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. Strengthen your mathematical foundation with key linear algebra concepts vital for machine learning algorithms. Progress to data visualization using Matplotlib and Seaborn, interpreting and presenting data effectively. Develop a strong base in simple linear regression and gradient descent, and explore classification techniques with KNN and logistic regression through hands-on case studies. Dive into advanced machine learning algorithms, including regularization techniques and deep learning foundations, tailored for NLP applications. By course end, you'll have a robust understanding of implementing and optimizing machine learning models for NLP tasks, preparing you for advanced projects and career opportunities. Ideal for aspiring data scientists, machine learning enthusiasts, and professionals specializing in NLP, with basic Python and high school-level math knowledge required.
Log Odds
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Skills You'll Learn
Data Science, Natural Language Processing, Linear Regression, NumPy, Machine Learning
From the lesson
Prerequisite - Logistic Regression
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.
Taught By
Packt - Course Instructors