Machine Learning & Data Science A-Z: Hands-on Python 2021
This course includes:
- 14.5 hours on-demand video
- 2 articles
- 4 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
What you'll learn
- Understanding the basic concepts
- Complete tutorial about basic packages like Numpy and Pandas
- Data Visualization
- Data Preprocessing
- Understanding the concept behind the algorithms
- Developing different kinds of Machine Learning models
- Knowing how to optimize your models' hyperparameters
- Learn how to develop models based on the requirement of your future business
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 excel their job level by learning machine learning
- Anyone who afraid of coding in Python but interested in Machine Learning Concepts
- Any one 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
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:
Chapter 1: Introduction and all required installations
Chapter 2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)
Chapter 3: Preprocessing
Chapter 4: Machine Learning Types
Chapter 5: Supervised Learning: Classification
Chapter 6: Supervised Learning: Regression
Chapter 7: Unsupervised Learning: Clustering
Chapter 8: Model Tuning
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