Statistics, Machine Learning, Deep Learning
Total Training hours – 40 hrs
Day – 1
- Introduction to Statistics (4hrs)
- Descriptive Statistics
- Inferential Statistics
Day – 2
- Introduction to Data Analytics using R (2hrs)
- Syntax and semantics needed in R with respect to Data Analytics
- Data Cleaning and Transformation in R
- Introduction to Machine Learning (2hrs)
- Understanding of Data
- Analytics project Life cycle with steps to be followed for model building
Day – 3
- Supervised Learning
- Regression o Linear Regression – theory and implementation (4hrs)
Day – 4
- Logistic Regression – theory and implementation (4hrs)
Day – 5
- Classification o Decision Trees – theory and implementation(2hrs)
- K Nearest Neighbour – theory and implementation (2hrs)
Day – 6
- Understanding of Ensemble Techniques (Bagging, Boosting, Stacking) (Random Forest, XGBoost – theory and implementation) (4hrs)
Day – 7
- Support Vector Machines – theory and implementation (4hrs)
Day – 8
- Unsupervised Learning(4hrs)
- Clustering Technique
- Hierarchical Clustering – theory and implementation
- K-Means – theory and implementation
Day – 9
- Deep Learning (4hrs)
- Introduction to Tensor flow
- Perceptron
Day – 10 ( Deep learning 4hrs )
- Activation Functions
- Neural Networks
