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Machine Learning With Python And Scikit Learn 3 In 1




Python and Machine Learning Software Training


Technology Learners


Both Classroom and Online Classes


Week Days and Week Ends

Duration :

Daily 2 hrs during Weekdays

Python and Machine Learning Objectives

•Troubleshoot advanced models in Python and Machine Learning.
•What are the advantages of Python and Machine Learning?
•Become a professional Python and Machine Learning Engineer by learning Python and Machine LearningHow to apply the Python and Machine Learning rules in different situations.
•Learn the Basic Concepts of Python and Machine Learning with Practical Examples
•Learn all the relevant skills needed to use Python and Machine Learning efficiently
•Beginner to Advance Level: Learn to Plan, Design and Implement Python and Machine LearningDiscover how to correctly test instance identity as well as equality in Python and Machine Learning.
•Learn Python and Machine Learning from basic to advanced with examples and interactive sessions at peak.

machine learning with python and scikit learn 3 in 1 Training Highlights

•Get job-ready for an in-demand career
•Exercises and handouts after every session
•Job Placement Assistance with Good Companies
•Personal attention and guidance for every student
•Indutry oriented training with corporate casestudies
•We also provide Cost Effective and Flexible Payment Schemes
•Live project based on any of the selected use cases, involving implementation of the concepts
• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

Who are eligible for Python and Machine Learning

•.Net Developer, PL SQL developer, UI Designer, Data Analyst, Business Analyst
•ETL Developer, Informatica MDM, SAP BO, SAP HANA, Oracle Apps Functional Finance, Finance Modules, 11i, R12, Oracle Apps, Oracle Apps DBA, EBusiness Suite
•Java Developer, Salesforce Developer, Solution Consulting, Qa Testing, Finance Executive, Full Stack Developer, Email Campaign, React.js, Ui Development
•OBIEE, Oracle Fusion Middleware, Oracle Database, Oracle apps DBA, Oracle core DBA, Apex Developer, Java/J2EE developer, Data Architect, Orcale Fusion
•webdesigner, informatica, datastage, teradata, mircostrategy, Sap Abap, QA Tester, Green hat tester, salesforce, developer, tibco, Hadoop


Fundamentals of Machine Learning with scikit-learn
•The Course Overview
•Machine Types and Learning Methods
•Data Formats
•Statistical Learning Approaches
•Elements of Information Theory
•Splitting Datasets
•Managing Data
•Data Scaling and Normalization
•Principal Component Analysis
•Linear Models and Its Example
•Linear Regression with scikit-learn
•Ridge, Lasso, and ElasticNet
•Regression Types
•Logistic Regression
•Stochastic Gradient Descent Algorithms
•Finding the Optimal Hyperparameters
•Classification Metrics
•ROC Curve
•Bayes’ Theorem
•Naive Bayes’ in scikit-learn
•scikit-learn Implementation
•Controlled Support Vector Machines
•Binary Decision Trees
•Decision Tree Classification with scikit-learn
•Ensemble Learning
•Clustering Basics
•DBSCAN and Spectral Clustering
•Evaluation Methods Based on the Ground Truth
•Agglomerative Clustering
•Implementing Agglomerative Clustering
•Connectivity Constraints
•User-Based Systems
•Content-Based Systems
•Hands-On Machine Learning with Python and scikit-Learn
•Demo Machine Learning Product
•Setting Up Our Anaconda Environment
•Launching an iPython Notebook
•Loading and Manipulating Data with Pandas
•ML Objective + Data Splitting and Common Pitfalls
•Descriptive Analytics With Pandas
•Planning Our Preprocessing Stages
•Handling Categorical Data
•Imputing Missing Values
•Handling Outliers
•Feature Extraction
•Feature Selection
•Pipelining Transformers
•Bias/Variance Trade-Off, Overfitting, and Underfitting
•Cross Validation
•Scoring Metrics
•Developing Model Baselines
•Hyper-Parameters and Strategic Search Ranges
•The Importance of Cross Validation in Grid Searches
•“Model Wars” Using Grid Searches
•Final Model Selection and Exposure to Holdout Set
•Model Selection – Where Do We Go Now?
•A Brief Note on Persistence and Version Perils
•Deploying ML Applications Behind a RESTful Endpoint Using Flask
•Learn Machine Learning in 3 Hours
•Operation of an Unsupervised Machine Learning Algorithm
•Operation of a Supervised Machine Learning Algorithm
•Avoid Overfitting and Splitting Data into Training and Testing Sets
•Data Cleaning, Conversion, and Preprocessing
•Using PCA to Easily Explore and Visualize Data
•What Does the Unsupervised K-Means Clustering Algorithm Do?
•Example Problem
•Data Preparation and Processing
•Implementing K-Means Clustering
•Improving Performance and Hyperparameter Fitting
•Operation of the K-Nearest-Neighbor Classification Algorithm
•Implementing K-Nearest-Neighbor Classification
•Operation of the Support Vector Machine Classification Algorithm
•Implementing Support Vector Machine Classification
•Operation of the Support Vector Machine Regression Algorithm
•Implementing Support Vector Machine Regression
•Operation of the Gradient Boosting Algorithm