PROGRAM NAME
Data Science Using Python
DURATION OF PROGRAM
40 HOURS
- Introduction to Python
- Introduction to data science
- Introduction to AI-ML
- Header
- Panda read csv
- datatype and statistics
- Panda column operations
- Panda operations
- Merge and concat
- Tables
- Graphs
- One Dimension
- Two Dimension
- Two Dimension stacking
- Data Dictionary
- Single numeric descriptive analysis
- Double numeric descriptive analysis
- Categorical and all Numeric Descriptive Analysis
- Introduction and Preprocessing
- Feature Selection Regularisation
- Residual Analysis
- Data Read
- Normality test and BoxCox transformation
- Linear Regression structure
- Linear Regression for Numeric features
- HotEncoding and Scaling
- Linear Regression with HotEncoding and Scaling Data
- Generic Treeflow in Prediction
- CatBoost
- CatBoost Hyperparameter Tuning
- Classification Introduction
- Classification: Code and data load
- Classification: Random Forest
- Classification: Random Forest code
- Classification: CatBoost code
- Classification: One class SVM code
- Classification:Logistic Regression
- Classification: Logistic Regression code
- Clustering: Introduction
- Clustering: KMeans
- Clustering: Agglomerative
- Clustering: KNN
- Clustering:KNN using Iris
- Text Analytics: Introduction
- Text Analytics: NLTK Installation
- Text Analytics: Tokenization TextBlob
- Named-entity recognition (NER)-Stemming-Lemmatization
- Word Cloud
