MODULE 1
Introduction to Machine Learning
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What is Machine learing?
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Overview about scikit-learn package
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Types of ML
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Basic steps of ML
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ML algorithms
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Machine learning examples
MODULE 2
Data Preprocessing
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Dealing with missing data
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Identifying missing values
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Imputing missing values
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Drop samples with missing values
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Handling with categorical data
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Nominal and Ordinal features
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Encoding class labels
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One hot encoding
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Split data into training and testing sets
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Feature scaling
MODULE 3
Machine Learning Classifiers
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K-Nearest Neighbors (KNN)
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Decision tree
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Random forest
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Support vector machines (SVM)
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Naive Bayes
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Logistic Regression
MODULE 4
Regression Based Learning
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Simple Regression
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Multiple Regression
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Predicting house prices with Regression
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Polynomial Regression
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Bagging Regression
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Boosting Regression
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Support vector Regression
MODULE 5
Clustering Based Learning
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Definition
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Types of clustering
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The k-means clustering algorithm
MODULE 6
Natural Language Processing
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Install nltk
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Tokenize words
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Tokenizing sentences
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Stop words with NLTK
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Stemming words with NLTK
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Twitter Sentiment analysis Project
MODULE 7
Working with OpenCV
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Installing opencv
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Reading and writing images
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Applying image filters
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Writing text on images
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Image Manipulations
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Face detection Project
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Speech Recognition Project