Applying Python in AI & ML Now Made Easy with this Coursera Program

This Python for Data Science, AI & Development course is designed to teach learners the basics of the Python programming language and its applications in data science and Artificial Intelligence (AI).

Throughout the Python in AI and ML course, you will learn how to work with various Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. You will also learn to analyze and visualize data using Python and create predictive models using Machine Learning (ML) algorithms. The course is ideal for those who wish to become programming experts and offers numerous insights into the world of data science.

Joseph Santarcangelo, the course instructor, has a doctorate in Electrical Engineering. His research focuses on using ML, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM after completing his doctorate. He takes many courses on data science, ML, and AI on Coursera. His teaching style is good, and he can be followed easily.

Instructors from Coursera teach this 4 module course that consists of several video lectures, quizzes, and programming assignments. By the end of the course, learners get a solid foundation in Python programming and are able to apply it to real-world data science problems.

The course is self-paced, so learners can work through the material at their own pace. It is a great option for anyone learning applications of AI and ML in Python.

The 4 modules of the course are:

• Week 1: Python Basics
  This module teaches the basics of Python and begins by exploring some of the different data types, such as integers, real numbers, and strings. Then teaches how to use expressions in mathematical operations, store values in variables, and the many different ways to manipulate strings.
   
• Week 2: Python Data Structures
  This module delves into Python data structures by explaining the use of lists and tuples and how they can store data collections in a single variable. Next, it deals with dictionaries and how they function by storing data in pairs of keys and values. It ends with Python sets to learn how this type of collection can appear in any order and will only contain unique elements.
   
• Week 3: Python Programming Fundamentals
  This module discusses Python fundamentals and begins with the concepts of conditions and branching. Continue through the module to learn about implementing loops to iterate over sequences, create functions to perform a specific task, perform exception handling to catch errors, and how classes are needed to create objects.
   
• Week 4: Working with Data in Python
  This module explains the basics of working with data in Python and begins the path of learning how to read and write files. Continue the module and uncover the best Python data manipulation and mathematical operations libraries. 
   
• Week 5: APIs and Data Collection
  This module delves into the unique ways to collect data using Application Programing Interfaces (APIs) and web scraping. It further explores data collection by explaining how to read and collect data in different file formats.

I am a third-year computer science student at Gitam University in Bangalore. I liked the course as it gives a quick introduction to the concepts of Python and its libraries, Pandas and NumPy. I really enjoyed doing lab work using IBM Watson Studio.

After doing this course, you will understand the basics and computer logic of data handling and cloud management. The syllabus of the course progresses from basic to advanced level. Practical work is a great way to learn and was a fundamental part of the course.

It is a very good course for people unfamiliar with Python. It just starts with the basics and focuses on data structures which is essential for data science, not on unessential Python syntax, which is not useful in this specialization course. Overall, this course is suitable for anyone who wants to learn data science, data analytics, software development, data engineering, AI, and DevOps. You can easily learn the python basics for data science using this course.