Requirements
Python's basic syntax
Description
Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?
Are you interested in programming in Python, but you always afraid of coding?
I think this course is for you!
Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.
This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:
Chapter1: Introduction and all required installations
Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)
Chapter3: Preprocessing
Chapter4: Machine Learning Types
Chapter5: Supervised Learning: Classification
Chapter6: Supervised Learning: Regression
Chapter7: Unsupervised Learning: Clustering
Chapter8: Model Tuning
Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.
Remember! That this course is created for you with any background as all the concepts will be explained from the basic! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.
Who this course is for:
Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic
Beginners, intermediate, and even advanced students in the field of artificial intelligence, Data Science and Machine Learning
Students in college that looking for securing their future jobs
Employees that look forward to excelling their job level by learning machine learning
Anyone who afraid of coding in Python but interested in Machine Learning Concepts
Anyone who wants to create a new business using machine learning
Graduate students and researchers that want to apply machine learning models in their thesis and projects
