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Machine Learning Training and Placement Course

  • 24×7 Doubt support
  • Classroom Course
  • Expert Trainers
  • Assignments and Quiz
  • Live projects
  • Course Duration : 1.5 month (45 Days)


Machine learning is a sub part of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

By the end of this course, you will.

  • Have sound mathematical understanding of popular ML algorithms.
  • Preparedness to use state of the art machine learning algorithms in formulating and solving new problems.
  • Capability to train (or solve optimization problems) ML models with applications in real-world use cases.

Machine Learning is about giving a brain to a machine by feeding information using complex algorithms such as CNN and RNN. In this course you will learn and train machine to make machine mimic human behavior.

Batch Timings

  • Weekdays : Mon, Tue, Wed, Thu, Fri (2 hrs)
  • Weekends : Sat, Sun (4-5hrs)

Modes Of Teaching

Course Content

Machine Learning Training
1. Learning Data Analytics
1.1 Business Analytics, Data, Information
1.2 Understanding Business Analytics and R
1.3 Compare R with other software in Analytics
1.4 Install R
1.5 Perform Basic operation in R using the Command Line
1.6 Learn the use of IDE R studio
1.7 How to use R help feature in R
2. Introduction to R programming Language
2.1 Variables in R
2.2 Scalars
2.3 Vectors
2.4 Matrices
2.5 List
2.6 Data Frames
2.7 Using C, Cbind, Rbind, Attach and Detach functions in R
2.8 Factors
3. Introduction to Data manipulation in R
3.1 Data Sorting
3.2 Find and remove duplicates Record
3.3 Cleaning Data
3.4 Recording Data
3.5 Merging Data
3.6 Slicing Data
3.7 Apply Functions
4. Data Import Techniques In R
4.1 Reading Data
4.2 Writing Data
4.3 Basic SQL Queries in R
4.4 Scrapping
5. Exploratory Data Analysis
5.1 Box plot
5.2 Histogram
5.3 Pareto Charts
5.4 Line Charts
5.5 Pie Graph
5.6 Scatter Plot
5.7 Developing Graphs
6. Basics of Statistics, Linear and Logistics Regression
6.1 Basics of statistics
6.2 Inferential statistics
6.3 Probability
6.4 Hypothesis
6.5 Standard Deviation
6.6 Outliears
6.7 Correlation
6.8 Linear & Logistic Regression
7. Data Mining : Clustering Techniques, Regression and Classification
7.1 Introduction to Data Mining
7.2 Understanding Machine Learning
7.3 Supervised and Unsupervised Machine Learning Algorithms
7.4 K means clustering
8. Anova and Sentiment Analysis
8.1 Anova
8.2 Sentiment Analysis
9. Data Mining: Decision Trees and Random Forest Algorithms
9.1 Decision Tree
9.2 Random Forest Concept
9.3 Random Forest Working
9.4 Random Forest Attributes
10. Assignment Projects
10.1 Research Project
10.2 One Project (Real Time)

Contact Us

Training Center

Chennai Branch : Saidapet Center Address

No. 31A, E Jones Rd, Periyapet, Saidapet Chennai, Tamil Nadu

Pin Code – 600015 (IN)

Call Us :

(+91) 4442064048

(+91) 9385252523

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