Learn Python for DataScience

A handful of jupyter notebooks exploring some python & ML topics. numpy, matplotlib, sklearn, pylab, scipy, pandas, statsmodels, tensorflow...

An Intro To Tensorflow

Exploratory Data Analysis Examples

Learn how to perform exploratory data analysis (EDA) using Python by analyzing honey production data. Discover trends, visualize insights with Seaborn, and explore data manipulation techniques using Pandas.

Specific Data-Analysis Features

Learn the fundamentals of mean, median, and mode in statistics. This guide covers their calculations, differences, and practical applications in data analysis.

Understand standard deviation and variance, two essential concepts in statistics for measuring data dispersion and distribution in datasets.

Explore different types of data distributions, including normal, skewed, and uniform distributions, with visualizations and real-world examples.

Learn about percentiles, quartiles, and their role in summarizing and analyzing datasets, including how to calculate them using Python.

Understand moments in statistics, including mean, variance, skewness, and kurtosis, and their significance in data analysis.

Learn methods for detecting and removing outliers from datasets, including the IQR method, Z-score filtering, and practical Python examples.

Discover the relationship between covariance and correlation and how they help analyze linear relationships between variables in statistics.

Understand conditional probability, Bayes' theorem, and their applications in real-world decision-making and probability modeling.

Learn linear regression, a fundamental machine learning technique for predictive modeling, with step-by-step Python examples using scikit-learn.

Explore polynomial regression, an extension of linear regression used for curve fitting, and understand how to implement it in Python.

Learn about multiple regression, a statistical technique for modeling relationships between multiple independent variables and a dependent variable.

Understand the train-test split method in machine learning for model evaluation and how cross-validation improves model performance.

Explore the Naive Bayes algorithm, a probabilistic classification technique widely used in spam filtering, text classification, and sentiment analysis.

Learn K-Means clustering, an unsupervised learning algorithm used for grouping similar data points in machine learning applications.

Discover XGBoost, a powerful gradient boosting algorithm, and how ensemble learning improves machine learning model performance.

Learn about Support Vector Machines (SVMs), a powerful classification algorithm in machine learning, including kernel methods and implementation in Python.

Build a movie recommendation system using similarity measures in Python, exploring collaborative filtering techniques.

Enhance movie recommendations using content-based filtering by analyzing genre similarity in Python.

Use the K-Nearest Neighbors (KNN) algorithm to build a movie recommendation system and find similar movies based on user preferences.

Learn about Principal Component Analysis (PCA), a technique for reducing high-dimensional data while preserving essential features.

Explore reinforcement learning, including Q-learning and policy optimization techniques, and how they are used in AI decision-making.

Understand K-Fold Cross-Validation, a model evaluation technique that helps balance bias and variance for better predictive performance.