Machine Learning With Python

Machine Learning With Python

Instructor
Will be announced soon...

Course Description

Introduction:
  • Introduction to Data Science and Machine Learning
  • Examples & Techniques of Machine Learning
  • Real Life Industry Based ML Problem Statements
Python:
  • Python Installation Guidelines
  • How to use Jupyter Notebook
  • Introduction to Python
  • Python programs
  • Python Data Structures-List
  • Python Data Structures-Dictionary
  • Python Data Structures-Sets
  • Python Data Structures-Strings
  • Python Data Structures-Tuples
  • Python Numpy
  • Python Pandas-Data Frames
  • Python Pandas-Series
  • Python Pandas-Quick-tips
Statistics:
  • Basics of Statistics
  • Central Tendency
  • Covariance
  • Correlation
  • Standard Deviation
  • Z-Score
Data Processing:
  • Data processing techniques in Python
  • Matplotlib Python visualization library
  • Box Plots
  • Histograms
  • Label Encoding
  • One Hot Encoding
  • Training and Testing data

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

Course Registration

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