A short summary
Do you want to learn more about data science and machine learning, but you don’t know where to start or how to interpret the concepts?
Are you interested in learning Python programming but have a fear of coding?
This course seems to be a good fit for you!
Even if you’ve studied machine learning before, this course will help you study all of the methods and understand the concepts behind each word.
This course is fully organized, and we do not begin in the centre! We simply begin with the definition of each word and work our way up to applying it in Python. The following is the course’s structure:
Introduction and all necessary installations are covered in Chapter 1.
Useful Machine Learning Libraries (Chapter 2) (NumPy, Pandas & Matplotlib)
Preprocessing (Chapter 3)
Forms of Machine Learning (Chapter 4)
Supervised Learning: Grouping (Chapter 5)
Regression of Supervised Learning (Chapter 6)
Unsupervised Learning: Clustering (Chapter 7)
Model Tuning (Chapter 8)
You also learn how to deal with various actual datasets and how to use them to build the models. All of the Python code templates that we create together during the course are available to download using the resource button at the bottom of each line.
Keep in mind! That this course is designed for people with every level of experience, since all topics would be explained from the ground up! Also, Python programming will be demonstrated from the ground up, and what you need to remember is the Python syntax.
This course is planned for the following individuals:
- Beginners, intermediate, and even advanced students in the area of artificial intelligence,
- Data Science, and Machine Learning with at least high school mathematic skills are welcome.
- Students in college who want to secure their potential employment
- Employees who wish to boost their work efficiency by learning machine learning
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