Here I covered both Supervised and Unsupervised Learning Techniques with Practical Implementation. Everything from scratch and I solved a lot of different problems with different Machine Learning techniques . The Topic that are covered in this repository are:

Supervised Machine Learning

     i. Classfication
      
                 a) Naive Bayes Classifier(NB)
                 
                 b) Logisttic Regression
                
                 c) k-Nearest Neighbors(kNN)
                 
                 d) Decision Tree Classifier
                 
                 e) Random Forrest Classifier
                 
                 f) L2 Regularised Logistic Regression

      ii. Regression
      
                 a) Liner regression
                 
                 b) Decision Tree Regression
                 
                 c) Random Forrest Regression
                 
                 d) Ridge Regression
                 
                 e) Lasso Regression

Unsupervised Machine Learning

       i. Association Rule
       
                 a) Apriori Algorithm
                 
                 b) FP Growth Algorithm
                 
       ii. Clustering
       
                 a) k-Mean Clustering
                 
                 b) Hierarchical Clustering

Dimensionality Reduction

       i. Principal Component Analysis(PCA)
       
       ii. Linear Discriminant Analysis (LDA)
       
       iii. Quadratic Discriminat Analysis (QDA)