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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Python and machine learning

Class description

Uses of the Python and machine Learning

Course objectives

Course Format

Class Syllabus

Display their FAQs

Class Syllabus

Module 1 - Introduction to Python Crash Course

Module 2 - Introduction to Descriptive Statistics

Module 3 - Supervised Learning and Linear Regression

Module 4 - Classification and Logistic Regression

Module 5 - Decision Tree and Random Forest

Module 6 - Unsupervised Learning

Module 7 - Naïve Bayes and Support Vector Machine

Module 8 - Natural Language Processing and Text Mining

Module 9 - Introduction to Deep Learning