Predictive Modeling with Python

Overview

This course provides a practical introduction to statistical analysis and machine learning with Python. Learn essential machine learning concepts, methods, and algorithms with a focus on applying them to solve real-world problems. By the end of the course, you will: - Understand different data types used in statistical analysis. - Learn techniques to manage inconsistent data effectively. - Perform hypothesis testing using parametric and non-parametric tests. - Develop exploratory data analysis (EDA) models using statistical and machine learning methods. - Enhance machine learning models through evaluation and optimization techniques. Designed for individuals with a foundational knowledge of Python programming and basic statistical concepts, this course is ideal for aspiring data analysts, data scientists, business executives, machine learning engineers, and anyone passionate about data-driven decision-making. Gain hands-on experience in statistical and predictive modeling and apply your skills to real-world scenarios. Enroll in "Predictive Modeling with Python" today and take your expertise to the next level!

Syllabus

Data and Information

In the first module of this course, learners will explore various data types and utilize different measures of central tendency and measures of dispersion to address data inconsistencies.

Probability Distribution Function

In this module, learners will learn to manage data using probability distribution functions. Learners will start by applying the Bernoulli distribution to model categorical data, explore the Poisson distribution for forecasting, and utilize the Exponential and Normal distributions for regression modeling.

Inferential Statistics

In the third module of this course, Learners will learn to apply the Central Limit Theorem in scenarios where data may be improperly distributed. Identify and analyze sample data, using both parametric and non-parametric methods to handle various test cases for hypothesis testing and decision-making.

Introduction to (Exploratory Data Analysis) EDA

In the fourth module, learners will explore implementing Exploratory Data Analysis (EDA) on large, complex datasets by conducting both univariate and multivariate analysis. They will also learn how to clean and process data, as well as perform feature engineering to prepare the data for analysis.

Predictive Modeling and Analysis

In this module, learners will learn how to use machine learning models to extract insights from data. They will apply regression and classification algorithms and then optimize the results produced by these models.

Course Wrap-Up and Assessment

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Probability, Statistical Modeling, and Machine Learning.