Anyaoha/MACHINE-LEARNING-TUTORIAL

Machine Learning Tutorial

Hi guys 😄

Machine learning is a subfield of artificial intelligence that involves training algorithms to make predictions or decisions based on data. Machine learning algorithms are designed to automatically improve their performance as they are exposed to more data.

This repo contains the jupyter notebooks and datasets I used on the Tirendaz Akademi Youtube channel. In these tutorials, I used Pandas for data preprocessing, Numpy for multidimensional arrays and matrix operations, Matplotlib and Seaborn for data visualization, Scikit-Learn to implement machine learning algorithms.

I hope this helps! Let me know if you have any questions. If you enjoy these notebooks and videos, don't forget to give a star ✨.

Let's Connect:

Happy learning 🎉

YouTube Videos

• HOW TO CREATE A MACHINE LEARNING MODEL

• SCIKIT-LEARN TUTORIAL-1

• SCIKIT-LEARN TUTORIAL-2

• FEATURE ENGINEERING

• K-NEAREST NEIGHBOR (KNN)

• LINEAR REGRESSION

• LINEAR REGRESSION in ACTION

• RIDGE AND LASSO REGRESSION

• LINEAR MODELS FOR CLASSIFICATION

• LOGISTIC REGRESSION in ACTION

• NAIVE BAYES CLASSIFICATION

• SUPPORT VECTOR MACHINES

• DECISION TREES

• ENSEMBLE LEARNING

• ARTIFICIAL NEURAL NETWORK

• DATA SCALING

• PRINCIPAL COMPONENT ANALYSIS

• MANIFOLD LEARNING

• K-MEANS CLUSTERING

• AGGLOMERATIVE-HIERARCHICA -DBSCAN CLUSTERING

• GAUSSIAN MIXTURE MODELS

• MODEL EVALUATION

• MODEL IMPROVEMENT

• EVALUATION METRICS

• PIPELINES

• TEXT ANALYSIS

• FEATURE ENGINEERING

• FEATURE SELECTION

• INTRODUCTION MACHINE LEARNING

Happy learning ... ✌️ ...