Python Programming + Machine Learning Combo

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  • Start Date: Oct. 10, 2020, 11:52 p.m.
  • End Date: Oct. 25, 2020, 11:53 p.m.
  • Venue: This is a virtual event. The link to join the event will be shared with you.
₹ 999
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
Machine Learning Module 1.
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
Module 2
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
Module 3
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