Machine learning with many practical examples. Regression, Classification and much more
What you will learn from this Course:
- Create machine learning applications in Python as well as R
- Apply Machine Learning to own data
- You will learn Machine Learning clearly and concisely
- Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. …)
- No dry mathematics – everything explained vividly
- Use popular tools like Sklearn, and Caret
- You will know when to use which machine learning model
Requirements for this Course
- You should have programmed a little before.
- No knowledge of Python or R is required.
- All necessary tools (R, RStudio, Anaconda, …) will be installed together in the course.
Description:
AI is just truly fun when you assess genuine information. That is the reason you examine a lot of pragmatic models in this course:
- Gauge the worth of trade-in vehicles
- Compose a spam channel
- Analyze bosom disease
All code models are displayed in both programming dialects – so you can pick whether you need to see the course in Python, R, or in the two dialects!
After the course you can apply Machine Learning to your own information and settle on educated choices:
You know when which models may come into question and how to analyze them. You can investigate which segments are required, regardless of whether extra information is required, and know which information should be ready ahead of time.
This course covers the significant subjects:
- Relapse
- Arrangement
On this load of subjects, you will find out with regards to various calculations. The thoughts behind them are essentially clarified – not dry numerical equations, yet clear graphical clarifications.
We utilize normal devices (Sklearn, NLTK, caret, data. table, …), which are likewise utilized for genuine AI projects.
What do you realize?
- Relapse
- Straight Regression
- Polynomial Regression
- Arrangement
- Calculated Regression
- Guileless Bayes
- Choice trees
- Arbitrary Forest
You will likewise figure out how to utilize Machine Learning:
- Peruse in information and set it up for your model
- With the complete viable model, clarified bit by bit
- Track down the best hyper boundaries for your model
- “Boundary Tuning”
- Contrast models and one another:
- How the precision worth of a model can delude you and what can be done
- K-Fold Cross-Validation
- Coefficient of assurance
Who this course is for:
- Developers interested in Machine Learning
Course content:
- Introduction
- Setting Up The Python Environment
- Setting Up The R Environment
- Basics Machine-Learning
- Linear Regression
- Project: Linear Regression
- Train/Test
- Linear Regression With Multiple Variables
- Compare Models: Coefficient of Determination
- Practical Project:Coefficient of Determination
