Machine Learning

H2kinfosys of offering  Machine Learning – Python and R Hands-On

Why H2K Infosys?

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    100% job oriented training

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    Instructor-led face-to-face live training

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    Huge course syllabus on latest technologies

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    Real-time projects to gain hands-on experience

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    Cloud Test Lab to practice on software tools and projects

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    Lifetime access to recorded class videos

About Course

  • This course gives you a good insight into the Machine Learning world of Data Science. It helps you understand topics like data preprocessing, different types regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, dimensionality reduction, model selection & boosting. There are sufficient hands on exercises and assignments covered in the course to help you feel confident in succeeding in your career as a Data scientist.

  • • I.T. career aspirants who wish to kickstart their career in one of the hottest technologies in the market 

    • Architects, software administrators, developers, testers who want to build an effective data processing application or perform big data analysis

    • ETL developers who wish to add an additional technology in their skillset on data management

Course Syllabus

  • • Introduction

    • Data Preprocessing

    • Regression

    • Simple Linear Regression

    • Multiple Linear Regression

    • Polynomial Regression

    • Support Vector Regression(SVR)

    • Decision Tree Regression

    • Random Forest Regression

    • Evaluating Regression Models Performance

    • Classification

    • Logistic Regression                    

    • K-Nearest Neighbors

    • Support Vector Machine(SVM)

    • Kernel SVM

    • Naive Bayes

    • Decision Tree Classification

    • Random Forest Classification

    • Evaluating Classification Models Performance

    • Clustering

    • K-Means Clustering

    • Hierarchical Clustering

    • Association Rule Learning

    • Apriori

    • Eclat

    • Reinforcement Learning

    • Upper Confidence Bound

    • Thompson Sampling

    • Natural Language Processing

    • Deep Learning

    • Artificial Neural Networks

    • Convolutional Neural Networks

    • Dimensionality Reduction

    • Principal Component Analysis (PCA)

    • Linear Discriminant Analysis (LDA)

    • Kernel PCA

    • Model Selection & Boosting

    • Model Selection

    • XGBoost

Interview Questions

Interview questions not provied yet

Sample Resumes

Sample resumes not provied yet

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