Syllabus of Machine Learning-Softpro India

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Syllabus of Machine Learning

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Course Curriculum

S.No	Contents
1.	Introduction to Data Science Deep Learning & Artificial Intelligence Introduction to Deep Learning & AI Deep Learning: A revolution in Artificial Intelligence
2.	What is Deep Learning? Need for Data Scientists Foundation of Data Science What is Business Intelligence? What is Data Analysis? What is Data Mining?
3.	What is Machine Learning? Analytics vs Data Science Value Chain Types of Analytics Lifecycle Probability Analytics Project Lifecycle Advantage of Deep Learning over Machine learning Reasons for Deep Learning Real-Life use cases of Deep Learning Review of Machine Learning
4.	Numpy & Pandas Learning NumPy Introduction to Pandas Creating Data Frames GroupingSorting Plotting Data Creating Functions Slicing/Dicing Operations.
5.	Machine Learning, Deep Learning & AI using Python Introduction ML Fundamentals ML Common Use Cases Understanding Supervised and Unsupervised Learning Techniques
6.	Implementing Association rule mining What is Association Rules & its use cases? What is Recommendation Engine & its working? Recommendation Use-case Case study
7.	Decision Tree Classifier How to build Decision trees What is Classification and its use cases? What is Decision Tree? Algorithm for Decision Tree Induction Creating a Decision Tree Confusion Matrix Case study
8.	Random Forest Classifier What is Random Forests? Features of Random Forest Out of Box Error Estimate and Variable Importance Case study
9.	Linear Regression Case study Introduction to Predictive Modeling Linear Regression Overview Simple Linear Regression Multiple Linear Regression
10.	Logistic Regression Case study Logistic Regression Overview Data Partitioning Univariate Analysis Bivariate Analysis Multicollinearity Analysis Model Building Model Validation Model Performance Assessment AUC & ROC curves Scorecard
11.	Support Vector Machines Case Study Introduction to SVMs SVM History Vectors Overview Decision Surfaces Linear SVMs The Kernel Trick Non-Linear SVMs The Kernel SVM
12.	Machine Learning Algorithms Python Various Machine Learning Algorithms in Python Apply Machine Learning Algorithms in Python Feature Selection and Pre-processing How to Select the right data? Which are the best features to use? Additional feature selection techniques A feature selection case study Preprocessing Preprocessing Scaling Techniques How to preprocess your data? How to scale your data? Feature Scaling Final Project
13.	PySpark and MLLib Introduction to Spark Core Spark Architecture Working with RDDs Introduction to PySpark Machine learning with PySpark – Mllib
14.	Deep Learning & AI Case Study Deep Learning Overview The Brain v/s Neuron Introduction to Deep Learning
15.	Project Work

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Training Platform

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Training Duration

30 Hours