Python Machine Learning Course

Python is a powerful general-purpose programming language that has been around since the 1980s. It is one of the recommended languages to learn for students who are beginning to learn coding. The aim of this class is to help the students jumpstart their journey in Python. This not only aims to provide basics of Python, but as the course progresses, students will have a chance to transfer to a more advanced Python class.

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Languages

Python

Start date

Course Schedule

Experience Level

Beginner

Age Level

Middle School
High School

Summary

Python is a powerful general-purpose programming language that has been around since the 1980s. It is one of the recommended languages to learn for students who are beginning to learn coding. The aim of this class is to help the students jumpstart their journey in Python. This not only aims to provide basics of Python, but as the course progresses, students will have a chance to transfer to a more advanced Python class.

Introduction to Python Programming

  • Overview of Python
  • History of Python
  • Python Basics – variables, identifiers, indentation
  • Data Structures in Python (list, string, sets, tuples, dictionary)
  • Statements in Python (conditional, iterative, jump)
  • OOPS concepts
  • Exception Handling Regular Expression

Introduction to various packages and related functions

  • Numpy, Pandas and Matplotlib
  • Pandas Module
  • Series
  • Data Frames
  • Numpy Module
  • Numpy arrays
  • Numpy operations
  • Matplotlib module
  • Plotting information
  • Bar Charts and Histogram
  • Box and Whisker Plots
  • Heatmap
  • Scatter Plots

Data Wrangling using Python

  • NumPy – Arrays
  • Data Operations (Selection , Append , Concat , Joins)
  • Univariate Analysis
  • Multivariate Analysis
  • Handling Missing Values
  • Handling Outliers

Introduction to Machine Learning with Python

  • What is Machine Learning?
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Basic Probability required for Machine Learning
  • Linear Algebra required for Machine Learning

Supervised Learning - Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Polynomial Regression
  • R2 and RMSE

Supervised Learning – Classification

  • Logistic Regression
  • Decision Trees
  • Random Forests
  • SVM
  • Naïve Bayes
  • Confusion Matrix

Project development

Some of the sample projects: https://www.siliconvalley4u.com/swatstore
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