INTRODUCTION – The Different Types of Machine Learning Understanding in Machine Learning: Here is a broader vision that one goes through initially with participant in getting through essential concepts to know its weightage into the broad field of data science. The initial part of the course would explore basic principles into the backbone of machine … Read more
INTRODUCTION – Probability The basic principles of probability will be learned through this module, which serves to prepare people practically for their application in data analysis. The emphasis will be on the basic probability rules pertaining to a single event, from which learners will build a basic theoretical formation for themselves in the first part … Read more
INTRODUCTION – Tree-Based Modeling This whole section is solely centered on supervised learning, a subfield of machine learning, where the participants learn about testing and validating the performance of many supervised machine learning models, such as decision trees, random forests, and gradient boosting. This deep dive into the understanding of these models will give students … Read more
INTRODUCTION – Clean Your Data For the final segment of the course, just remember that you have to learn about three very important EDA components. These include data cleaning, data joining, and data validation. These become part of the processes by which you develop your data-analytic skills, helping you in extracting meanings from different datasets. … Read more
INTRODUCTION – Workflow for Building Complex Models They are trained on facts until the month of October 2023. This unit exposes participants to the well-structured workflow within which data professionals work for machine learning projects. The course will explain the various important steps in this workflow and make participants appreciate the importance of each stage … Read more
INTRODUCTION – Logistic Regression In this segment, participants are going to learn binomial logistic regression-a statistical method that classifies data into two different classes. The first exploration seems to cover thoroughly the theory and techniques involved in binomial logistics regression. Students should possess an excellent understanding of theory and practice in developing and interpreting such … Read more
INTRODUCTION – Advanced Hypothesis Testing During this module, students will increase their knowledge of hypothesis testing through the study of further important and common critical statistics such as the Chi-squared test and the analysis of variance (ANOVA). These are part and parcel of a statistician’s tool kit and give him the ability to perform tests … Read more
INTRODUCTION – Multiple Linear Regression From the simple basics on simple regression, participants will now advance into the more complicated world of multiple-linear regression, where there will be many very important variables combined together to make a good prediction model. The simple principles of linear regression have been the starting point for this section, and … Read more
INTRODUCTION – Simple Linear Regression The training sounds intense, going up to the complicated relationship modeling of data, focusing on correlation relationships. It is great counterpart the curriculum-an intensive course ensuring the participants profusely understand model application in interpreting complicated interconnections among data and investigate aspects of correlation in the analysis of data. Practical being … Read more
Introduction to Complex Data Relationships This will be an exhaustive journey into regression models, covering every possible aspect in a step-by-step manner by the participant. They would start with a thorough understanding of all the core assumptions and their methods of interpretation, and thus would be equipped with all that would be needed to create … Read more