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Course Details

Machine Learning
Machine Learning

Self Paced

Last Update:

February 17, 2023

3d rendering robot learning - Machine learning

About Course

Introductory price for limited time period

In today’s world, the ubiquitous influence of machine learning is being felt everywhere. With or without our knowledge of machine learning technologies are assisting or influencing our lifestyle. You are surrounded by machine learning everywhere, from ‘Alexa,’ ‘Siri,’ and ‘Google Assistant’ to Autonomous Vehicles. You see a large amount of data everywhere. Therefore, it is vital to analyze this data to extract useful information and develop an algorithm based on this analysis. This can be achieved through machine learning. Machine learning is an integral part of artificial intelligence, which designs algorithms based on data trends and historical relationships between data. Machine learning is used in various fields such as bioinformatics, intrusion detection, information retrieval, game playing, marketing, malware detection, image deconvolution, etc.

What is the objective of this course?

Machine learning has been recognized as central to the success of Artificial Intelligence, and it has applications in various areas of science, engineering, and society. The main objective is to develop systems (programs) for specific practical learning tasks in application domains. This course helps to analyze new learning methods and develop general learning algorithms independent of applications. Machine Learning has become achievable in many essential applications because of the recent progress in learning algorithms and theory, the rapid increase of computational power, the excellent availability of a massive amount of data, and interest in commercial ML application evolution. ML is naturally a multi-disciplinary subject area. It unlocks opportunities to develop cutting-edge machine learning applications in various verticals – such as cyber security, image recognition, medicine, or face recognition.

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What Will You Learn?

  • To get introduced to programming and libraries used in machine learning
  • To explore regularization, and support vector machine
  • To analyze NLP, recommender system, and neural networks
  • To understand image analysis with CNN
  • To understand the fundamentals of unsupervised learning and dimension reduction
  • To learn python programming using NumPy, Pandas, Scikit-learn, and Matplotlib
  • To well versed with the basics of anomalies and anomaly detection, sentence segmentation, tokenization, stemming and lemmatization
  • To explore Neural Networks, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)
  • This course helps you analyze K-Means clustering, hierarchical clustering, and density based clustering
  • The course presents statistics in machine learning
  • This course helps you analyze the present scenario in the world of machine learning

Course Content

Python Programming using NumPy, Pandas, Scikit-learn, and Matplotlib

Linear Regression, Logistic Regression, and Least Squared Method

Overfitting, Under-fitting, and its Prevention Techniques

Regularization and its Techniques

Anomalies and Anomaly Detection

Natural Language Processing (NLP)

Sentence Segmentation, Tokenization, Stemming and Lemmatization

Dependency Parsing, POS Tagging and Named Entity Recognition

Libraries like spaCy and NLTK with Hands on Example

Matrix Factorization Approach

Neural Networks, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)

Autoencoders, Image Analysis and its Applications

ResNet 50 and Residual Mapping

K-Means Clustering, Hierarchical Clustering, and Density Based Clustering

Dimension Reduction and its Types

Statistics in Machine Learning

Parametric Tests and Non-Parametric Tests

Pearson Correlation Coefficient and Z-Test

ANOVA and Spearman’s Rank Correlation

Hands-on Examples for all of the above


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3,999.00 7,080.00
  • Instructor
    Self Paced
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Material Includes

  • E-Learning Modules
  • Quizzes & Assessment after each module
  • Final Assessment


  • Good to have a background in mathematics/statistics/calculus
  • Good to have computer-programming skills
  • No prior knowledge of machine learning is required


  • Someone who has some background in machine learning but would like a more current and complete understanding
  • Someone who has little or no machine learning background
  • Anyone who has no prior knowledge about machine learning
  • Working professionals, freshers, and hobbyists who want to understand machine learning