Basic Statistics and Regression in Python Online Course
Basic Statistics and Regression in Python Online Course
This online course is tailored for machine learning enthusiasts eager to build a solid foundation in basic statistics and regression techniques using Python. It begins with environment setup and an introduction to Python and its key libraries. You’ll explore the fundamentals of machine learning, types of data, and essential statistical methods, including Central Tendency Analysis, variance, and standard deviation.
As the course progresses, you’ll dive into advanced concepts like percentiles, normal and uniform distributions, z-scores, and various regression techniques (linear, polynomial, and multiple). These techniques are taught through manual calculations and Python functions, with increasing dataset complexity handled using CSV files. The course also emphasizes finding regression coefficients traditionally and through Python for efficiency.
Finally, you’ll learn data normalization and standardization techniques to enhance algorithm performance. By the end, you’ll have a comprehensive understanding of statistical regression and its practical application in machine learning using Python.
Key Benefits
- This course provides in-depth coverage of Python programming essentials, including coding, data visualization, loops, variables, and functions, ensuring a strong foundation in the language.
- It emphasizes both manual calculations and Python-based implementations to help learners understand the nuances and practical differences between traditional methods and automated solutions.
- The course spans beginner to advanced levels, introducing mathematical and statistical concepts critical for understanding and implementing machine learning algorithms effectively.
Target Audience
This course is designed for beginners and individuals eager to understand the mathematical foundations of machine learning. No prior coding experience or advanced knowledge is required—just a strong learning mindset and enthusiasm for exploring concepts.
It is particularly beneficial for those who wish to uncover the underlying mechanics of Python functions and algorithms, even at an introductory level. The only prerequisites are basic computer literacy and a keen interest in learning mathematics as it applies to machine learning.
Learning Objectives
- Learn how to prepare and configure the required environment for Python programming and statistical analysis.
- Understand key statistical measures, including mean, median, and mode, to analyze data distributions effectively.
- Explore foundational statistical techniques and their applications in data analysis.
- Master various regression techniques, including linear, polynomial, and multiple regression, to build predictive models.
- Gain hands-on experience with essential Python libraries such as NumPy, Matplotlib, and scikit-learn for data manipulation, visualization, and model implementation.
- Learn advanced techniques to scale and preprocess data, improving the performance and accuracy of machine learning algorithms.
Course Outline
The Basic Statistics and Regression in Python Exam covers the following topics -
Module 1 - Environment Setup – Preparing Your Computer
- Environment Setup: Part 1
- Environment Setup: Part 2
Module 2 - Key Components of Anaconda
- Overview of Essential Components Included in Anaconda
Module 3 - Python Basics - Assignments
- Python Basics: Assignment
Module 4 - Python Basics - Flow Control
- Flow Control: Part 1
- Flow Control: Part 2
Module 5 - Python Basics - Lists and Tuples
- Understanding Lists and Tuples in Python
Module 6 - Python Basics - Dictionaries and Functions
- Dictionaries and Functions: Part 1
- Dictionaries and Functions: Part 2
Module 7 - Introduction to NumPy
- NumPy Basics: Part 1
- NumPy Basics: Part 2
Module 8 - Introduction to Matplotlib
- ○ Matplotlib Basics: Part 1
- ○ Matplotlib Basics: Part 2
Module 9 - Fundamentals of Data for Machine Learning
- Basics of Data for Machine Learning
Module 10 - Central Tendency - Mean
- Understanding Mean Calculation
Module 11 - Central Tendency - Median and Mode
- Median and Mode: Part 1
- Median and Mode: Part 2
Module 12 - Variance and Standard Deviation - Manual Calculation
- Manual Calculation: Part 1
- Manual Calculation: Part 2
Module 13 - Variance and Standard Deviation in Python
- Using Python to Calculate Variance and Standard Deviation
Module 14 - Percentiles - Manual Calculation
- Step-by-Step Percentile Calculation
Module 15 - Percentiles in Python
- Calculating Percentiles Using Python
Module 16 - Uniform Distribution
- Overview of Uniform Distribution
Module 17 - Normal Distribution
- Normal Distribution: Part 1
- Normal Distribution: Part 2
Module 18 - Z-Score Calculation - Manual Method
- Calculating Z-Scores Manually
Module 19 - Z-Score Calculation in Python
- Computing Z-Scores Using Python
Module 20 - Scatter Plot for Multivariable Datasets
- Visualizing Multi-Variable Datasets Using Scatter Plots
Module 21 - Linear Regression Overview
- Introduction to Linear Regression
- Manually Calculating Linear Regression Correlation Coefficient: Part 1
- Manually Calculating Linear Regression Correlation Coefficient: Part 2
- Manually Deriving the Linear Regression Slope Equation: Part 1
- Manually Deriving the Linear Regression Slope Equation: Part 2
- Predicting Future Values Using Linear Regression Equation
Module 22 - Linear Regression in Python
- Introduction to Linear Regression with Python
- Implementing Linear Regression in Python: Part 1
- Implementing Linear Regression in Python: Part 2
- Understanding Strong and Weak Linear Regression Models
- Future Value Prediction with Linear Regression in Python
Module 23 - Introduction to Polynomial Regression
- Visualizing Polynomial Regression
- Prediction and R² Value in Polynomial Regression
- Finding SD Components in Polynomial Regression
- Polynomial Regression Using Manual Methods
- Calculating SD Components for Polynomial Regression (abc Method)
- Deriving Polynomial Regression Equations and Predictions
- Understanding Polynomial Regression Coefficients
Module 24 - Introduction to Multiple Regression
- Multiple Regression Overview
- Importing Data as CSV for Multiple Regression in Python
- Visualizing Data for Multiple Regression in Python
- Creating Regression Objects and Making Predictions Using Python
Module 25 - Manual Approach to Multiple Regression
- Introduction and Finding Means for Multiple Regression
- Identifying Components of Multiple Regression: Part 1
- Identifying Components of Multiple Regression: Part 2
- Finding Coefficients (abc) for Multiple Regression
- Deriving Predictions and Coefficients for Multiple Regression Equations
Module 26 - Introduction to Feature Scaling
- Overview of Feature Scaling
- Standardization Scaling in Python: Part 1
- Standardization Scaling in Python: Part 2
- Manual Calculation for Standardization Scaling
