# Machine Learning using R Training in Marathahalli

Machine learning is the latest AI (Artificial Intelligence) based technology which can be applied anywhere in the real time environment. In AI, a software application can run its sequential tasks in an intelligent and independent manner. Here, R programming can be applied to develop customized analytical algorithms and data manipulation modules within the Machine Learning environment. Highly effective throughputs can be obtained using AI based application via statistical and predictive machine learning algorithms. Data collection, data preparation, data modeling, data model testing and performance monitoring are the five focus areas of Machine Learning. Some of the few examples for machine learning real-time applications are healthcare domain, face recognition, tagging features in social networks and spam detection of mailboxes.

**Machine learning Job Opportunities**

- Make yourself strong in statistics, data mining, reporting/visualization, classified algorithms, supervised and unsupervised machine learning algorithms, you can find opportunities as “Machine Learning expert”.
- Start learning business analysis, SQL and R programming along with Machine Learning to make yourself eligible for “Data Scientist” jobs.
- If you possess strong Machine Learning experience with deep learning, NLP, Java and Python, you can get job as Machine Learning expert.
- Make yourself strong in Hadoop, NoSQL and Big Data concepts with Machine Learning. Then, you can find opportunities as “Big Data Machine Learning expert”.
- JP Morgan, Accenture, Aricent, Fiserv, SAP, Sutherland, Intel, AIG, Bosch, Qualcomm are some of the companies hire for Machine Learning experts.

TIB Academy gives best Machine learning training. Trainers are very good in provide training to students. Classrooms are enabled with free wifi and projectors. They provide classroom training, online training and weekend training for machine Learning course. Classes are very interactive and practical.

**Prerequisites for Machine Learning**

- Basic statistics and programming knowledge.
- If you are already familiar with the above, this course will be easier for you to learn. Otherwise, our experienced professionals are here to teach you and coach you right from the Machine Learning fundamentals.

**Are you a beginner? Evaluate yourself with the following basic prerequisite questions.**

- What is median?
- What is meant by variance and covariance?
- How to define a function?
- What are the difference between loops and conditions?

**Our Machine Learning Training and Support**

TIB Academy is the best Machine Learning training institute in Marathahalli. Our trainers are highly experienced professionals. Currently, they are all working in top rated MNCs and Corporates, carrying years of real time industry experience in their particular technologies. In this Machine Learning training in Marathahalli, you will be experiencing a differentiated learning environment. Our Machine Learning syllabus includes statistics, workflow of R tool, data mining, reporting/visualization, fundamental of SQL, classified algorithms, supervised, unsupervised machine learning algorithms and lot more. For the detailed Machine Learning course syllabus, please check below.

Usually, our Machine Learning training sessions are scheduled during weekday mornings (7AM – 10AM), weekday evenings (7PM – 9:30PM) and weekends (flexible timings). We do provide Machine Learning classroom training and Machine Learning online training, both on weekdays and weekends based upon the student’s preferred time slots.

You will surely enhance your technical skills and confidence with this Machine Learning training. Our connections and networks in the job market will help you to achieve your dream job easily. Compared to other training institutes, we are offering the best Machine Learning training course in Marathahalli, Bangalore, where you can get the best Machine Learning training and placement guidance for reasonable and affordable cost.

## Machine Learning with R Training in Marathahalli Syllabus

#### 1. Introduction to R

- What is R?
- Why R?

#### 2. Installing R

- R environment
- How to get help in R
- R console and Editor

#### 3. Understanding R data structure

- Variables in R
- Vectors
- Matrices
- List
- Data frames
- Using Cbind, Rbind, attach and detach functions in R
- Importing data
- Reading Tabular Data files
- Reading CSV files
- Importing data from excel
- Access database

#### 4. Saving in R data

- Loading R data objects
- Writing to files
- Manipulating Data
- Selecting rows/observations
- Selecting columns/fields
- Merging data
- Relabeling the column names
- Converting variable types
- Data sorting
- Data aggregation
- Using functions in R
- Commonly used Mathematical Functions
- Commonly used Summary Functions
- Commonly used String Functions
- User defined functions
- local and global variable
- While loop
- If loop
- For loop
- Arithmetic operations
- Charts and Plots
- Box plot
- Histogram
- Pie graph
- Line chart
- Scatter plot

#### 5. Objective Test-1

#### Descriptive Statistics

- Measures of Central Tendency,Shape,Variablity
- Probability Distribution Function
- Continuous PDF & Discrete PDF
- Central limit Theorm

#### Inferential Statistics

- Hypothesis testing
- Usage of Hypothesis Testing
- ANOVA

#### 6. Objective Test-2

Regression Algorthim in R

- Linear Regression(OLS)

#### 7. Objective Test-3

8. Project -1

9. Logistic Regression(MLE)

#### 10.Objective Test-4

#### 11. Project-2

#### 12.Tree Algorthim in R

- Decision Tree(CART & CHAID)
- Random Forest(Bagging)
- Objective Test-5
- Project-3

#### 13.Classification Algorithm in R

- Clustering( K-means)
- Objective Test-6
- Project-4

#### 14. Dimension reduction/Naïve Bayes in R

- Principal Component Analysis
- Association Rule(Market Basket Analysis/Apriori)
- Sentiment Analysis( Naïve-Bayes Approach)
- Final Project

## Machine Learning Interview Questions

1. You are given a train data set having 1000 columns and 1 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.)

2. Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?

3. You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

4. You are given a data set on cancer detection. You’ve build a classification model and achieved an accuracy of 96%. Why shouldn’t you be happy with your model performance? What can you do about it?

5. Why is naive Bayes so ‘naive’ ?

6. Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm?

7. You are working on a time series data set. You manager has asked you to build a high accuracy model. You start with the decision tree algorithm, since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than decision tree model. Can this happen? Why?

8. You are assigned a new project which involves helping a food delivery company save more money. The problem is, company’s delivery team aren’t able to deliver food on time. As a result, their customers get unhappy. And, to keep them happy, they end up delivering food for free. Which machine learning algorithm can save them?

9. You came to know that your model is suffering from low bias and high variance. Which algorithm should you use to tackle it? Why?

10. You are given a data set. The data set contains many variables, some of which are highly correlated and you know about it. Your manager has asked you to run PCA. Would you remove correlated variables first? Why?