Essentials of Python for Machine Learning and Artificial Intelligence

This course – with expert Pramod Gupta -- provides an introduction to the Python programming language essential for data manipulation, statistical analysis, and modeling techniques required for machine learning and artificial intelligence.

Data Science | Core | 5 minutes | Aug 31 | 08:08 PM IST

Overview

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This course includes:

14+ hours of on-demand video
10 modules
Core level
Direct access/chat with the instructor
100% self-paced online
Many downloadable resources
Shareable certificate of completion

This course provides an introduction to the Python programming language essential for data manipulation, statistical analysis, and modeling techniques required for machine learning and artificial intelligence. In this course we will explore the wonderfully concise and expressive use of Python’s advanced module features and apply it in probability, statistical testing, signal processing, financial forecasting, and various other applications. This course covers mathematical operations with array data structures, optimization, Probability Density Function, interpolation, Fast Fourier Transform, basic signal processing and other high performance benefits using the core scientific packages NumPy, Scipy, SkLearn/Scikit learn and Matplotlib. Students will gain a deep understanding and problem solving experience with these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. The course will teach practical aspects of python for data wrangling needed for ML and AI applications so that the students will be able to apply lessons to solve problems using machine learning in their own careers and fields. The course uses examples to guide you through foundational concepts, often employing live algorithms to facilitate visual understanding. Pseudocode will be provided for most of the algorithms covered. You are encouraged to use the pseudocode as a reference to create your own programs in Python. The class has in-class quizzes to gauge learning and group activities including discussion. Homework assignments involving programming in Python are designed for in-depth practice.

Learning Outcomes (At The End Of This Program, You Will Be Able To...)

Learn Python’s underlying object model, operators, and syntax.
Use Python and its libraries interactively through Jupyter Notebooks (IPython).
Manipulate data types in Python, and in particular “container” types: those built into Python (str, tuple, list, dict) as well as those that are the basis of Numpy and Pandas (ndarray, Series, and DataFrames).
Practice the Python mechanisms needed to understand the thousands of data analysis examples available online: flow-of-control, function protocols, sequence unpacking, list comprehensions and other functional programming tools.
Use functions to customize data cleaning and the behavior of data transformations.

Visualization and machine learning algorithms framework.
Be able to solve more complex engineering, financial, mathematical and scientific problems
Develop complex functions and scripts to perform complicated calculations and to visualize the results of these calculations.
Attain deeper understanding of the mathematical toolkit provided by the powerful core packages subject in this course
Acquire in depth hands-on experience
Install and configure Python and essential Python development tools, write Python programs, and run them to generate tabular and graphical results.
Manage and manipulate data, perform data type conversions, merge datasets, deal with missing values, and extract, delete, or transform subsets of data based on logical criteria.
Use Python to perform basic data analysis using data exploration, statistical analysis, and machine learning/AI techniques.

Who Should Attend

Anyone who wants to learn about using Python to build, evaluate or deploy machine learning and Artificial Intelligent models.
Scientists, engineers, business analysts, research who explore and analyze data and wish to present their findings in well-formatted textual and graphical forms.
Anyone wishing to get hands-on experience building machine learning models.
Professionals, students and job-seekers interested in learning the fundamentals of machine learning and data mining and want to learn to build, evaluate or showcase machine learning applications in Python.
The course will be appealing mostly to people that need an introduction to numerical computing and visualization using Python environment and also for technical staff that want to enhance their Python programming skills on the specific topics. Anyone who is interested in using Python’s NumPy, Scipy and Matplotlib packages as prototyping tools would also benefit from the course.

Instructors

Pramod Gupta

Dr. Pramod Gupta is a passionate data scientist and educator with over 20 years of experience in data mining, predictive analytics, and applied machine learning. He excels at transforming complex data into actionable insights and developing algorithms that solve real-world problems. His expertise lies in leveraging advanced statistical and predictive modeling techniques, utilizing tools such as Python, R, and SQL to deliver impactful, data-driven solutions.

Deeply committed to educating the next generation of data scientists and machine learning professionals, Dr. Gupta designs his courses with a strong emphasis on practical applications and solving real-world challenges. He ensures that his students not only grasp theoretical concepts but are also equipped to apply them effectively in their professional careers.

Driven by an enduring passion for learning and sharing knowledge, Dr. Gupta’s career is marked by numerous research publications and significant contributions across diverse industries, including bioinformatics, finance, and retail. He believes that teaching is a two-way exchange—where he gains valuable insights from his students just as much as he imparts knowledge—creating a dynamic and enriching learning environment.

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