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MACHINE LEARNING DATA SCIENCE MASTERCLASS

Machine Learning Online Training Institute

Job Aspirants

Online and Classroom Sessions

Week Days and Week Ends

45 Days

•How to apply Machine Learning Script.

•You will learn how to write Machine Learning.

•How to write clean production-ready code using Machine Learning.

•How to store and handle file upload in Machine Learning.

•Learn from the basic and core guide to Machine Learning

•Learn all the relevant skills needed to use Machine Learning efficiently

•An easy way to learn one of the widely used Machine Learning

•Learn Machine Learning – A super fun way to improve your programming skills

•Learn how to code in Machine Learning This Machine Learning Course is set up for complete beginners!

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•Post training offline support available

•25+ projects for good Learning experience

•Real time live project training and Guidance

•Create hands-on projects at the end of the course

•Interview guidance and preparation study materials.

• Finessing your tech skills and help break into the IT field

•One-on-one training, online training, team or Corporate training can be provided

• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

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•c++, React.js, Java Fullstack, Core Java Data Structure, Java Micro-services, Devops, Microsoft Azure, Cloud Computing, Machine Learning, Automation Testing

•Front End, Javascript, Computer Graphics, Html, Css, Problem Solving, CSS, Web Technologies, Design, Software Development, Full Stack Developer

•Ms Crm, Guidewire, Sdm, Sde2, Qae, Sdet, Jbpm, Ext Js, Windows Admin, Full Stack, Aem, Spark, Hadoop, Big Data, Data Engineer, Azure, Cloud, Opentextphp, wordpress, drupal, Iphone Developer, Android, Java, Team Management, Android Developer, Mobile Application Development

•Xml Publisher, Php Developer, Android Application Development, Html Tagging, E-publishing, Software Development

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Why Machine Learning?

•Who am I? How Is The Course Structured?

•Python Or R?

•Download Required Materials

•Setting Up The Python Environment

•Installing Required Tools

•Crash Course: Our Jupyter-Environment

•How To Find The Right File In The Course Materials

•Setting Up The R Environment

•Installing R And RStudio

•Crash Course: R and RStudio

•Intro: Vectores in R

•Intro: data.table In R

•Basics Machine-Learning

•What’s A Model?

•Which Problems Is Machine Learning Used For

•Linear Regression

•Intuiton: Linear Regression (Part 1)

•Intuition: Linear Regression (Part 2)

•Intuition Comprehend With Geogebra

•Quiz 1: Check: Linear Regression

•Python: Read Data And Draw Graphic

•Note: Excel

•Python: Linear Regression (Part 1)

•Python: Linear Regression (Part 2)

•R: Linear Regression (Part 1)

•R: Linear Regression (Part 2)

•R: Linear Regression (Part 3)

•R: Linear Regression (Part 4)

•Excursus (optional): Why Do We Use The Quadratic Error?

•Project: Linear Regression

•Intro: Project Linear Regression (Used Car Sales)

•Project Linear Regression

•1 question

•Python: Sample Solution

•R: Sample Solution

•Train/Test

•Intuition: Train / Test

•Check: Train / Test

•Python: Train / Test (Part 1)

•Python: Train / Test (Part 2)

•Python: Train / Test – Challenge

•R: Train / Test (Part 1)

•R: Train / Test (Part 2)

•R: Train / Test – Challenge

•Linear Regression With Multiple Variables

•Intuition: Linear regression with multiple variables (Part 1)

•Intuition: Linear regression with multiple variables (Part 2)

•Check: Linear regression with multiple variables

•Python: Linear regression with multiple variables (Part 1)

•Python: Linear regression with multiple variables (Part 2)

•R: Linear regression with multiple variables (Part 1 + 2)

•Compare models: coefficient of determination

•Intuition: R² – The coefficient of determination (Part 1)

•Intuition: R² – The coefficient of determination (Part 2)

•Check: R² / coefficient of determination

•Python: Calculate R²

•Python: Compare models by R²

•R: Calculate R²

•R: Compare models by R²

•Practical project: Coefficient of Determination

•Introduction: Practical project: coefficient of determination

•Note: Where can you find the project?

•Python, practical project: Calculate coefficient of determination

•R, Praxisprojekt: Bestimmtheitsmaß berechnen

•Concept: Types of data and how to process them

•Intuition: Data Types (Part 1) – What Types Are There?

•Intuition: Data Types (Part 2) – Metric & Nominal Data

•Intuition: Data Types (Part 3) – Ordinal Data

•Python: Processing Nominal Data (Part 1, Preparing Data)

•Check your solution!

•Python: Processing Nominal Data (Part 2)

•R: Process nominal data (Part 1 + 2)

•Optional excursus: Why were we allowed to remove a column?

•Polynomiale Regression

•Intuition: Polynomial Regression (Part 1)

•Intuition: Polynomial Regression (Part 2)

•Python: Polynomial Regression (Part 1)

•Python: Polynomial Regression (Part 2)

•R: Polynomial Regression (Part 1)

•Practice Project: Polynomial Regression

•Presentation: Practice Project Polynomial Regression

•Python: Sample Solution: Project Polynomial Regression

•R: Sample Solution: Project Polynomial Regression

•Excursus R: Vectorize calculations in R (matrices, …)

•R: Vectors and matrices

•R: Access elements in vectors

•R: Naming of elements

•R: Matrices

•R: Name matrices

•R: DataTables

•Excursus Python: Vectorize Calculations (Numpy)

•Excursus Python: Why Numpy? (Part 1)

•Excursus Python: Why Numpy? (Part 2)

•Excursus Python: Numpy (Arrays)

•Excursus Python: Numpy (Arrays – Application)

•Excursus Python: Numpy (Matrices)

•Excursus Python: The np.where() function

•More stable test results with K-Fold Cross-Validation

•Intuition: K-Fold Cross-Validation

•K-Fold Cross-Validation

•Python: K-Fold Cross Validation (Part 1)

•Python: K-Fold Cross Validation (Part 2)

•Python: K-Fold Cross Validation (Part 3)

•R: K-Fold Cross Validation (Part 1-3)

•Intuition: Repeated K-Fold Cross-Validation

•Repeated K-Fold Cross-Validation

•Python: Repeated K-Fold Cross-Validation

•R: Repeated K-Fold Cross-Validation

•Practical project: K-Fold Cross-Validation

•Task: K-Fold Cross-Validation

•Python: Sample Solution K-Fold Cross-Validation

•R: Sample Solution K-Fold Cross-Validation

•Statistics basics

•Why do we need statistics basics?

•Intuition: mean vs. median

•Mean value and median

•Python: Calculate mean value & median

•R: Calculate mean value & median

•Intuition: Sample

•Intuition: variance and standard deviation

•Variance and standard deviation

•Expert Knowledge (Optional): Corrected Sample Variance

•Python: Draw Histograms

•R: Draw Histograms

•Project: Statistics basics

•Introduction: Practice project “Statistics Basics”

•Python Excursus: Open and filter data

•R Excursus: Open and filter data

•Python: Sample Solution Project “Statistics Basics”

•R: Sample Solution Project “Statistical Foundations

•Classification

•Intuition: What is classification?

•Presentation: Our example data

•Logistic Regression

•Intuition: Logistic Regression

•Intuition: Logistic Regression (Error Term)

•Python: Display data

•Python: Scale data

•Python: Predict data

•Python: Visualize decision boundary

•Python: Visualize decision boundary (smooth transition)

•Python (optional): How is decision limit visualized? (Part 1)

•Python (optional): How is decision limit visualized? (Part 2)

•Python: Your Classification Template

•R: Display data

•R: Scale data

•R: Visualize decision boundary

•R: Visualize decision boundary (smooth transition)

•R (optional): How is the decision limit visualized?

•R: Calculate accuracy

•R: Your Classification Template

•Practice Project: Detect Breast Cancer

•Python: Task breast cancer project

•Python: Sample solution breast cancer project

•R: Task breast cancer project

•R: Sample solution breast cancer project

•Classification with Several Classes

•Intuition: One-Vs-All, One-Vs-One

•One-Vs-All, One-Vs-One

•Python: One-Vs-All, One-Vs-One

•R: One-Vs-All

•Intuition: Multinomial Logistic Regression

•Python: Multinomial Logistic Regression

•R: Multinomial Logistic Regression

•K-Nearest-Neighbor (KNN)

•Intuition: KNN

•KNN

•Python: KNN

•Python: KNN (effects of k)

•R: KNN

•R: KNN (effects of k)

•R: KNN (Tip: The predict function)

•Practical project: Classifying iris blossom leaves

•Project: Iris (Introduction)

•Python: Task Project “Iris”

•Python: Sample solution “Iris” project

•R: Task Project “Iris”

•R: Sample solution “Iris” project

•Decision Trees

•Intuition: Entropy

•Entropy

•Intuition: Decision Trees

•Further information: Entropy

•Python: Decision Trees

•Python: Visualizing Decision Trees (Part 1)

•Python: Visualizing Decision Trees (Part 2)

•Python: Restricting Decision Trees

•Python: Export Decision Trees

•R: Decision trees

•R: Visualize decision trees (Part 1)

•R: Visualize decision trees (Part 2)

•R: Decision trees (the predict() function)

•R: Restrict decision trees

•R: Export decision trees

•Practical project: Classifying mushrooms

•Task: Classify project mushrooms

•Python: Solutions

•R: Solutions

•Random Forests

•Intuition: Random Forest

•Random Forest

•Python: Random Forest

•R: Random Forest

•Task: RandomForest

•The Bias/Variance Dilemma

•Intuition: Training vs. testing terror

•Intuition: Bias vs. Varianz

•Bias vs. Varianz

•Intuition: Comparison of models with high bias or high variance

•Intuition: Validation curve

•Python: Validation curve

•Python: Task Vali

Freetrainings is a training powerhouse offering trainings on varied IT Technologies, Technology services on IT technologies, As an Independent Verification & Validation service provider.

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