Machine Learning with R Programming
Master the concepts of R programming and Machine Learning using R. Enroll today to learn from R and ML expert.
Job Assured Program100% Assistance Till You Get Job
Course Duration5 Months + Life Time Access
Industry Experienced TrainerGet Trained from Experts
Training FormatClassroom/Live Online
R tops Google searches as the preferred Advanced Analytics software, experiencing rapid growth alongside its user base. Consequently, R emerges as an intriguing programming language, highly sought-after as a valuable addition to your resume. This training will make you an expert in R Programming language, Data Manipulation, Data Visualization, Exploratory Data Analysis, Data Mining, Correlation & Regression, Sentimental Analysis.
Key Highlights
Enterprise training for teams
Placement Stats
Our Alumni in Top Companies
Placement Highlights
Batches Timing for Data Analytics Certification
| Track | Weekdays (Tue-Fri) | Weekends (Sat-Sun) | Fast Track |
| Course Duration | 4 Months | 5 Months | 30 Days |
| Hours Per Day | 2-3 Hours | 3-4 Hours | 6 Hours |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
Machine Learning Certification
Earn your Certificate after the completion of the course.
This certification helps you gain skills and knowledge to jump start journey towards becoming a successful Machine Learning Certified Professional.
Post your Certificate on LinkedIn, Meta, Twitter and get recognition of the Hiring Managers from the top-notch companies.
Course Curriculum
Gyansetu’s Machine Learning with R Certification course will make you build your career in the field of AI and ML. It will make you a master of analyzing data and predicting through it using R programming skills, Statistics skills and Machine learning algorithms.
R Programming for Machine Learning
- 5 Modules
- Arithmetic, Variables and Datatypes in R
- Vectors
- Operators
- Indexing and Slicing
- Creating, Selecting and Indexing in R Matrices
- Factor and Categorical Matrices
- Operations in Matrices
- Data frames basics
- Indexing, Selection and Operations on Data Frames
- Lists and its Operations
- File Handling – Working with CSV, Excel and SQL
- Web Scraping
- Logical Operators
- Conditional Statements
- Loops
- Functions
- Built-in R functions – Apply functions, Math functions, Regular Expressions and Dates & Timestamps
Statistics for Machine Learning
- 4 Modules
- 36 Hours
- Intro to Descriptive Statistics
- Measures of Center: Average, Mean, Median, Mode
- Measures of Dispersion: Range, Quartiles and Percentiles, Inter Quartile Range (IQR), Variance, Standard Deviation
- Covariance and Correlation
- Simpson’s Paradox
- Normal Distribution
- Standard Normal Distribution
- T- Distribution
- Point Estimation
- Confidence Intervals
- P-Value
- Hypothesis testing
- Levels of Hypothesis Testing
- Uses of Probability
- Need of Probability
- Basics of Probability
- Binomial Distribution
- Conditional Probability
- Bayesian Inference
- Bayes Rule
- Linear Regression Analysis
- Logistic Regression Analysis
- Problem of Collinearity
- WOE and IV
- Residual Analysis
- Heteroscedasticity
- Homoscedasticity
Machine Learning in R
- 12 Modules
- What is Machine Learning?
- Statistical Learning vs Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Accuracy
- Precision
- Recall
- True Positive Rate / False Positive Rate
- F1-Score
- Confusion Matrix
- ROC Curve
- Area Under the ROC curve
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- R2 Score
- What is Regression and its use cases?
- Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression – ID3 and CART
- What is Classification and its use cases?
- Logistic Regression
- Support Vector Machine (SVM)
- Kernel SVM
- Naïve Bayes
- k-Nearest Neighbours (k-NN)
- Decision Tree Classification – ID3 and CART Algorithms
- Ensemble Learning
- Bagging vs Boosting
- AdaBoost, Gradient Boost, XGBoost
- Random Forest
- What is model tuning?
- Overfitting vs Underfitting
- Cross Validation
- Bias and Variance
- Hyperparameter Optimization
- Grid Search vs Random Search
- What is Dimensionality Reduction?
- Feature Selection
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Random Projection
- ICA
- What is Clustering & its Use Cases?
- K-Means Clustering
- K-Means++
- How to do optimal clustering – Elbow Curve?
- C-means Clustering
- Hierarchical Clustering
- DBSCAN Clustering
- Gaussian Mixture Model
- Clustering Model Evaluation Metrics – Silhouette Coefficient, Dunn’s Index, etc.
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering
- What is Reinforcement Learning?
- Why Reinforcement Learning?
- Elements of Reinforcement Learning
- Exploration vs. Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- Values
- What is Time Series Analysis?
- Importance and Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- Intro to NLP
- Setting up NLTK environment
- What is NLTK Toolkit?
- Text Processing
- Cleaning
- Normalization
- Tokenization
- Stop Word Removal
- Part of Speech Tagging
- Named Entity Recognition
- Stemming
- Lemmatization
- Feature Extraction
- Bag of Words Model
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- Word Embeddings
- What is Deep Learning?
- Curse of Dimensionality
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron?
- Learning Rate
- Epoch
- Batch Size
- Activation Function
- Single Layer Perceptron
Industry Ready Projects
This project helps to predict customer churn/ attrition for telecom companies using dataset like customer information, service usage and churn status. Implementing data pre-processing like handling missing values, feature engineering, and encoding categorical variables. You will be working on Classification algorithms like Logistic Regression, Decision Trees and SVM to build predictive models. This will help companies to improve retention rate, enhance customer experience and improve business profitability.
This project uses ML algorithms like Random Forest, Gradient Boosting on historical data to predict equipment failures or maintenance needs. Sensors are used on machinery to collect Time series data and pre-processed. Techniques like anomaly detection and feature engineering are applied to identify patterns indicating failures. This helps companies to improve equipment uptime and reduce maintenance cost, enhancing overall efficiency and productivity.
Skills you can add in your CV after this course
What Sets ThisProgram Apart?
All-in-One Toolkit
Beginner-to-Pro
Gen AI Empowered
Career-Focused Tracks
Real Industry Exposure
Legacy & Expertise
Practice Over Theory
Expert Mentorship
- Aspiring Data Scientists/ AI Professionals
- Market Researchers
- Data Analysts
- Career Changers
- Economists
- Academic Researchers and Educators
Career Assistance we offer
Get a 100% Guaranteed Interview Opportunities Post Completion of the training.
Get chance to connect with Hiring partners from top startups and product-based companies.
Get One-On-One Mock Interview Session with our Experts. They will provide continuous feedback and improvement plan until you get a job in industry.
Live interactive sessions with industry experts to gain knowledge on the skills expected by companies. Solve practice sheets on interview questions to help crack interviews.
Personalized career focused sessions to guide on current interview trends, personality development, soft skill and HR related questions.
Get help in creating resume & Naukri Profile from our placement team and learn how to grab attention of HR’s for shortlisting your profile.
Top Companies Hiring
Our Learners Testimonials
Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals.
Frequently Asked Questions
What are the prerequisites for taking up this Machine Learning in R Certification training?
There is no prerequisite to join this course. However, basic knowledge of programming concepts is an additional benefit.
Why should you do Machine Learning in R Certification from Gyansetu?
Though there are many online courses available online but we at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is the most important task. This is the reason we have our course curriculum designed and delivered by industry experts along with capstone industry ready projects which will drive your learning through real-time IT industry scenarios and help in clearing interviews.
How long is the course duration?
The total duration of the Machine Learning in R Certification course is 320 hours (160 Hours of live Instructor-Led training and 160 hours of self-paced learning).
I am currently working as an IT professional; will you provide me with Job Assistance?
We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.
Interview selection depends on your knowledge and learning. As per the past trend, the initial 5 interviews are a learning experience of :-
- What type of technical questions are asked in interviews
- What are their expectations?
- How should you prepare?
Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.
I am currently working in a non-IT field; will you provide me with Job Assistance?
We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!!
Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x.
Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of –
- What type of technical questions are asked in interviews
- What are their expectations?
- How should you prepare?
Our faculty team will constantly support you during interviews. Usually, students get a job after appearing in 6-7 interviews.
I’m not working or have a gap in profile or currently pursuing studies, will you provide me Job Assistance?
We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students, so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x.
Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of :-
- What type of technical questions are asked in interviews
- What are their expectations?
- How should you prepare?
Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.
Will you provide a demo session before enrollment?
Yes, a 1:1 faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you resolve all your queries before you start classes with us.
What if I miss a class?
We understand the importance of every session. Session’s recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.
Do you provide learning content?
Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc., so that a student can get a good handle of these topics.
What if I am taking more time to learn or I want to repeat the course?
We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.
What is your average batch size?
We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.
Do you provide classes on weekends?
Yes, we have batches available on weekends. We understand many students are in jobs and it’s difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors on +91-9999201478.
Do you provide classes on weekdays?
Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more about this on +91-9999201478.
How will I get the software installed?
You don’t need to pay anyone for software installation, our faculties will provide you with all the required software’s and will assist you in the complete installation process.
What if I have a query after I complete the course?
Our faculties will help you in resolving your queries during and after the course.
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