Looking to master Machine Learning for your job or as a career enhancement?
Goals of this course:
Go from beginner to advance in Python Programming! Apply your knowledge in Machine Learning Data Science & Data Analysis in this intensive course!
Python is the most widely used programming language on the planet. In the United States, the average pay for a Python developer is $116k. That's almost $30k more than the competition!
This course (Python Programming Beginner to Advance + Machine Learning) will help you reach your dreams!
Python, Machine Learning, Data Science and Data Analysis is utilized by large corporations like as Google, Facebook, Dropbox, Reddit, Spotify, Quora, and others.
Its clear and beautiful syntax appeals to mathematicians, scientists, engineers, and developers.
It's the most popular language for AI and machine learning, and it's also the best language for novices to learn. Much less difficult than C++ or JavaScript! It is also used for various lucrative fields such as Machine Learning, Data Science and Data Analysis.
This course covers all Python has to offer, from the fundamentals to more advanced subjects.
A great blend of theory and practice, jam-packed with real-world examples, exercises, and step-by-step answers - devoid of "fluff" and long explanation!
Learn how to use Python to automation, web development, and machine learning.
You will learn the following:
Introduction to Python course:
Intermediate Python- Functions, Modules, Classes and Exceptions
Introduction Data Analysis in Python
Applied Data Analysis in Python - Machine learning and Data science
Introduction to Deep Learning - Tensorflow for image analysis
Machine learning specialized libraries and frameworks are available in a large number of Python distributions, making the development process easier and decreasing development time. Python's straightforward syntax and readability enable it to be used for fast testing of complicated algorithms while also making it accessible to those who are not programmers.
Data science with Python is made simpler by the availability of a plethora of libraries, such as NumPy, Pandas, and Matplotlib, which facilitate data cleaning, data analysis, data visualization, and machine learning activities.
In data analysis using python python's ability to create and manage data structures quickly, for example, is one of the most common applications of the language in data analysis — Pandas, for example, provides a plethora of tools for manipulating, analyzing, and even representing complex datasets — and this is one of the most common applications of Python in data analysis.
We had a team people editing and marketing the course, the editing was done by Mohammad Chowdhury and the marketing was done by Mohammad Fahmid Chowdhury.
The course was created by professors with years of Python experience. The course content was created by Matt Williams, he is a professor with years of Python and Data Science experience, under the CC Attribution license.
Lectures: adapted from Creative Commons video content recorded by Tyler Caraza-Harter for Data Science Programming II (CS 320) at the University of Wisconsin-Madison; this adaptation is not connected with, sponsored by, nor endorsed by Caraza-Harter
