Skip to content
This repository was archived by the owner on Mar 7, 2026. It is now read-only.

README.md

Latest commit

 

History

History
121 lines (88 loc) · 3.13 KB

README.md

File metadata and controls

121 lines (88 loc) · 3.13 KB
Raw

Introduction:

This repo is full sections and knowledge. There are three main sections in this repo

  1. Lectures and Exercises on Python Programming Languages.
  2. Using Python you can learn the basic of Machine Learning Models, Deep Learning Models.
  3. Project using different Machine Learning Model.

Sections:

1. Python Programming Language:

In this section you will the basic of python programming Language and some important concept for machine learning and deep learning. Each Lecture is stated in Chapters and Some important concepts are stated below:

Chapter 2: Introduction

  1. Control Structure
  2. Functions
  3. Input & Output
  4. Classes and Object
  5. Algorithm
  6. Data Structure
  7. Working with Files
  8. Working with Documents(Word, PDF, Excel)

Chapter 3: Python Libraries

There are multiple libraries that will be used for this project.

  1. NumPy Package
  2. Pandas Packages
  3. Visualization Package
    1. Matplotlib and Pandas Package
    2. Seaborn Visualization

Chapter 4: Probability and Statistics

  1. Probability Theory
  2. Probability Rules
  3. Random Variable
  4. Discrete Probability Distribution
  5. Continuous Probability Density
  6. Conjoint Probability

2. Machine Learning & Deep Learning:

Chapter 5: Machine learning -Part 1

  1. Data Processing
    1. Machine Learning with Scikit-Learning
    2. Data-Processing
    3. Feature Engineering
  2. Unsupervised Learning
    1. Clustering Analysis
    2. Principal Component Analysis(PCA)
    3. Application of Principal Components
  3. Regressions
    1. Training and testing in Machine Learning
    2. Linear Regression basic
    3. Linear Regression Diagnostics
    4. Other Regression Type

Chapter 6: Machine Learning -Part 2

  1. Classification Prediction
    1. Logistic Regression Basic
    2. Logistic Regression Performance Metrics
    3. Naïve Bayes Algorithm
    4. SVM Algorithm
  2. Classification Prediction
    1. Tree Algorithm
    2. Ensemble Algorithms

Chapter 7: Natural Language Processing

  1. Natural Language Processing
    1. String Manipulation
    2. Data Acquisition
    3. Introduction to the Natural Language Processing
    4. Language Model
    5. Representation Model
    6. Classification Analysis
    7. Topic Modeling
  2. Image processing
    1. Image Data
    2. Filtering Image Data
    3. Transforming Image Data

Chapter 8: Deep Learning

  1. Introduction to Deep Learning

Chapter 9: Deep Learning with Keras

  1. Keras Basic
  2. Al with keras
  3. Natural Language processing with Keras

3. Project:

In this section there is a machine learning Projects and following are the model that has being used.

  1. TDIF
  2. Word2Vec
  3. Fast Track
  4. Berd
  5. SVM

The following machine learning Model are being used for the detection of hate speech detection. the reference paper and the dataset are in the repo.

Exercise & Quiz:

In this repo you will also find some more python questions, Exercises and some Quiz for practice as well. You will find the Exercises and Quiz in the Lecture folder.

Contact Info:

For any help leave a question in a issue section. or email the Author:

Name: Abdul Rafay

Email: 99marafay@gmail.com