Duration
1 day
What You’ll learn
- Learn how to solve real life problem using the Machine learning techniques
- Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
- Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
- Understanding of basics of statistics and concepts of Machine Learning
- How to do basic statistical operations and run ML models in R
- Indepth knowledge of data collection and data preprocessing for Machine Learning problem
- How to convert business problem into a Machine learning problem
Course Outline
Machine learning basics
- Introduction to Machine Learning in Data Science
- Building a Machine Learning Model
Data Preprocessing for Regression Analysis
- Gathering Business Knowledge
- Data Exploration
- The Data and the Data Dictionary
- Importing the dataset into R
- Univariate Analysis and EDD
- EDD in R
- Outlier Treatment
- Outlier Treatment in R
- Missing Value imputation
- Missing Value imputation in R
- Seasonality in Data
- Bi-variate Analysis and Variable Transformation
- Variable transformation in R
- Non Usable Variables
- Dummy variable creation: Handling qualitative data
- Dummy variable creation in R
- Correlation Matrix and cause-effect relationship
- Correlation Matrix in R
Classification Models: Data Preparation
- The Data and the Data Dictionary
- Importing the dataset into R
- EDD in R
- Outlier Treatment in R
- Missing Value imputation in R
- Variable transformation in R
- Dummy variable creation in R
Machine Learning with R
- Linear Regression
- Logistic Regression
- K Nearest Neighbors
- Decision Trees and Random Forests
- Naive Bayes Classifier
- Support Vector Machines
- K-means Clustering
- Natural Language Processing
- Neural Nets
Programme scope, delivery arrangements and suitability are confirmed during enquiry.