Machine Learning Foundation

BY
DataMites

Learn the fundamentals and foundation of machine learning with the Machine Learning Foundation by DataMites.

Mode

Online

Duration

2 Months

Fees

₹ 16900 21000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based

Course overview

The Machine Learning Foundation certification course is an online course of three-month live projects and six days or three weekends training that covers Python, machine learning algorithms, and deploying machine learning models. The whole Machine Learning Foundation training will take place via an online platform and the candidate will have complete access to that after enrolling in the course. The candidate can get Machine Learning Foundation certification at the end of the course accredited from IABAC. The complete curriculum of the Machine Learning Foundation certification syllabus is structured according to the emerging demand of the industries. 

The highlights

  • Different learning modes - Live virtual, Blended learning, Classroom training 
  • Classroom training 
  • Live virtual 
  • Certification accredited by IABAC 
  • Access to data science cloud lab for practice purpose 
  • The course provided by DataMites
  • Certificate of completion by DatMites

Program offerings

  • Live sessions
  • Assessments
  • Training
  • Projects
  • Mentorship
  • Placement support
  • Cheat sheets
  • Blended
  • Self paced
  • Data set
  • Videos
  • Study material
  • Newsletter
  • Online learning.

Course and certificate fees

Fees information
₹ 16,900  ₹21,000
  • The candidate can choose different plans according to the package for the Machine Learning Foundation fees. 
  • The candidate can go with any plan according to his or her preference. The fee amount for a Live virtual session is Rs.21,000, for Blended learning is Rs. 13,000and for Classroom training is Rs.26,000. 

Particulars

Amount

Discounted Fee

Virtual Live 

Rs. 21,000

Rs. 16,900

Blended learning 

Rs. 13,000

Rs. 7,900

Classroom  learning

Rs. 26,000

Rs. 17,900
certificate availability

Yes

certificate providing authority

IABAC

Who it is for

Following candidates can apply in the Machine Learning Foundation programme - 

  • The professionals who want to build their career in machine learning or data science should opt for the course. 
  • Fresh graduates can apply for the course. 
  • Senior professionals who want to gain knowledge of the field can opt for the course. 
  • Candidates that are pursuing the data science tracks can apply for the course. 

Eligibility criteria

Work experience 

For registration in the Machine Learning Foundation by DataMites, no work experience is needed.

Education 

The candidate must have an elementary knowledge of machine learning, high-level machine learning algorithms, coding of popular ML algorithms, and some basic knowledge of the IABAC framework for applying to the Machine Learning Foundation certification course. 

Certification qualifying details 

The candidate can achieve the Machine Learning Foundation certification after completing the course. 

What you will learn

Machine learning

After the completion of the Machine Learning Foundation programme, the candidate will learn the following skills - 

  • The candidate would get a chance to understand the elements of machine learning. 
  • Supervised learning will be accompanied by the training along with the theoretical aspects. 
  • Learn about the working of the fundamental elements of the machine learning process. 
  • With high-level theory and hands-on application of ML algorithms to traditional data sets, this course will give a holistic grasp of diverse ML techniques.

The syllabus

Machine learning introduction

  • What is machine learning
  • Machine learning vs artificial intelligence
  • Applications of machine learning
  • Machine learning vs statistical modeling
  • Machine learning languages and platforms

Machine learning algorithms

  • Popular machine learning algorithms
  • Supervised vs unsupervised learning
  • Clustering, classification, and regression
  • Application of supervised learning algorithms
  • Overview of modelling machine learning algorithm: Train, evaluation and testing.
  • Application of unsupervised learning algorithms
  • How to choose a machine learning algorithm?

Supervised Learning I

Simple linear regression
  • Theory, implementing in Python (and R), working on use cases
K-nearest neighbours
  • Theory, implementing in Python (and R), KNN advantages, working on use cases.

Multiple linear regression
  • Theory, implementing in Python (and R), working on the use case.

Decision trees
  • Theory, implementing in Python (and R), decision |tree pros and cons, working on use cases.

Random forests
  • Theory, implementing in Python (and R), reliability of random forests, working on Use Case

Supervised Learning II

Naive Bayes Classifier
  • Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on use case.

Association rules
  • Theory, implementing in Python (and R), working on the use case.

Support Vector machines
  • Theory,Support vector machines with Python and R, Improving the performance with Kernals, Working on Use Case.

Model evaluation
  • Overfitting and underfitting

Understanding different evaluation models

Unsupervised learning

  • K-means clustering: theory, Euclidean distance method.
  • K-means advantages and disadvantages
  • K-means hands-on with Python (and R)
  • Hierarchical clustering: theory
  • Hierarchical advantages and disadvantages
  • Hierarchical clustering with Python (and R)

Dimensionality Reduction

  • Dimensionality reduction: Feature extraction and selection
  • PCA example with Python (and R) with use case
  • Principal component analysis (PCA): Theory, eigenvectors
  • Advantages of dimensionality reduction
  • Collaborative filtering and its challenges
  • Application of dimensionality reduction with a case study.

Admission details

Follow the below-mentioned procedure to get enrolled in the Machine Learning Foundation online course - 

Step 1: To understand everything about the course and confirm the information, go and visit the official webpage.

Step 2: Select the "enquire now" option and, after addressing the question, select a package for the course.

Step 3: Finally, make a payment using a debit/credit card or a visa.

Step 4: Candidates would get a confirmation of payment receipt in the mail.

Step 5: The student is enrolled in the course.

How it helps

The candidate can avail of several Machine Learning Foundation certification benefits during and after completion of the course by attaining the certification accredited by IABAC. The certificate holds great importance as per the industrial requirement.  The course curriculum will provide the candidates with a better understanding of the emerging industry and the placement support will help the candidate to get better with their career perspective. After passing the course and receiving the certification, the applicant is eligible for the Machine Learning Foundation certification benefits. The candidate will get knowledge of cutting-edge technologies that are in great demand across the world.

FAQs

In what manner will all of the course's sessions be held?

Only the online method will be used for all live sessions and classes.

When will a candidate be able to obtain IABAC certification?

When the Machine Learning Foundation certification course is completed, the applicant will be able to obtain the IABAC accredited certification.

Is understanding of coding or Python programming required for the course?

Yes, knowledge of basic Python programming and coding is required to apply for the course.

What is the methodology of training for the course?

Candidates can choose from a variety of learning options at DataMites, including remote learning, classroom learning, and online learning.

What are the aspects of the Machine Learning Foundation online course that distinguish it from others?

This course adheres to current industry standards and provides students with exposure to the latest cutting-edge techniques and technology.

Is it viable to seek a refund of a candidate's fee if they decide not to continue?

The training money will be reimbursed in full if the training does not fulfil the candidate's expectations; however, the test fee will not be returned.

Will the candidate be supplied with materials for further study after the completion of the course?

DataMites contains study tools, data sources, and webinars to ensure that the applicant understands and practices the material fully. 

Is it permissible for the candidate to pay in installments for the course fee?

To reserve a place for the whole term of the course, the course fee must be paid in full at one time.

Do students get job assistance when they finish or after finishing the course?

DataMites has a Placement Assistance Team that helps job seeker candidates to discover the right fit on a personal level.

Is the candidate permissible to complete the Machine Learning Foundation programme in intervals?

No, the course can’t be completed in intervals, it needs to be done in one go only. 

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