Packt
Prerequisites and Advanced Machine Learning for NLP
Packt

Prerequisites and Advanced Machine Learning for NLP

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Install and set up Python and Anaconda for NLP projects.

  • Understand and evaluate linear regression and gradient descent methods.

  • Visualize data effectively with Matplotlib and Seaborn.

  • Apply machine learning algorithms like linear regression and KNN to NLP tasks.

Details to know

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Recently updated!

September 2024

Assessments

5 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Natural Language Processing with Real-World Projects Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 11 modules in this course

In this module, we will introduce the foundational aspects of Python, including installation and basic programming concepts. You will learn about variables, operations, loops, functions, and data structures such as strings, lists, tuples, sets, and dictionaries, preparing you for more advanced Python programming tasks.

What's included

18 videos2 readings

In this module, we will cover the essential concepts of NumPy, focusing on array operations. You will learn how to perform various computations and manipulations with NumPy arrays, enabling efficient data handling in Python.

What's included

3 videos

In this module, we will dive into Pandas, a powerful data manipulation library. You will learn about Series and DataFrames, data operations, indexing, merging, and pivot tables, equipping you with the skills to handle complex data analysis tasks.

What's included

12 videos1 assignment

In this module, we will explore linear algebra concepts crucial for machine learning. You will learn about vectors and matrices, perform various operations, and understand how to extend these concepts to higher dimensions, forming a solid mathematical foundation for advanced topics.

What's included

5 videos

In this module, we will focus on data visualization techniques using Matplotlib and Seaborn. You will learn how to create and interpret visualizations, work on a case study, and apply these techniques to time series data, enhancing your ability to present and analyze data visually.

What's included

4 videos

In this module, we will introduce you to machine learning and linear regression. You will learn about the principles and mathematics behind linear regression, as well as how to apply it to real-world data through case studies, preparing you for more complex machine learning algorithms.

What's included

10 videos1 assignment

In this module, we will cover gradient descent, a fundamental optimization technique. You will learn about its prerequisites, cost functions, optimization methods, and the differences between closed-form solutions and gradient descent, providing a strong basis for learning advanced machine learning algorithms.

What's included

8 videos

In this module, we will introduce classification and K-Nearest Neighbors (KNN). You will learn about classification principles, how to measure KNN's accuracy and effectiveness, and how to apply KNN to various problems, with practical case studies to reinforce your understanding.

What's included

14 videos

In this module, we will delve into logistic regression, an essential classification technique. You will learn about the Sigmoid function, log odds, and how to apply logistic regression to a case study, providing a robust understanding of this powerful tool.

What's included

4 videos1 assignment

In this module, we will explore advanced machine learning algorithms and concepts. You will learn about regularization techniques, model selection, and performance evaluation through practical case studies, enhancing your ability to implement and optimize advanced models.

What's included

10 videos

In this module, we will introduce deep learning, covering its history, key concepts, and neural network structures. You will learn about training neural networks, activation functions, and representations, providing a comprehensive introduction to this transformative field in machine learning.

What's included

10 videos1 reading2 assignments

Instructor

Packt
Packt
196 Courses2,786 learners

Offered by

Packt

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