AI Powered Machine Learning Essentials with Python

Synopsis

Description

This comprehensive course provides a thorough introduction to machine learning essentials using Python. Divided into multiple sections, the curriculum covers fundamental concepts of machine learning, key Python libraries for data manipulation and visualization, and practical implementation of machine learning algorithms using Scikit-learn. Participants will learn how to preprocess data, train machine learning models, evaluate model performance using cross-validation techniques, and conduct movie review analysis as a real-world application of machine learning.

Introduction to Machine Learning:

Participants are introduced to the basics of machine learning, including its advantages and disadvantages, and its applications in various domains.

NumPy:

Participants gain proficiency in using NumPy, a fundamental Python library for numerical computing, for efficient array manipulation and mathematical operations.

NumPy Array:

Participants learn how to create NumPy arrays, perform indexing and slicing operations, and manipulate arrays of arrays for data handling.

Matplotlib:

Participants explore Matplotlib, a popular Python library for data visualization, and learn how to create various types of plots and charts to visualize data.

Pandas:

Participants learn how to use Pandas, a powerful data manipulation library in Python, for data analysis, manipulation, and cleaning.

Scikit-learn:

Participants delve into Scikit-learn, a versatile machine learning library in Python, and learn how to train machine learning models, make predictions, and evaluate model performance.

Learning and Predicting:

Participants learn the process of training machine learning models on training data and making predictions on new data using Scikit-learn.

Cross Validation:

Participants explore cross-validation techniques for evaluating model performance and preventing overfitting in machine learning models.

Movie Review Analysis:

Participants apply their machine learning skills to a real-world project on movie review analysis, where they learn how to preprocess text data, build sentiment analysis models, and evaluate model performance.

Reference Files:

Participants have access to reference files and resources for further learning and exploration of machine learning concepts and techniques.

Throughout the course, participants engage in theoretical lectures, practical demonstrations, and hands-on exercises to reinforce their learning and develop proficiency in machine learning essentials with Python. By the end of the course, participants will have the knowledge and skills to apply machine learning algorithms to various datasets and conduct meaningful analysis using Python.