Course Description
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.
This is module #10
Modules
- Module 1 : Introduction to Python, imp libraries and installation
- Module 2: Data Structures in Python, Lists and Tuples and Various Operations
- Module 3: Data Structures in Python, Dictionaries and Sets and Various Operations
- Module 4: Introduction to Numpy, 1D Array and 2D Arrays
- Module 5: Deep Dive into Numpy and Various Operations with Arrays
- Module 6: Introduction to Pandas, Pandas Series and Various Operations on Series
- Module 7: Intro to Pandas DataFrame and Various operations with DataFrames
- Module 8: Data Cleaning and Transformation with Pandas
- Module 9: Data Visualization with Matplotlib/Seaborn
- Module 10: Introduction to Machine Learning and Scikit-Learn
Learning Outcomes
- 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.
Prerequisites
- Basic Python knowledge is assumed
- Some software development experience (including languages, databases…)
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.
