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
• 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