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Machine Learning Made Easy Beginner To Expert Using Python




Python and Machine Learning IT Training


Working Professionals and Freshers


Regular Offline and Online Live Training


Week Days and Week Ends

Duration :

60 Days

Python and Machine Learning What will you learn?

•How to apply Python and Machine Learning Script.
•What are the advantages of Python and Machine Learning?
•Learn how to develop, build and deploy Python and Machine Learning
•How to perform read and write operations in Python and Machine Learning.
•Learn from scratch how to execute code with Python and Machine Learning
•Learn all the relevant skills needed to use Python and Machine Learning efficiently
•Learn the Ins and Outs of Python and Machine Learning in few Hours
•Learn Python and Machine Learning from Scratch and Achieve Highest Knowledge with Practical Examples
•This course is designed for any graduates as well as Software Professionals who are willing to learn Python and Machine Learning.

machine learning made easy beginner to expert using python Course Features

•Career guidance providing by It Expert
•Course has been framed by Industry experts
•Software & others tools installation Guidance
•Create hands-on projects at the end of the course
•We provide Classroom and Online training in Metro Cities
•Courseware that is curated to meet the global requirements
•Flexible group timings to admit freshers, students, and employed professionals
•The course is all about familiarizing the trainees with simpler and smarter ways to develop the skills required for Implementation.

Who are eligible for Python and Machine Learning

•c#, .net Technologies, java, J2ee, c++, director, vp, architect, Senior Architect, Sde1, Sde3, Engineering Manager, Python Developer
•delphi, wpf, Oracle Forms, wlan, wifi, wimax, nms, ems, oss, Big Data, hadoop, dpi, snmp, c, Cloud Computing, Vlsi, Data Structure, Algorithm
•Java/J2EE, Springs, API, REST/, MySQL, Java, Admin UI developer with HTML/JavaScript/Ember.js, Java Enterprise Integration/ESB/API Management experts with Mule
•React.js, core, .net Core, React Native, Front End Developers, .Net Developers, .Net Tech Leads
•Software Development, Big Data, Hadoop, Spark, Hive, Oozie, Big Data Analytics, Java, Python, R, Cloud, Data Quality, Scala, Nosql, Sql Database, Core Java


Python and It’s IDE
•Basic Commands in Python
•Objects, Numbers and Strings
•Objects, List, Tuples & Dictionaries
•If, Else & Loop
•Functions and Packages
•Important Packages
•End Note
•Data Handling in Python
•Introduciton to DataHandling
•Basic Commands and Checklist
•Subsetting the Dataset
•Calculated Field Sort Duplicates
•Merge and Exporting
•Data Handeling Quiz
•Descriptive Statistics Plots
•Basic Statistics and Sampling
•Discriptive Statistics
•Percentile and Boxplot
•Graphs Plots and Conclusion
•Descriptive Statistics Plots Quiz
•Data Cleaning and Treatement
•Data cleaning Introduction and Model Building Cycle
•Model Building Cycle
•Data Cleaning Case Study
•LAB – Step1 Basic Content of Dataset
•Variable Level Exploration Catagorical
•Reading Data Dictionary
•LAB – Step2 Catagorical Variable Exploration
•Step3 Variable Level Exploration – continuous
•LAB – Step3 Variable Level Exploration – continuous
•Data Cleaning and Treatments
•Step4 Treatment – scenario1
•LAB – Step4 Treatment – scenario1
•Step4 Treatment – scenario2
•LAB – step4 Treatment – scenario2
•Data Cleaning scenario 3
•LAB – Data Cleaning scenario 3
•Some Other variables
•Linear Regression
•LAB_ Correlation
•Beyond Pearson Correlation
•From Correlation to Regression
•Regression _ LAB
•How Good is My Line
•R Squared
•Multiple Regression Model
•Adjusted R Squared
•Multiple Regression Issues
•Multicolinearity LAB
•Linear Regression Quiz
•Logistic Regression
•A Logistic function
•Building a Logistic Regression Line in Python
•Multiple Logistic Regression Model
•Goodness of fit Logistic Regression
•Multicollinearity in Logistic Regression
•Individual Impact of Variables
•Model Selection
•Logistic Regression Quiz
•Decision Trees
•The Decision Tree Philosophy & The Decision Tree Approach
•The Splitting criterion & Entropy Calculation
•Information Gain & Calculation
•The Decision Tree Algorithm
•Many Splits for a Variable
•Decision Tree Fitting and Interpretation
•Decision Tree Validation
•Decision Tree Overfitting
•Pruning and Pruning Parameters
•Tree Building & Model Selection-Lab1
•Tree Building & Model Selection-Lab2
•Decision Tree Quiz
•Model Selection and Cross Validation
•Sensitivity Specificity
•LAB – Sensitivity and Specificity in Python
•Sensitivity Specificity Contd p.1
•Sensitivity Specificit Contd p.2
•The best model
•The best Model Lab
•Overfitting Underfitting p.1
•Overfitting Underfitting p.2
•Overfitting Underfitting p.3
•Overfitting Underfitting p.4
•Bias-Variance Treadoff
•Holdout data Validation
•LAB Holdout data Validation
•Ten fold CV
•Ten fold CV LAB
•Boot Strap Cross Validation
•LAB – Boot Strap Cross Validation
•MSCV Conclusion
•MSCV Quiz
•Neural Networks
•Neural Networks Introduction
•Logistic Regression Recap LAB
•Decision Boundry – Logistic Regression
•Decision Boundry – LAB
•New Representation for Logistic Regression
•Non Linear Decision Boundry – Problem
•Non Linear Decision Boundry – Solution
•Intermediate Output LAB
•Neural Network Intution
•Neural Network Algorithm
•Demo Neural Network Algorithm
•Neural Network LAB
•Local Minima and Number of Hidden Layers
•Digit Recogniser Lab
•Neural Network Quiz
•The Classifier and Decision Boundary P.1
•The Classifier and Decision Boundary LAB
•SVM-The Large Margin Classifier
•The SVM Algo and Results
•SVM in Python
•Non Linear Boundary
•Kernal Trick
•Kernal Trick in Python
•Soft Margin and Validataion
•SVM Advantages Disadvantages and Applications
•Lab Digit Recognizer
•SVM Conclusion
•SVM Quiz
•Random Forest and Boosting
•Wisdom of Crowd
•Ensemble Learning
•Ensemble Models
•Random Forest
•LAB Random Forests in Python
•Boosting Illustration
•LAB Boosting in Python
•RF & Boosting Quiz