Machine learning
Classical algorithms, model evaluation, feature work and the maths behind them.
Page 10 of 17
- Measuring Feature Importance with Shuffle Technique9 min
Shuffle Feature Importance is a model-agnostic technique that measures feature importance by randomly permuting individual features and observing the impact...
- Non-Uniform Negative Sampling for Imbalanced Data in Python12 min
Non-uniform negative sampling is a technique used to address imbalanced datasets in machine learning. It involves selectively choosing negative examples to...
- Normalizing and Feature Extraction of 3D Point Clouds10 min
Point clouds are 3D representations of objects or environments, consisting of numerous points in space. Normalizing these point clouds and extracting...
- One-Hot Encoding in Python for Machine Learning6 min
One-Hot Encoding is a technique used in machine learning to represent categorical data as binary vectors. It is particularly useful when working with...
- Outlier Detection and Removal in Python12 min
Outliers are data points that significantly deviate from the normal pattern of a dataset. Detecting and handling outliers is crucial for maintaining data...
- Outliers in Machine Learning! Identifying and Handling Anomalies10 min
Outliers are data points that significantly deviate from the majority of observations in a dataset. In machine learning, these anomalous instances can have a...
- Solve imbalanced datasets in Python4 min
Imbalanced datasets occur when the distribution of classes in a dataset is heavily skewed, with one or more classes being significantly underrepresented...
- Sparse Matrices for Machine Learning in Python6 min
Sparse matrices are data structures that efficiently store and operate on matrices with mostly zero elements. They are crucial in machine learning for...
- Splitting Arrays for Machine Learning Model Training12 min
Array splitting is a crucial technique in machine learning, particularly for preparing data for model training. It involves dividing a large dataset into...
- Strategies for Handling Imbalanced Datasets14 min
The fundamental challenge in imbalanced datasets occurs when one class significantly outnumbers others, leading to biased model predictions. This slide...
- Target Encoding for Categorical Variables in Machine Learning6 min
Target encoding represents a sophisticated approach to handling categorical variables by replacing category levels with their corresponding target mean...
- The Importance of Feature Engineering in Data Science13 min
Feature engineering is a crucial process in data science that involves transforming raw data into meaningful features that can significantly improve the...
- The Unsung Importance of Data Cleaning for Machine Learning8 min
Data cleaning is a crucial step in the machine learning pipeline that often goes unnoticed. It's the process of preparing and refining raw data before it's...
- Ultimate Cheatcode to Feature Engineering5 min
Feature engineering is the process of transforming raw data into meaningful features that can improve machine learning model performance. It's a crucial step...
- Ultimate Feature Engineering Techniques for Data Enthusiasts10 min
Feature scaling transforms numerical features to a similar scale, preventing certain features from dominating the learning process due to their larger...
- Understanding Outliers in Machine Learning with Python11 min
Outliers are data points that significantly differ from other observations in a dataset. In machine learning, identifying and handling outliers is crucial...
- Unveiling Feature Selection in Machine Learning with Python11 min
Feature selection is a crucial step in machine learning that involves choosing the most relevant variables for your model. It helps improve model...
- When Feature Scaling Matters in Machine Learning12 min
Feature scaling transforms data into a specific range, typically \[0,1\] or \[-1,1\], to normalize the influence of features with different magnitudes. This...
- 11 Essential Data Normality Tests for Machine Learning6 min
Data normality testing is crucial in many machine learning models and statistical analyses. This slideshow will explore 11 essential methods to test for...
- 15 Most Commonly Used ML Algorithms in Python14 min
Machine Learning (ML) algorithms are computational methods that enable systems to learn from data and improve their performance on specific tasks without...
- 17 Python Interview Questions for Data Science11 min
A dictionary is a mutable, unordered collection of key-value pairs in Python. It provides constant-time complexity for basic operations and serves as the...
- Acing the Machine Learning Interview11 min
Dropout is a powerful regularization technique used in deep learning to combat overfitting. It works by randomly "turning off" a certain percentage of...
- Advanced Machine Learning Models in Python11 min
Advanced Machine Learning Models are sophisticated algorithms that can learn complex patterns from data. These models go beyond traditional methods, offering...
- AI vs Machine Learning Exploring the Differences11 min
Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they have distinct differences. AI is a broader concept that aims...
- Balancing Accuracy and Interpretability in Machine Learning with Python11 min
In machine learning, achieving high accuracy is often the primary goal. However, interpretability is equally important, especially in critical domains like...
- Beginner's Guide to Machine Learning with Python12 min
Understanding fundamental data structures is crucial for efficient machine learning implementation. NumPy arrays and Pandas DataFrames form the backbone of...
- Beginner's Guide to Python for Data Science and Machine Learning8 min
Pandas is a powerful data manipulation library that provides essential tools for data analysis. The first step in any data science project is loading and...
- Building a Machine Learning Model11 min
Advanced data preprocessing techniques including handling missing values, feature scaling, and categorical encoding using pandas and scikit-learn libraries...
- Building a Machine Learning Model The Complete Workflow in Python4 min
Machine learning is a powerful tool for solving complex problems. This slideshow will guide you through the complete workflow of building a machine learning...
- Comparing CSV Files Using Machine Learning in Python7 min
Machine learning techniques can be applied to compare CSV files for similarity. This process involves data preprocessing, feature extraction, and utilizing...