![]() Recognize the significance of feature scaling and selection and learn how to apply techniques such as standardization and normalization to improve model convergence and interpretability. Identify missing data and outliers within datasets and implement appropriate strategies to handle them effectively. You will gain insights into various regression techniques such as linear regression, polynomial regression, and logistic regression, and their implementation using Python programming. This module is crucial to building strong foundational knowledge in predictive modeling and data analysis. You will learn how to transform raw data into a clean, normalized format by delving into real-world datasets, ensuring accurate and reliable linear regression model outcomes. Through hands-on learning, participants will understand the importance of data quality, addressing missing values, outlier detection, and feature scaling. Module description: The Linear Regression - Data Preprocessing module is a fundamental course that equips participants with essential skills for preparing and optimizing data before applying linear regression techniques. Module 3: Linear Regression - Data Preprocessing Develop the ability to formulate customized time-series forecasting strategies based on data characteristics. Discover various forecasting methods, such as ARIMA models and seasonal forecasting techniques, and implement them using Python programming. You will acquire the ability to build forecasting models for future predictions based on historical data. Utilizing methods like ARIMA, exponential smoothing, and state-space models, they enable accurate forecasting, empowering decision-makers across various fields to make informed choices based on data-driven predictions. By analyzing historical patterns, trends, and seasonal variations, these models provide insights into data behavior over time. Module description: Time-series models are powerful tools designed to uncover patterns and predict future trends within sequential data. Acquire the skill to differentiate between different types of patterns and understand their implications in forecasting. Discover techniques for exploratory data analysis, time series decomposition, trend analysis, and handling seasonality. You will develop expertise in time series analysis and forecasting. This module is a valuable asset for professionals seeking to harness the potential of temporal data. By delving into real-world case studies and ethical considerations, participants will be equipped to make strategic decisions across industries using the power of time-series analysis. ![]() With hands-on experience in leading software, they will learn to build, validate, and interpret forecasting models. You will master fundamental concepts such as trend identification, seasonality, and model selection. Module description: The Time-Series Analysis and Forecasting module provides a comprehensive exploration of techniques to extract insights and predict trends from sequential data. Module 1: Time-Series Analysis and Forecasting
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