Python Machine Learning

MODULE 1

Introduction to Machine Learning

  • What is Machine learing?
  • Overview about scikit-learn package
  • Types of ML
  • Basic steps of ML
  • ML algorithms
  • Machine learning examples

MODULE 2

Data Preprocessing

  • Dealing with missing data
  • Identifying missing values
  • Imputing missing values
  • Drop samples with missing values
  • Handling with categorical data
  • Nominal and Ordinal features
  • Encoding class labels
  • One hot encoding
  • Split data into training and testing sets
  • Feature scaling

MODULE 3

Machine Learning Classifiers

  • K-Nearest Neighbors (KNN)
  • Decision tree
  • Random forest
  • Support vector machines (SVM)
  • Naive Bayes
  • Logistic Regression

MODULE 4

Regression Based Learning

  • Simple Regression
  • Multiple Regression
  • Predicting house prices with Regression
  • Polynomial Regression
  • Bagging Regression
  • Boosting Regression
  • Support vector Regression

MODULE 5

Clustering Based Learning

  • Definition
  • 2. Types of clustering
  • The k-means clustering algorithm

MODULE 6

Natural Language Processing

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words with NLTK
  • Stemming words with NLTK
  • Twitter Sentiment analysis Project

MODULE 7

Working with OpenCV

  • Installing opencv
  • Reading and writing images
  • Applying image filters
  • Writing text on images
  • Image Manipulations
  • Face detection Project
  • Speech Recognition Project