Linear Regression: Absolute Fundamentals
What you'll learn
- An idea on Machine Learning and Linear Regression
- Understand the fundamentals of Machine learning
- How to start working with Data Science in Python
- Regression Mathematics
- Yes, A basic knowledge in Python 3 is preferred for technical part.
Hello, everybody! The Machine Learning Absolute Fundamentals for Linear Regression course is open to all students. Beginner Python developers who wish to begin their machine learning adventure should take this course. In this lesson, we'll apply a linear regression model from the Python scikit-learn module to forecast the total number of COVID19 positive cases in a specific Indian state.
You'll be able to: once you've finished this course.
1. Explain what machine learning is
2. Describe what a dataset is.
3. Describe the functions of machine learning.
4. Explanation of the linear regression concept
5. Describe the cost function and the line of greatest fit (MSE)
6. To read and prepare the dataset, use the pandas library functions.
7. Data division for training and testing
8. Using Sklearn, develop a linear regression model and train it.
9. Assess the model and make value predictions
10. Data visualisation with Matplotlib
In linear regression, linear predictor functions are used to model relationships, with the model's unknown parameters being estimated from the data. These models are referred to as linear models. The conditional mean of the response is typically considered to be an affine function of the values of the explanatory variables (or predictors) the conditional median or another quantile is occasionally employed. In common with all other types of regression analysis, linear regression concentrates on the conditional probability distribution of the response given the values of the predictors rather than the joint probability distribution of all these variables, which is the purview of multivariate analysis.
Who this course is for:
- Beginner Python developers who are curious about Machine Learning
Languages - C, C++, Python, Verilog, System Verilog
Hardware - Digital Logic Design, Computer Architecture, VLSI Design, Analog Electronics, Signal Processing, Embedded Systems
Software - Data Structures & Algorithms, Operating Systems, Database Management Systems, Computer Networks, Machine Learning, Deep Learning.
Tools - Xilinx Vivado, Matlab, Multisim, Altium, Arduino IDE, TinkerCAD, Tanner EDA, Cadence Virtuoso
Boards - Arduino, 8051, TIVA, Raspberry Pi, NodeMCU
Areas of Interest - Artificial Intelligence, Digital Design, Software Engineering, Algorithms