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Machine Learning

Hyderabad  |  Added on 27 Feb, 2018 |  Ad ID: 21204

Views : 6

Description:

Machine Learning Training Pupil should be able to Answer by Day 1: Introduction to Machine Learning (Demo) 1.What is Machine Learning? 2.Problems solved by Machine Learning 3.Limitations of Machine Learning 4.Types of Machine Learning 5.Examples Day 2: Python 1 Basics 1.Python Evolution 2.Where is it used? 3.Why Python? 4.Variable Types in Python 5.Print and input statements 6.Commenting Code 7.And or Not 8.Tuple, Set 9.If loop, Closing Loops 10.Arrays, Range, xrange 11.Dictionary 12.For, while loop 13.Try Except continue 14. Assignment Day 3: Python 2 1.Functions 2.Vectorizing Operators 3.Numpy basics 4.Pandas 5.Other references Day 4: Word Count and Sentiment Analysis 1.Writings through time 2.Google Corpus of documents 3.Simple Word Count from a document 4.Sentiment Analysis 5.Ref: Email Spam Detection, duplicate documents detection, Minhashing 6. Identifying clauses in legal documents, understanding context to give Hash tags etc� Day 5: Supervised Learning: Gradient Descent and Cost Function 1.Role of Analyst, Data Scientist, Data Engineer ,.. 2.Basic Statistics 3.Sampling, confidence interval, null hypothesis 4.R- squared, Adj R squared, p -value 5.Cost Functions : SSE, MLE and others 6.What is Gradient Descent? 7.Partial Derivative 8.Coding in Cost and Gradient Descent in Python 2 Day Buffer Time to complete if inComplete Day 6: Linear Regression, Sigmoid Function and Logistic Regression 1.Linear Regression 2.Sigmoid Function 3.Classification Problem, 4.Logistic Regression Day 7: Regularization 1.Outliers 2.Detection and Treatment 3.Ridge Regression 4.Lasso Regression Day 8: Missing Value Treatment 1.Natural forms in data 2.Replacing Missing Values 3.Tagging Missing data 4.How to Replace Missing Data 5.Using Mean, Mode or Random Values to replace 6.Using Regression to complete missing data Day 9: Importance of Visualization 1.Classic problems in using Summary Statistics 2.Regression Line output 3.Gestalt Principles 4.Finding relations with data 5.Making Features 6.Importance of Domain Knowledge 7.Examples of Good Visualizations 8.Using MatPlotlib Day 10: Trees and Random Forests 1.Information Value 2.Weight of Evidence, Coarse Classing and Fine Classing 3.Importance in Banking 4.Other Building Blocks for a Tree 5.CART and CHAID 6.Random Forest 7.Others Gradient Boosting , XGBoost and AdaBoost 2 Day Buffer Time to complete if inComplete Day 11: Neural Networks 1.Evolution of Neural Networks 2.Feed Forward Algorithm 3.Back Propagation Algorithm 4.Basic Neural Network Day 12: UnSupervised Machine Learning 1.Distance Function 2.Clustering 3.Centroids and KNN 4.Other Methods K-Medoids, Hierarchial Clustering etc.. Day 13: Anomaly Detection 1.Importance of Anomaly Detection 2.Practical Cases 3.Importance of Machine Learning in Anomaly Detection Day 14: Dimensionality Reduction 1.Why reduce variables? 2.Importance and Methods to reduce features 3.How is Coarse/Fine Classing used to reduce Features 4.Principal Component Analysis 5.Singular Value Decomposition 6.Non-Negative Matrix Factorization 7.Importance while Visualization Day 15: Dimensionality Reduction 1. Exploring the code for Non-Negative Matrix Factorization Regards, Swarna IT Solutions Online Training & Support Online Training | Corporate Training | Job Supportwww.swarnaitsolutions.com | enquiry@swarnaitsolutions.com For Escalations :- +91- 8106456699 ; 040 29701747

 

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