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Complete Machine Learning Data Science With R Programming

Course

COMPLETE MACHINE LEARNING DATA SCIENCE WITH R PROGRAMMING

Category

Machine Learning Tech Training

Eligibility

Job Aspirants

Mode

Regular Offline and Online Live Training

Batches

Week Days and Week Ends

Duration :

2 Months

Machine Learning Objectives

•Learn to write Machine LearningFunctions.
•How to apply Machine Learning in multiple Projects.
•Learn Everything you need to know about Machine Learning!
•How to connect to multiple data sources with Machine Learning.
•Learn Machine Learning from scratch & understand core programming concept
•Learn how to get a Job as a Machine Learning developer .
•Learn Basic and Advanced Machine Learning Programming and become a Machine Learning Developer
•How to setup a Machine Learning script and Interface in real time development.
•Get to know tips and tricks to work more quickly and effectively in Machine Learning.

complete machine learning data science with r programming Course Highlights

•Additional Sessions for Doubt Clearing
•Course has been framed by Industry experts
•Get Certified at the Best Training Institute.
•Personal attention and guidance for every student
•We hire Top Technical Trainers for Quality Sessions
•Access to a huge closet containing information about Hadoop
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•The course is all about familiarizing the trainees with simpler and smarter ways to develop the skills required for Implementation.

Who are eligible for Machine Learning

•Artificial Intelligence, Data Science, Block Chain, Iot, Cloud Computing, Ux Design, Mobile Application Development, Natural Language Processing, Business
•Hyperion Developer, Hyperion Essbase, OBIEE, Oracle BI, Oracle Business Intelligence, Data Warehouse, Oracle Apps Funcitional, Oracle Apps HRMS Technical
•java, .net, php, Software Testing, Automation Testing, oracle, sap, msbi, tableau, networking, Linux Administration, storage, full stack developer, devops
•php, wordpress, drupal, Iphone Developer, Android, Java, Team Management, Android Developer, Mobile Application Development
•Software Engineer, Business Operational Analyst, Project Manager, Software Test Engineer, Android Developers, HTML5 Developers, IT Help Desk, IT Freshers

COMPLETE MACHINE LEARNING DATA SCIENCE WITH R PROGRAMMING Syllabus

Complete machine learning & data science course Introduction
•How to get help for Machine learning & Data science
•Data science & machine learning as career option
•How to make right decisions for your career in data science & machine learning
•Various Job options for aspiring data scientists & machine learning engineers
•AI Vs ML Vs DL with Types of machine learning
•Job hunting strategy
•Strategy 1 with tips on resume/cv building
•Strategy 2 to target job avenues to get more calls & offers
•Hands-on R programming for machine learning & data science
•R Introduction with installation of rstudio
•Vectors, Matrix & Data frame
•Data types in R
•Variables & Objects in R
•Comments & Vectors in R
•Data wrangling with R-Part 1
•Data wrangling with R-Part 2
•Operators in R-Part 1
•Operators in R-Part 2
•Loops in R
•If Else conditional blocks in R
•Functions in R
•Assignment for R Programming fundamentals
•Machine learning fundamentals
•Reading various kind of files with R
•Data pre-processing introduction- selection & manipulation
•Data selection & manipulation-Rows & Columns
•Data selection & manipulation with Dplyr- Part 1
•Data selection & manipulation with Dplyr- Part 2
•Data selection & manipulation with Subset & Merge
•Data selection & manipulation-Handling missing data
•Data manipulation & selection assignment
•Machine learning fundamentals Quiz
•Data visualization with R
•Data visualization with R- introduction
•Histogram vs bar plot with plotting missing values
•Bar plots and Histograms with R
•Horizontal bar plots and Plot function
•More on Plot function with heat map
•Boxplot with Pair & Par commands
•Line graphs and Maps
•GGPlot 2 Introduction
•Data visualization with GGPlot2
•Lattice and Scatter3d plot libraries
•Assignment
•Data Visualization Quiz
•Applied Statistics for Machine learning
•Introduction to applied statistics with Variables and Sample Size
•Descriptive vs Inferential analysis
•Mean, Median, Mode and Range
•Variance and Standard deviation
•Standard Error- Skewness with Kurtosis
•P value with confidence interval
•T test and F ratio
•Hypothesis testing
•Introduction to Machine learning models
•Regression fundamentals
•Classification fundamentals
•Fundamentals of dimension reduction and data reduction models
•ANOVA with R
•ANOVA introduction & fundamentals
•ANOVA in R
•ANOVA Project
•Evaluation metrics or loss function for linear regression
•Linear regression with R
•Fundamentals of Linear regression
•Implementation of linear regression in R
•Linear regression project
•Logistic Regression with R
•Fundamentals of Logistic Regression
•Logistic Regression with R- Part 1- Data Wrangling
•Logistic regression with R-Part 2 Data Wrangling and visualization
•Logistic regression with R-Part 3 Conclusion with Prediction
•Logistic Regression Project
•Dimension reduction technique with principal component analysis
•Fundamentals of Dimension reduction technique with principal component analysis
•PCA implementation in r with princomp
•PCA project
•Clustering with K-Means
•Fundamentals of clustering with K-Means
•K-Means implementation in r
•K-Means Project
•Tree based models- CART technique & Random Forest
•Fundamentals of Decision tree and CART technique
•CART Implementation in R
•Fundamentals of Ensemble techniques with Random forest machine learning model
•Random Forest with R
•Random Forest Project
•KNN- K Nearest Model
•Fundamentals of KNN
•Implementation of KNN in R
•KNN Project
•Naive Bayes
•Naive Bayes fundamentals and implementation in R
•Naive Bayes project
•Neural networks with R
•Fundamentals of Neural Networks
•Implementation of Neural networks with R
•Neural network project
•Machine learning & Data science with Python
•Introduction of Machine learning with Python
•Python tool box for machine learning
•Anaconda distribution walk through & environment setup
•Jupyter notebook walk through
•Virtual environment introduction & setup
•Environment setup with Anaconda prompt
•Environment setup with Anaconda navigator
•Python’s best friends for machine learning- Numpy + Pandas
•Numpy introduction
•Operations with numpy object
•Data selection with Numpy
•Pandas introduction & overview
•Data analysis with Pandas
•Data visualization with python- Pandas, Matplotlib & Seaborn
•Data visualization with pandas
•Data visualization with Matplotlib
•Data visualization with seaborn
•Multi-class linear regression with Python & Scikit-learn
•Multi-class linear regression with Python & Scikit-learn- Part 1
•Multi-class linear regression with Python & Scikit-learn- Part 2
•Logistic regression with Python
•Introduction of logistic regression with Python
•How to use label encoding & one hot encoding
•How to handle Multi-Collinearity
•How to optimize performance with grid search & regularization
•Deep learning with Image recognition- Python-Keras-MLP
•Deep learning introduction
•Deep learning fundamenals
•Deep learning methodology
•Deep learning architecture
•Why do we need Activation function
•Relu & Softmax with Introduction to Keras
•Image recognition with Python, Keras & MLP- Part 1
•Image recognition with Python, Keras & MLP- Part 2
•Image recognition with Python, Keras & MLP- Part 3
•Image recognition with Python, Keras & MLP- Part 4