Courses

Introduction to MLOps in Python

This course introduces a modern approach for managing large-scale machine learning solution using the different principles of MLOps and applies them o
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    Difficulty: Advanced
  • Asset 1
    Duration Approximately 6 hours

Control Flow in Python

This course introduces control flow concepts in Python and covers all the techniques with examples and adequate coding exercises so as to provide a co
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    Difficulty: Beginner
  • Asset 1
    Duration: Approximately 2.5 hours

Functions in Python

This course introduces the core concepts of developing Functions in the Python Programming language and their application in different aspects.
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    Difficulty: Beginner
  • Asset 1
    Duration: Approximately 2.5 hours

Recurrent Neural Networks in Python

This course introduces the fundamental concepts underlying Recurrent Neural Network and how to train them using backpropagation through time, using Py
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    Difficulty: Advanced
  • Asset 1
    Duration: Approximately 8 hours

Data Structures in Python

This course covers different Python data structures such as lists, sets or dictionaries and their application in programming to perform complex data a
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    Difficulty: Beginner
  • Asset 1
    Duration: Approximately 3 hours

Naive Bayes in Python

This course introduces the learner to the underlying aspects of the Naive Bayes algorithm and deals in detail with the concepts of probability theory,
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    Difficulty: Intermediate
  • Asset 1
    Duration: Approximately 4 hours

Scripting in Python

This course explains how to efficiently write Python Scripts that can be used for performing complicated tasks and reuse existing modules and packages
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    Difficulty: Beginner
  • Asset 1
    Duration: Approximately 2.5 hours

Sentiment Analysis in Python

This course accomplishes the task of Sentiment Analysis through the mixture of text preprocessing techniques and machine learning algorithms.
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    Difficulty: Advanced
  • Asset 1
    Duration: Approximately 6 hours

Tree Models in Python

This course covers a detailed explanation of tree-based models and their application in both regression and classification tasks and their Python impl
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    Difficulty: Advanced
  • Asset 1
    Duration: Approximately 6 hours

Clustering Algorithms in Python

This course provides a comprehensive understanding of  clustering techniques of unsupervised learning and implement them in the Python programming la
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    Difficulty: Advanced
  • Asset 1
    Duration: Approximately 6 hours

Logistic Regression in Python

This course will focus on providing exposure to building a logistic regression model and interpreting the results of the model as well.
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    Difficulty: Intermediate
  • Asset 1
    Duration: Approximately 4 hours

Boosting Algorithms in Python

This course provides a comprehensive understanding of boosting algorithms which are frequently used in data science along with their Python implementa
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    Difficulty: Advanced
  • Asset 1
    Duration: Approximately 6 hours