Embark on a comprehensive learning journey starting with fundamental Python programming, including installation, variable manipulation, and essential data structures like lists, tuples, and dictionaries. Gain proficiency in numerical computations with NumPy and data manipulation with Pandas.
Prerequisites and Advanced Machine Learning for NLP
This course is part of Natural Language Processing with Real-World Projects Specialization
Recommended experience
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.
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September 2024
5 assignments
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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
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.