Project management methodology(CRISP-ML (Q))
- 1. Sanjivani Rural Education Society’s Sanjivani College of Engineering, Kopargaon-423 603 (An Autonomous Institute, Affiliated to Savitribai Phule Pune University, Pune) NACC ‘A’ Grade Accredited, ISO 9001:2015 Certified Department of Computer Engineering (NBA Accredited) Prof. S. A. Shivarkar Assistant Professor Contact No.8275032712 Email- shivarkarsandipcomp@sanjivani.org.in Subject- Unsupervised Modeling for AIML (CO9301) Subject- Unsupervised Modeling for AIML (CO9301)
- 2. Content Content Lectures: 4 Hrs/Week Credits: 4 Examination Scheme: CIA: 40 End Semester: 60
- 3. Course Outcome Course Outcome Understand project management methodology and Exploratory data analysis. Apply feature engineering techniques. Apply clustering techniques. Apply dimensionality reduction techniques Apply association rules and recommendation system Tecniques. Apply text mining and NLP Techniques.
- 4. Course Objective Course Objective To learn CRISP-ML(Q) method of machine learning models To understand Clustering, dimensionality reduction To learn Association rules and recommendation system To understand various NLP strategies To learn how to evaluate the models and performance metrics
- 5. Unit I: Requirement to Machine Learning Unit I: Requirement to Machine Learning Project management methodology(CRISP-ML (Q)),Prescriptive Analytics, Predictive Analytics, Diagnostic Analytics, Descriptive Analytics, introduction of data types, measurement levels, measure of central tendency, expected value ,Explorative data analysis, number summary, boxplot, bargraph, Histogram, correlation graph, scatter plots ,exploring two or more variables,Data sampling and its types,various types bias.
- 6. Unit II: Feature Engineering Techniques Unit II: Feature Engineering Techniques Dummy variables conversion techniques Standardization and normalization, outlier identification and outlier treatment techniques, skewness identification and its treatment. Finding null values and its treatment.
- 7. Unit III: Unsupervised Learning-Clustering Unit III: Unsupervised Learning-Clustering Supervised Vs Unsupervised learning, clustering/segmentation algorithms-Hierarchical, Distance metrics for categorical data, Distance metrics for continuous ,distance metrics for mixed data, distance for clusters, k-means clustering, k selection- elbow curve, drawbacks and comparison
- 8. Unit IV: Unsupervised Learning -Dimensionality Reduction Unit IV: Unsupervised Learning -Dimensionality Reduction Need for dimensionality reduction, Principal component analysis(PCA),applications for PCA, Singular Value Decomposition(SVD),application of SVD
- 9. Unit V: Unsupervised Learning - Unit V: Unsupervised Learning -Association rules and Association rules and recommendation system recommendation system Market basket analysis,Association rules intuition,Association rules applications ,Association rules terminology, need for recommendation systems,similaritymeasures,user based recommendation system,item to item collaborative filtering.
- 10. Unit VI: Unit VI: Text Mining-Sentiment Analysis and NLP Text Mining-Sentiment Analysis and NLP Need of text mining, Bag of words, terminology and preprocessing,DTM and TDM,corpus level word cloud. Introduction of NLP,data preprocessing in NLP context ,NLP terminology ,feature extraction from text,topic modeling, vector representation
- 11. Unit I: Requirement to Machine Learning Unit I: Requirement to Machine Learning Project management methodology(CRISP-ML (Q)),Prescriptive Analytics, Predictive Analytics, Diagnostic Analytics, Descriptive Analytics, introduction of data types, measurement levels, measure of central tendency, expected value ,Explorative data analysis, number summary, boxplot, bargraph, Histogram, correlation graph, scatter plots ,exploring two or more variables,Data sampling and its types,various types bias.
- 12. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Overall, the CRISP-ML(Q) process model describes six phases: 1. Business and Data Understanding 2. Data Engineering (Data Preparation) 3. Machine Learning Model Engineering 4. Quality Assurance for Machine Learning Applications 5. Deployment 6. Monitoring and Maintenance.
- 13. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q))
- 14. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q))
- 15. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Business and Data Understanding: Developing machine learning applications starts with identifying the scope of the ML application, the success criteria, and a data quality verification. The goal of this first phase is to ensure the feasibility of the project. Defining clear and measurable Key Performance Indicators (KPI) such as “time savings per user and session” is required.
- 16. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Machine Learning Model Engineering The modeling phase includes model selection, model specialization, and model training tasks. Additionally, depending on the application, we might use a pre-trained model, compress the model, or apply ensemble learning methods to get the final ML model. Many phases in ML development are iterative. Sometimes, we might need to review the business goals, KPIs, and available data from the previous steps to adjust the outcomes of the ML model results. Finally, we package the ML workflow in a pipeline to create repeatable model training during the modeling phase.
- 17. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Evaluating Machine Learning Models Model training is followed by a model evaluation phase, also known as offline testing. During this phase, the performance of the trained model needs to be validated on a test set. Additionally, the model robustness should be assessed using noisy or wrong input data. Finally, the model deployment decision should be met automatically based on success criteria or manually by domain and ML experts. Similar to the modeling phase, all outcomes of the evaluation phase need to be documented. Deployment: a process of the ML model integration into the existing software system. Monitoring and Maintenance https://ml-ops.org/content/crisp-ml
- 18. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Deployment: The ML model deployment denotes a process of the ML model integration into the existing software system. After succeeding in the evaluation step in the ML development life cycle, the ML model is graduated to be deployed in the (pre-) production environment. The ML model deployment includes the following tasks: inference hardware definition, model evaluation in a production environment (online testing, e.g., A/B tests), providing user acceptance and usability testing, providing a fall- back plan for model outages, and setting up the deployment strategy to roll out the new model gradually (e.g. canary or green/blue deployment).
- 19. Project management methodology(CRISP-ML (Q)) Project management methodology(CRISP-ML (Q)) Monitoring and Maintenance Once the ML model has been put into production, it is essential to monitor its performance and maintain it. When an ML model performs on real-world data, the main risk is the “model staleness” effect when the performance of the ML model drops as it starts operating on unseen data. Furthermore, model performance is affected by hardware performance and the existing software stack. Therefore, the best practice to prevent the model performance drop is to perform the monitoring task when the model performance is continuously evaluated to decide whether the model needs to be re-trained. This is known as the Continued Model Evaluation pattern. The decision from the monitoring task leads to the second task - updating the ML model.
- 20. DEPARTMENT OF COMPUTER ENGINEERING, Sanjivani COE, Kopargaon 20 Reference Reference https://ml-ops.org/content/crisp-ml