Machine Learning with Python

K-means

• Introduction to Clustering
• Understanding Income Group data set
• Understanding of K-means Algorithm
• Implementation of K-means in Python(Income Group)
• Elbow Test Method

Dimensionality Reduction:

• Principal Component Analysis(PCA)
• PCA implementation in Python

Statistics: Feature Scaling:

• Normalization
• Standardization
• Implementation of Feature scaling in Python

Decision Tree:

• Understanding of Decision Tree
• Identification of Root Node
• Implementation in Python

Evaluation: Confusion Matrix:

• Accuracy
• Recall
• Precision
• F-Score

Random Forest:

• Understanding of Random Forest
• Bagging Techniques
• Implementation in Python

K-Nearest Neighbours:

• Understanding of KNN algorithm
• Understanding of iris dataset
• Implementation in Python

Linear Regression:

• Understanding of Linear Regression
• Assumptions of Linear Regression
• Implementation in Python
• Problems in achieving accuracy of model
• R-Square
• Adjusted R-Square
• Bias
• Variance
• Trade-Off between Bias and Variance

Polynomial Regression:

• Understanding of Polynomial Regression
• Implementation in Python
• Visualization of output by changing parameters

Regularization Techniques

• Ridge Regression
• LASSO Regression
• Elastic Net Regression

Logistic Regression:

• Understanding of Logistic Regression
• Implementation in Python
• Pros and Cons

Naive Bayes:

• Understanding of Naives Bayes
• Examples
• Pros and Cons
• Implementation in Python

Support Vector Machines:

• Understanding of Support Vector Machines(SVM)
• Understanding of different Scenarios
• Pros and Cons
• Implementation in Python

Azure Cloud: Azure Machine Learning Studio:

• Understanding of Azure ML Studio
• Implementation of case study in Azure ML Studio Demo

NLP Basics:

• What are NLP and NLTK?
• NLP setup and overview
• Reading the text data
• Exploring the dataset
• What are Regular Expressions
• Machine Learning Pipeline

Implementation:

• Removing Punctuation
• Tokenization
• Removing stop words

Supplementing Data Cleaning:

• Stemming
• Lemmatizing
• Text Summarizing-Word Cloud and Topic modelling

Vectorizing Raw Data:

• Count Vectorizing
• N-gram Vectorizing
• Inverse document frequency weighting

Sentimental Analysis in Jupyter Notebook