Course Content

1. Python

• Classes & Objects
• Functions
• Error handling
• Datastructure ( List, Dictionary, Set, Tuple)
• List Comprehension, Lambda functions

2. Data Analysis

• Numpy
•  Matplotlib & Seaborn
• Mini Project

3. Statistics

• Linear Algebra
• Descriptive Statistics
• Inferential Statistics
• Probability & Problems

4. Data Science

• Classification Algorithms
• Logistic Regression
• KNN and SVM
• Naïve Bayes
• Decision Tree
• Random Forest
• Xgboost
• Cross validation
• Confusion Matrix
• Normalization
• Unsupervised Learning
•  K Means clustering

7. Artificial Intelligence

• ANN
• CNN
• RNN