Machine Learning

Course Description

This comprehensive course covers essential Python programming skills, foundational data science libraries, and advanced machine learning techniques. Designed for both beginners and those looking to deepen their expertise, this course provides a solid understanding of Python basics and explores real-world applications in data science and machine learning. The curriculum includes practical projects and hands-on experience to solidify your learning.

What You Will Learn

• The fundamentals of Python programming including variables, data types, and control structures.
• How to use Python functions, handle arguments, and implement lambda functions.
  

  Python Basics

  • Python Introduction
  • Variables in Python
  • Data Types and Operators in Python
  • Strings in Python
  • List in Python
  • Tuple in Python
  • Sets in Python
  • Dictionary in Python

  Control Structures in Python

  • if else in Python
  • For Loop in Python
  • while loop in Python
  • Break and Continue Statements in Python

  Functions in Python

  • Function in Python
  • Args and Kwargs in Python
  • Lambda Function in Python

  Object-Oriented Programming (OOP) in Python

  • OOPs in Python
  • Class and Object in Python
  • Constructor & Self in Python
  • Methods and Variables in Python
  • Inheritance in Python
  • Types of Inheritance in Python

  Data Science Libraries in Python

  • Numpy in Python
  • Pandas

  Introduction to Machine Learning

  • Introduction to Machine Learning
  • Types of Machine Learning Categories
  • Linear Regression Algorithm
  • Logistic Regression in Machine Learning
  • Decision Tree Algorithm
  • K Nearest Neighbors
  • Confusion Matrix
  • Correlation
  • Applications of ML
  • Data Collection and Description

  Data Analysis Tools

  • Data Analysis with Excel
  • Jupyter Notebook Introduction

  Machine Learning Algorithms in Practice

  • Linear Regression
  • Housing Price Prediction using Linear Regression
  • Housing Price Prediction using Linear Regression (Continuation)
  • Chronic Kidney Disease Analysis
  • Diabetes Prediction Using ML

  Introduction to Deep Learning

  • Introduction to Convolutional Neural Networks (CNN)
  • Mathematics Behind Convolutional Neural Networks
  • Computer Vision
  • CNN for Flower Classification
  • Recognizing the Face of a Person using YOLOv3 and Deep Learning
  • Real-time Object Detection with YOLO and its Application
  • NLP (Natural Language Processing)
  • NLTK (Natural Language Toolkit)

  Real-Time Tasks

  • Chronic Kidney Disease Prediction
  • Predicting the Trend of House Prices in the U.S.
  • Classifying Flowers using Neural Networks
  • Recognizing the Face of a Person using YOLOv3
  • Credit Card Fraud Detection using ML
  • Fake News Classification
  • Detection of Retinal Pigmentosa in Pediatrics using CNN with Tkinter Framework
  • Covid-Affected Person Detection using Supervised Learning Approach
  • Heart Disease Prediction using Machine Learning