Contents of Education

This course facilitates classroom learning, letting students develop competence and confidence in using Python , R , NodeJs and other languages to develop models This course addresses beginner-level and medium-level skills in Machine Learning Algorithms.

Maximum number of attendees are limited with 7 person.

- What is Machine Learning,                       - Grid Search

- Tools :  Python, R , NodeJS                      - Cross Validation

- Libraries : Sckit, Pandas, Numpy              - Confusion Matrix

                                                                    - Precision, ReCall, Accuracy, f1-score 

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- Supervised Learning                                 - Bagging

- UnSupervised Learning                            - Stacking

- SemiSupervised Learning                         - Boosting  

- ReInforced Learning                                 - Custom Stacking (BA)

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 - Linear regression,                                    - Clustering

 - Estimator bias and variance,                   - K-means

- Cost function                                             - Optimization objective

- Active Laerning                                         - Choosing Number Of Cluster

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- Neurons and the Brain                              - Linear Regression

- Binary/Multi Classification                         - Artificial Neural Network

- Backpropagation Algorithm

- Random Initialization

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- Decision TreeAlgorithms                           - Decision Tree

- Constructing Tree                                     - Random Forest

- Post-Prune, Pre-Prune                              - Model Combination

- Random Forest Algorithm

- Building Forest using Decision Tree

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- Regularization                                           - K-Means Clustering

- Parallel Processing                                   - Develop a usecase with Company Sample Data

- Handling Missing Values                           - Bonus - Tensorflow Intro

- Built-in Cross-Validation

- Continue on Existing Model

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- Understanding of 6 Machine Learning algorihtms

- Create usecase from business requirements

- Develop models based on data analysis

- Using metrics to compare models

- Deploy models to production

Education Who Should Attend

This course is aimed at participants who wish to gain an industry qualification in the Machine Learning Algorithms and use cases.

Candidates should have an understanding of the basic statistics, any software development language, SQLand intro level PYTON knowledge. 

Gains

This series facilitates classroom learning, letting students develop competence and confidence in using Python , R , NodeJs and other languages to develop models.

If you want to get detail info, send email to [email protected]

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