Python, NumPy, deep learning, AI, machine learning, simple linear regression, and data science
For Python, Machine Learning, and Data Science courses [2023], this course has been created. The course has covered the fundamentals of Python, Machine Learning, and Data Science [2023] before concluding on a professional level. To make the course useful for students, real-world examples from the Python, Machine Learning, and Data Science Courses [2023] have also been added.
What you will discover
- Learning Machines
- Machine intelligence
- machine learning under supervision
- Controlled ML Model
- Describe regression.
- Basic LR
- Multi-LR
- Regression using Polynomials
- Development of Models
- Preprocessing of Data
- Code Regression
- Scripting in Scikit
- gathering of data
- Division of Data
- Multiple Scatter Plot
- SML KNN-Model
- Choice Tree
- SML data visualization
- Support Vector Theory.
- dispersed plots
- Matplotlib Errors
- Colors that are Dispersed
- Scatter plot vs. a plot
- Plotting bars
- Several-Bar Plot
- Subplots and Stacked Plots
- Hexagonal Plot
- a data set
- Distribution of Data
What you’ll discover
- Learning Machines
- Regression Definition
- Uncomplicated Linear Regression
- both Python and data science
Exist any prerequisites or course requirements?
- smartphone, laptop, or computer
- Who this course is for: Anyone interested in learning Python, data science, and machine learning
