Course Content

1.Python

2.Pandas

3.Numpy

4.Matplotlib & Seaborn

• Mini Project

5.Machine Learning Introduction

• Regression
• Mini Project

6.Classification Algorithms

• Logistic Regression
• KNN and SVM
• Naïve Bayes
• Decision Tree
• Random Forest
• Xgboost
• Cross validation
• Consfusion Matrix
• Normalization
• Unsupervised Learning
•  KMeans clustering

7.Artificial Intelligence

• Perceptron
• ANN
• CNN
• RNN
• Mini Project