Certificate of Proficiency in Machine Learning for Data Science & AI

Application fee : 1500 * INR
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Details

Certification Body: Aegis School of Data Science
Location: Online
GST: 18%
Total course fee: 0 * INR
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Course Details

Certification Exam Description

This certification is designed to meet a growing business need of individuals skilled in Artificial Intelligence, Data Analytics and Data Science as Machine Learning is the backbone of these fields. This certification will combine theory and practice to enable everyone to gain the necessary knowledge to compete in the ever changing work environment. This certification will be based on Basics of Machine Learning, Linear Models for regression and classification, Non-Linear Models for regression and classification, Dimensionality Reduction Techniques and Clustering Techniques (Topic are provided below).

The exam is closed book and no outside reference materials are allowed. The exam’s duration is 120 minutes, and it comprises 120 questions. The following topics are general guidelines for the content likely to be included in the exam. However, other related topics may also appear on any specific delivery of the exam. If any changes required in the following guidelines, sufficient time will be provided to the candidates to prepare accordingly.this cannot happen randomly, at any time, changes should be made only at pre-specified intervals, and sufficient time will have to be given to the candidates to prepare accordingly.

Brief about Topic:

Machine learning is a subfield of artificial intelligence that is concerned with the design, analysis, implementation, and applications of programs that learn from experience. It offers some of the most cost-effective approaches to automated knowledge acquisition in emerging data-rich disciplines (bioinformatics, cheminformatics, neuroinformatics, environmental informatics, social informatics, business informatics, security informatics, materials informatics, education etc.). Learning algorithms can also be used to model aspects of human and animal learning.This certification will test your subject knowledge. Also, this will test your communication skills to convince your findings / results to your clients.

Certification Exam Topics

Sl. No.

Topic/Subtopics

1.

Introduction

  • Data Science, Data and Big data,
  • Predictive Analysis, Machine Learning, Data Mining,
  • Introduction to Machine learning: Machine Learning Basics, How Machines Learn, Machine learning in Practice, Types of Machine Learning Techniques

2.

Exploratory Data Analysis and Data Preprocessing

  • Summary Statistics,
  • categorical data, continuous data, comparing features
  • Data Transformations,
  • Dealing with Missing values, Outliers detection and treatment, Encoding Categorical Features, Binning Predictors

3.

Resampling Techniques and Model Tuning

  • Bias -Variance Trade-off,
  • Train/Test/Validation sets,
  • Learning Curves,
  • k-fold cross validation, Randomized and Grid Search

4.

Linear Regression Analysis

  • Understanding Regression,
  • Simple Linear Regression,
  • Ordinary Least squares estimation,
  • Gradient Descent method,
  • Evaluation Metrics,
  • Multiple Linear Regression

5.

Regularization Techniques

  • Use of Ridge regression, LASSO and Elastic Net

6.

Logistic Regression and Evaluation Metrics for Classification

  • Need of Logistic regression,
  • Using Logistic Regression for classification,
  • Evaluation Metrics for Classification and ROC Curve

7.

Imbalance Class Classification problems

  • Handling of Imbalance classes problem using various strategies

8.

K-Nearest Neighbors

  • Classification and Regression using KNN

9.

Dimensionality Reduction Techniques

  • Principal Component Analysis,
  • Kernel PCA,
  • NMF and t-SNE

10.

Clustering Techniques

  • K-Means, Mean-shift, DBSCAN, PAM, Hierarchical Clustering

11.

Naïve Bayes Classifiers

Classification using Naïve Bayes family of Classifiers

12.

Support Vector Machines

  • Support Vector Classifier,
  • Support Vector Machines,
  • Support Vector Regression

13.

Decision Trees

  • Understanding Decision Trees,
  • Classification and Regression using Decision Trees

14.

Ensemble Techniques

  • An introduction to ensemble techniques,
  • BAGGING, Random Forest,
  • BOOSTing

15.

Recommender Systems

  • Building a recommender system using ML

16

Artificial Neural Networks