LSTM Skills you will learn

  • LSTM Architecture
  • Training LSTM Models
  • TimeSeries Forecasting
  • Text Generation with LSTM
  • Hyperparameter Tuning
  • Sequential Data Processing

Who should learn this free LSTM course?

  • Machine Learning Engineer
  • Data Scientist
  • AI Engineer
  • Deep Learning Engineer
  • NLP Engineer
  • Business Intelligence Analyst

What you will learn in this free LSTM course?

  • Introduction to LSTM

    • Introduction

      01:22
      • Introduction
        01:22

    • Lesson 1: What is LSTM?

      32:31
      • What is LSTM?
        32:31

    • Lesson 2: LSTM Next Word Prediction in Python

      17:27
      • LSTM Next Word Prediction in Python
        17:27

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Why you should learn Long Short-Term Memory Networks?

$421.1 Billion

Expected size of the global Machine Learning market by 2030.

$163K+ (USA) | INR 10.3 LPA

Average Salary of a Machine learning Engineer annually.

About the Course

This LSTM Course provides a comprehensive introduction to Long Short-Term Memory (LSTM) networks, a powerful type of recurrent neural network (RNN) used for sequential data tasks. Learn the architecture and components of LSTMs, including how they handle long-term dependencies in data. The course covers practical applications such as time-series forecasting, text generation, and natural language processing. Gain hands-on experience using frameworks like TensorFlow and Keras to build, train, and optimize LSTM models for real-world problems.

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FAQs

  • What is an LSTM?

     LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) designed to handle and predict data sequences with long-term dependencies, often used in tasks like time-series forecasting and natural language processing.

  • What will I learn in the Introduction to LSTM course?

     This course covers the fundamentals of LSTM networks, their architecture, how they differ from traditional RNNs, and how to apply them to real-world problems like text generation, speech recognition, and sequence prediction.

  • Do I need prior experience in deep learning to take this lstm course?

     While a basic understanding of machine learning and neural networks is helpful, this course is designed for beginners, providing a step-by-step introduction to LSTM.

  • What are the main applications of LSTM?

     LSTMs are commonly used in time-series prediction, speech recognition, language modeling, text generation, and other sequence-based tasks in AI and machine learning.

  • Will I get hands-on experience with LSTMs in this lstm course?

    Yes, this course includes practical exercises where you will build and train LSTM models using frameworks like TensorFlow or Keras.

  • What tools and frameworks will I use in this lstm course?

     You will work with popular deep learning frameworks such as TensorFlow, Keras, and PyTorch to implement LSTM networks.
     

  • How long does the lstm course take to complete?

     This Introduction to LSTM course is 2 hours long.

  • What career roles can this lstm course help me to pursue?

    After completing this course, you can pursue job roles like Machine Learning Engineer, Data Scientist, AI Engineer, or NLP Specialist, where LSTMs are widely used in real-world applications.

  • What prerequisites are required for this lstm course?

    Basic understanding of Python programming and machine learning concepts is recommended, but the course is suitable for beginners who want to learn about LSTM networks and their applications.

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