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- Introduction to Various Programming Languages (Tools) for Data Science
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- Introduction to Machine Learning Algorithms and their Implementation in Python
Introduction to Machine Learning Algorithms and their Implementation in Python
Beginner
Online
12 Weeks
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
certificate availability
certificate providing authority
The syllabus
Week 1- Introduction to Data Science and Programming Languages (Tools) for Data Science
Week 2- Basics of Programming
- Variables
- Operators
- Data Types
- Data Structure
- Control Structure in Python
- Function File in Python
Week 3- Essential Python Libraries
- NumPy
- SciPy
- Pandas
- Matplotlib
- Seaborn
Week 4- Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff
- Basics of Machine Learning
- Classification
- Fitting of Model with Cross-Validation
- Bias Variance Tradeoff.
Week 5- Evaluation Metrics
- Evaluation Metrics for Model Validation
Week 6- Importing Data and Hands on Imported Data
- EDA
- Correlation
- Feature Extraction
- Hyper Parameters
Week 7- Univariate and Multivariate Linear Regression
- Univariate Linear Regression
- Multivariate Linear Regression
- Implemented in Python
Week 8- Principal Component Analysis
- Eigen Values
- Eigen Vectors
- Singular Value Decomposition
- PCA
Week 9- Logistic Regression and k-nearest Neighbor
- Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python
Week 10- Decision Tree and Random Forest
- Decision Tree
- Random Forest
Week 11- K-Mean and Hierarchical Clustering
- K-Mean
- Hierarchical Clustering
Week 12- Neural Network
- Logistic Regression with Neural Network Mindset
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