Learn Machine Learning

65 articles in Machine Learning

3 min read

Learn Machine Learning

Linear Regression: Predicting Relationships in Data

Understanding Random Forest Classifiers: How They Work

Tuples: Immutable Collections in Machine Learning

Prediction Accuracy: Analyzing Model Performance

The Impact of Salary on Retention Through Crosstab Analysis

Analyzing Titanic Data: Combining Class and Gender for Insights

Splitting Data into Training and Testing Sets for Modeling

Visualizing Data Relationships with Seaborn Pair Plots

Label Encoding, Scaling, and Model Compatibility

Preparing Titanic Dataset: Splitting and Scaling Techniques

Understanding Dataset Structure in Machine Learning

Exploring KNN with the Iris Dataset in Python

Filling Missing Age and Embarked Data for Titanic Analysis

Accessing DataFrame Rows and Columns

Visualizing K-Nearest Neighbors with Simple Data Points

Visualizing Normal Distribution with NumPy

Data Frames: Concatenating Columns for Effective Splitting

K-Nearest Neighbors with Iris Flower Data Visualization

Handwritten Digit Recognition with Neural Networks

Versicolor and Virginica Misclassification in KNN Models

Precision and Recall: Improving Predictive Model Accuracy

Neural Network Data: Patterns in Machine Learning

Implementing User Input for Percentile Calculation in Python

Accuracy Scores in Linear Regression Models

Visualizing and Interpreting Data

Model Confusion: Insights from the Confusion Matrix

Data with Python: Analyzing Min, Max, and Mean Values

Understanding K-Nearest Neighbors in Supervised Learning

Variance: The Core of Statistical Analysis

Exploring Logistic Regression for Employee Retention Prediction

Standard Deviation and the Bell Curve

Model Compilation and Training in Neural Networks

Creating a User Input for Percentile Age Analysis with NumPy

Building a Three-Layer Neural Network with Keras and TensorFlow

Domain Knowledge and Data Analysis in Model Training

Visualizing and Predicting Classes with Scatterplot and KNN

Creating a DataFrame with Iris Dataset

Refining Data: Removing Outliers for Improved Model Training

Data for Readability: Enhancing Index and Column Clarity

Logistic Regression with Data Scaling and Preparation

Exploring the K-Nearest Neighbors Algorithm

Passenger Survival Rates: Insights from the Titanic Data

Training and Testing Linear Regression Models

Data Selection with LOC and ILOC in Pandas

Train-Test Split for Predictive Modeling in Python

Evaluating Model Predictions Against Test Data for Accuracy

Model Accuracy: Understanding Precision and Recall Metrics

Neural Network Training Process for Digit Recognition

One-Hot Encoding for Categorical Data in Machine Learning

Visualizing Survival and Class Distribution in Titanic Data Analysis

Data Processing with LabelEncoder for Categorical Variables

Regression and Data Analysis with Python Libraries

Building a K-Neighbors Classifier

Setting Up Google Colab and Google Drive

Mode: Finding the Most Common Value in Data Analysis

MNIST Dataset: Variations in Handwritten Digits

Visualizing Predictions with KNN on Flower Data

Continuous Classification: Linear Regression Explained

Utilizing Pandas for Data Calculations and Predictions

Analyzing Titanic Survival: Impact of Gender and Embarkation Port

Neural Network Predictions: Accuracy and Fine-Tuning

Predicting Titanic Survival with Random Forest Classifier

Setting Up Jupyter Notebooks in Google Colab with Google Drive

Neural Networks: Image Recognition to Handwritten Digits

Accurate Data Column Order for Predictive Modeling