Objectives

• ● - Understanding the basic concepts of machine learning, its techniques, and applications in various fields.● - Acquiring skills in basic statistical analysis, exploratory data analysis, clustering, productivity, and advanced data analysis techniques.● - Acquiring skills in applying machine learning techniques such as classification, clustering, regression, artificial neural networks, and their applications in various fields.● - Familiarizing oneself with cross-validation techniques, evaluation, advanced data analysis, unsupervised machine learning, and deep learning applications in various fields.● - Training on the use of popular frameworks and tools available for data analysis and machine learning techniques.● - Understanding the ethical issues related to machine learning and being able to think critically about its applications in society.

Outcomes

● Understanding the basic concepts of machine learning, its techniques, and applications in various fields.● Acquiring skills in basic statistical analysis, exploratory data analysis, clustering, productivity, and advanced data analysis techniques.● Acquiring skills in applying machine learning techniques such as classification, clustering, regression, artificial neural networks, and their applications in various fields.● Familiarizing oneself with cross-validation techniques, evaluation, advanced data analysis, unsupervised machine learning, and deep learning applications in various fields.● Training on the use of popular frameworks and tools available for data analysis and machine learning techniques.● Understanding the ethical issues related to machine learning and being able to think critically about its applications in society.● The ability to apply the concepts and skills acquired in solving practical problems that require data analysis and machine learning techni

Course Contents

Day 1 : ● Statistics Foundation and EDA ● a. Descriptive Statistics ● b. Data Preprocessing ● c. Exploratory Data Analysis ● d. Over Sampling Day 2 : ● Regression● a. Conventional Foundation i. Linear Regression ii. Polynomial Regression iii. Bias and Variance Trade Off iv. Lasso and Ridge v. Decision Tree Regression vi. Support Vector Machine Regression vii. K Nearest Neighbour Regression Day 3 : ● b. Ensemble Learning i. Random Forest Regression ii. XGBoost Regression iii. CatBoost Regression ● c. Transformers Approach i. TabNet Regression Day 4 : ● Classification ● a. Conventional Foundation i. Logistic Regression ii. Decision Tree Classifieriii. Support Vector Machine Classifier iv. K Nearest Neighbour Classifier ● b. Ensemble Learning i. Random Forest Classifier ii. XGBoost Classifier iii. CatBoost Classifier ● c. Transformers Approach i. TabNet Classifier Day 5 : ● Ensemble Learning● a. Bagging i. Random Forest ● b. Boosting i. XG Bo

Description

Data will be available soon!