DETAILED CURRICULUM : MODULES

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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