Machine Learning Training

Anyone can start Machine Learning course because there are no prior requirements necessary. We provide a 35-hour Machine Learning course taught by instructors with more than 10 years of real-time experience. The course includes real-world assignments and the faculty will direct you toward setting a work environment to practice assignments.

Assistance in CV preparation, interview questions answers, and materials are part of the training program. We do advise taking a one-hour session every day, From Monday through Friday, but one can also look into the weekend, fast-track, one-on-one, or customized programs.

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Machine Learning Engineer Career

✔️Machine Learning Pre-requisites

Basic Computer Knowledge.

✔️Jobs On Machine Learning

Top IT MNC such as Capgemini, Cognizant, IBM, Infosys, Accenture, etc.,

✔️Machine Learning Developer Salary

The average salary for a Machine Learning Engineer with 4 years experience in India is ₹18 Lakhs.

✍️ Detailed Course Curriculum

Our Technical expert will help you with real time issues. He also guide you in certification preparation and career mentoring if required.

Machine Learning Course FAQ's

01. can you help me in CV preparation?
Yes, We can help you preparing your resume.

02. will you help in interview preparation?
We can provide you interview question answers. The course covered many real-time examples. These examples might help you.

03. Is this course sufficient to get a job?
Our training covered as many real-time examples as we can. This course may equivalent to 2 to 3 years of work experience. You have to work hard if you are aimed at 4+ years of experience.

04. What’s the certification process?
Please come with an exam code. We’ll guide you further. We’ll guide you on how to get certified. Don’t worry, we’ll help you in certification process.

05. Can you provide Work Support?
We can provide job support for an additional fee. Contact the support team for fee details. You can choose either the hourly rate or monthly fee.

Introduction to Data Analysis

  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Introduction to R Programming

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors

Data Manipulation in R

  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions
  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Data Import Techniques

  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping

Exploratory Data Analysis

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs

Basics of Statistics & Linear & Logistic Regression

  • Basics of Statistics
  • Inferencial statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression

Data Mining: Clustering techniques, Regression &Classification

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering

Anova & Sentiment Analysis

  • Anova
  • Sentiment Analysis

Data Mining: Decision Trees and Random Forest

  • Decision Tree
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest

Project Work

  • 2 Real time Projects
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