Articles About Machine Learning

Building 3 Fun AI Applications with ControlFlow

Building 3 Fun AI Applications with ControlFlowImage by Author | Canva Pro The AI industry is rapidly advancing towards creating solutions using large language models (LLMs) and maximizing the potential of AI models. Companies are seeking tools that seamlessly integrate AI into existing codebases without the hefty costs associated with hiring professionals and acquiring resources. This is where Controlflow comes into play. With ControlFlow, you can develop complex AI applications using just a few lines of code. In this tutorial, […]

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Branching Out: Exploring Tree-Based Models for Regression

Our discussion so far has been anchored around the family of linear models. Each approach, from simple linear regression to penalized techniques like Lasso and Ridge, has offered invaluable insights into predicting continuous outcomes based on linear relationships. As we begin our exploration of tree-based models, it’s important to reiterate that our focus remains on regression. While tree-based models are versatile, how they handle, evaluate, and optimize outcomes differs significantly between classification and regression tasks. Tree-based regression models are powerful […]

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Decision Trees and Ordinal Encoding: A Practical Guide

Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and how it can be leveraged when implementing a Decision Tree Regressor. Through practical Python examples using the OrdinalEncoder from sklearn and the Ames Housing dataset, […]

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From Single Trees to Forests: Enhancing Real Estate Predictions with Ensembles

# Import necessary libraries for preprocessing import pandas as pd from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, FunctionTransformer from sklearn.compose import ColumnTransformer   # Load the dataset Ames = pd.read_csv(‘Ames.csv’)   # Convert the below numeric features to categorical features Ames[‘MSSubClass’] = Ames[‘MSSubClass’].astype(‘object’) Ames[‘YrSold’] = Ames[‘YrSold’].astype(

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5 Free Courses to Master Deep Learning in 2024

5 Free Courses to Master Deep Learning in 2024Image by Author AI applications are everywhere. I use ChatGPT on a daily basis — to help me with work tasks, and planning, and even as an accountability partner. Generative AI hasn’t just transformed the way we work. It helps businesses streamline operations, cut costs, and improve efficiency. As companies rush to implement generative AI solutions, there has been an unprecedented surge in the demand for AI skills. There hasn’t been a better time to […]

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The Concise Guide to Feature Engineering for Better Model Performance

The Complete Guide to Feature Engineering for Better Model Performance Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your data. They help the model understand and make predictions. Feature engineering improves these features to make them more useful. Modeling […]

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5 Real-World Machine Learning Projects You Can Build This Weekend

5 Real-World Machine Learning Projects You Can Build This WeekendImage by Author | Created on Canva Building machine learning projects using real-world datasets is an effective way to apply what you’ve learned. Working with real-world datasets will help you learn a great deal about cleaning and analyzing messy data, handling class imbalance, and much more. But to build truly helpful machine learning models, it’s also important to go beyond training and evaluating models and build APIs and dashboards as needed. […]

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From Data to Insights: A Beginner’s Journey in Exploratory Data Analysis

From Data to Insights: A Beginner’s Journey in Exploratory Data AnalysisImage by Editor | Ideogram Every industry uses data to make smarter decisions. But raw data can be messy and hard to understand. EDA allows you to explore and understand your data better. In this article, we’ll walk you through the basics of EDA with simple steps and examples to make it easy to follow. What is Exploratory Data Analysis? Exploratory Data Analysis (EDA) is the process of examining your […]

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Comparing Scikit-Learn and TensorFlow for Machine Learning

Comparing Scikit-Learn and TensorFlow for Machine LearningImage by Editor | Ideogram Choosing a machine learning (ML) library to learn and utilize is essential during the journey of mastering this enthralling discipline of AI. Understanding the strengths and limitations of popular libraries like Scikit-learn and TensorFlow is essential to choose the one that adapts to your needs. This article discusses and compares these two popular Python libraries for ML under eight criteria. Scope of Models and Techniques Let’s start by highlighting […]

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Filling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning

In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values. Missing data can significantly compromise the accuracy and reliability of models if not appropriately managed. This post explores various imputation strategies to address missing data and embed them into our […]

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