Intro to Python Programming May registration closes on June 10, 2022

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Python and machine learning

Class description

This is an instructor-led course to introduce you to the Python programming language. Python is one of the most popular programming languages used in industries ranging from gaming to finance. Python is an interpreted, object-oriented high-level programming language, which has recently become a popular tool in industry and in academia.


Python is free to use, easy to install and learn and its open source. In this course, you will learn to create programs, functions, complex data structures and to collect user input with Python. Furthermore, you will learn to read and write from external sources such as files and databases By learning these concepts, students will have a starting point to learn a few of the many ways that Python is utilized from basic reporting to complex machine learning solutions and much more.

Uses of the Python and machine Learning

  • The Python programming language is used by business analysts, software developers to analyze data and to create captivating yet insightful data visualizations
  • The Python programming language can be used for developing both desktop, web and mobile applications.
  • Python can be used for creating Windows, UNIX and Mac applications, from simple console and web-based to
  • elaborate graphic interfaces for video games.
  • Also, you can use Python for developing complex scientific and numeric applications for Machine Learning, Robotics and Finance


Course objectives

  • Introduce students to the basics of Python.
  • Provide students with the hands-on Python programming building block skills needed to develop applications
  • Create working Python scripts following best practices
  • Use python data types appropriately
  • Read and write files with both text and binary data
  • Search and replace text with regular expressions
  • Get familiar with the standard library and its work-saving modules
  • Create "real-world", basic level professional Python applications
  • Know when to use collections such as lists, dictionaries, and sets
  • Understand Pythonic features such as comprehensions and iterators
  • Write robust code using exception handling

Course Format

This course is a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions,

labs, and project work including a capstone assignment.

Class Syllabus

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Class Syllabus

Module 1 - Introduction to Python Crash Course

  •  System set up
  • Data Structures, Numbers, Strings and Loops
  • Input / Output Operations
  • Algorithms and conditional functions
  • Data Analysis  Modules (Numpy, Pandas)
  • Pandas Dataframes and Series
  • Pandas Operations

Module 2 - Introduction to Descriptive Statistics

  •  Introduction to Machine Learning
  • Need of Machine Learning
  •  Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning,
  • Machine Learning with Python, and the applications of Machine Learning

Module 3 - Supervised Learning and Linear Regression

  • Introduction to  types of supervised learning, such as regression and classification
  •  Introduction to regression
  • Simple linear regression
  • Multiple linear regression and assumptions in linear regression (Multivariate)
  •  Polynomial Regression

Module 4 - Classification and Logistic Regression

  • Introduction to classification
  • Linear regression vs logistic regression
  • Logistic Regression


Module 5 - Decision Tree and Random Forest

  • Implementing a decision tree from scratch in Python
  • Using Python library Scikit-Learn to build a decision tree and a random forest
  • Visualizing the tree and changing the hyper-parameters in the random forest

Module 6 - Unsupervised Learning

  • Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
  • Introduction to k-means clustering
  • Introduction to PCA
  • Dimensionality reduction with PCA

Module 7 - Naïve Bayes and Support Vector Machine

  • Introduction to probabilistic classifiers
  • Understanding Naïve Bayes and math behind the Bayes theorem
  • Understanding a support vector machine (SVM)
  • Kernel functions in SVM and math behind SVM

Module 8 - Natural Language Processing and Text Mining

  •  Introduction to Natural Language Processing (NLP)
  • Introduction to text mining
  •  Importance and applications of text mining
  • How NPL works with text mining
  • Writing and reading to word files
  • Language Toolkit (NLTK) environment
  • Text mining: Its cleaning, pre-processing, and text classification

Module 9 - Introduction to Deep Learning

  • Biological neural networks vs artificial neural networks
  •  Understanding perception learning algorithm, introduction to Deep Learning frameworks, and Tensor Flow constants, variables, and place-holders

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