This repository provides a comprehensive overview of various machine learning concepts and their practical applications. We will explore the machine learning pipeline and gain insights into supervised learning, regression models, and classification algorithms,unsupervised learning, clustering techniques, and ensemble modeling and also evaluate popular machine learning frameworks such as TensorFlow and Keras.
1.Handling inappropriate data
2.Outlier Detection and Removal
3.Data Processing phase
4.Feature Scaling
5.Linear Regression
6.Model Implementor
7.Feature Engineering
8.Multicollinearity
9.Hypothesis Test
10.Multiple linear Regression
11.Classification
12.Evaluation metrics for Classification
13.Visualization-Based for Classification metrics
14.Multiclass_Classification
15.Ensemble_learning
16.Decision_Tree
17.RandomForest
18.Boosting
19.Cross_Validation
20.Hyperparameter_Tuning
21.PCA(Feature Reduction)
22.KMeans(Clustering)
23.Recommendation_System