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!
Demonstration: Logistic Regression - Model Execution
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Skills You'll Learn
Data Analysis, Data Validation, Descriptive Statistics, Data Processing, Regression Analysis, Probability & Statistics, Statistical Hypothesis Testing, Machine Learning, Data Cleansing, Exploratory Data Analysis, Predictive Modeling, Feature Engineering, Probability Distribution, Statistical Inference, Statistical Analysis
From the lesson
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.
Taught By
Edureka