. …

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How to make neural network dream

This article shows how to create fantastic art using artificial neural networks.

The convolution neural network may contain several stacked layers, images fed as an input to neural network travel through subsequent layers, and the final decision made by the output layer. But, there exist several questions, such as

How…

PYTHON DATA ANALYSIS LIBRARY: PANDAS

Tips & tricks to import data into Pandas from various data sources

Pandas is a Python Data Analysis Library that has cemented its place in the Data Science world. Articles on the internet about top Python libraries for Data Science include Pandas as one of its favorites. Pandas library offers several functions that can speed up data wrangling and exploratory data analysis…

MACHINE LEARNING MODEL DEPLOYMENT

A simple guide to deploy machine learning model on Heroku

Machine Learning (ML) based applications play a crucial role in scaling, automating, and optimizing processes in this era of digitization. The ML model development lifecycle comprises data cleaning, exploratory data analysis, model development, model training, and model serving. There exist many articles on model development and training but not much on model serving. This article shows simple steps to deploy a trained ML model on Heroku.

Investing a considerable amount of time optimizing the ML model is one of the most common misconceptions and pitfalls for an unsuccessful ML project. Instead, teams with successful ML project invests time in gathering data, building efficient data pipelines to avoid training-serving skew, and building reliable model serving infrastructure. …

A/B Testing

Take right decisions and grow the business using A/B testing

Companies run experiments to understand the demand and the likely changes for their businesses to generate more revenue. However, it is not an easy task. Even changing the color of a button on the website is not random but calculated. This article shows few tricks widely used by Data Analysts and Data Scientists to build strategies for growing businesses efficiently.

Studies conducted by big companies have shown that even changing a minor feature such as the response time by few milliseconds, the color of a button, welcome image, fonts, and many more can significantly affect website traffic. A relatable example could be posting a picture on social media. Why specific…

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How to find the best performing Machine Learning algorithm

This article shows quick ways of comparing multiple machine learning algorithms for classification or regression.

Start guide

START GUIDE

START GUIDE

This article shows quick ways of comparing multiple machine learning algorithms for classification or regression.

START GUIDE

Exploratory Data Analysis (EDA) is the primary building block of any data-centric project. This article focuses on graphical and numerical ways of performing EDA using Python libraries such as Pandas, Seaborn, Tensorflow data validator, and Lux.

START GUIDE

START GUIDE

PYTHON DATA ANALYSIS LIBRARY: PANDAS

MACHINE LEARNING MODEL DEPLOYMENT

A/B Testing

Rahul Pandey