Machine Learning
Model Evaluation and Validation
Explore the methods for evaluating and validating machine learning models. This course covers techniques such as cross-validation, bootstrap sampling, and performance metrics like precision, recall, and F1 score. Students will learn to identify and a
Model Evaluation and Validation
Explore the methods for evaluating and validating machine learning models. This course covers techniques such as cross-validation, bootstrap sampling, and performance metrics like precision, recall, and F1 score. Students will learn to identify and a
Natural Language Processing Basics
An introduction to natural language processing (NLP), covering the fundamental techniques and tools used to analyze and interpret text data. Topics include tokenization, stemming, lemmatization, and sentiment analysis. Students will learn to apply NL
Natural Language Processing Basics
An introduction to natural language processing (NLP), covering the fundamental techniques and tools used to analyze and interpret text data. Topics include tokenization, stemming, lemmatization, and sentiment analysis. Students will learn to apply NL
Data Science in ML
Gain a broad understanding of data science, covering data manipulation, statistical analysis, and visualization techniques. The course introduces key concepts and tools used in data science, including Python programming, data wrangling, and explorato
Data Science in ML
Gain a broad understanding of data science, covering data manipulation, statistical analysis, and visualization techniques. The course introduces key concepts and tools used in data science, including Python programming, data wrangling, and explorato
Feature Engineering
Learn the art of feature engineering, an essential skill for improving machine learning model performance. This course covers techniques for creating, selecting, and transforming features to enhance model accuracy. Topics include handling categorical
Feature Engineering
Learn the art of feature engineering, an essential skill for improving machine learning model performance. This course covers techniques for creating, selecting, and transforming features to enhance model accuracy. Topics include handling categorical
Supervised Learning: Regression
Dive into regression analysis, learning about linear regression, polynomial regression, and model evaluation metrics. Students will understand how to implement and interpret regression models, apply them to real-world data, and evaluate their effecti
Supervised Learning: Regression
Dive into regression analysis, learning about linear regression, polynomial regression, and model evaluation metrics. Students will understand how to implement and interpret regression models, apply them to real-world data, and evaluate their effecti
Supervised Learning: Classification
In this course, students will delve into supervised learning with a focus on classification algorithms. Topics include decision trees, k-nearest neighbors (k-NN), and logistic regression. Students will learn to implement these algorithms in Python, u
Supervised Learning: Classification
In this course, students will delve into supervised learning with a focus on classification algorithms. Topics include decision trees, k-nearest neighbors (k-NN), and logistic regression. Students will learn to implement these algorithms in Python, u
Basics of Python for ML
This course focuses on the essential Python programming skills needed for machine learning. Students will learn to use important libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. The course also cover
Basics of Python for ML
This course focuses on the essential Python programming skills needed for machine learning. Students will learn to use important libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. The course also cover
Introduction to Machine Learning
This course provides a comprehensive introduction to the field of machine learning. It covers key concepts, algorithms, and applications, including supervised and unsupervised learning. Students will learn how to build and evaluate machine learning m
Introduction to Machine Learning
This course provides a comprehensive introduction to the field of machine learning. It covers key concepts, algorithms, and applications, including supervised and unsupervised learning. Students will learn how to build and evaluate machine learning m
Data Preprocessing and Cleaning
Data preprocessing and cleaning are critical steps in the machine learning workflow. This course teaches students how to handle missing data, remove duplicates, and normalize data. Techniques for feature scaling, encoding categorical variables, and d
Data Preprocessing and Cleaning
Data preprocessing and cleaning are critical steps in the machine learning workflow. This course teaches students how to handle missing data, remove duplicates, and normalize data. Techniques for feature scaling, encoding categorical variables, and d
Unsupervised Learning: Clustering
Study unsupervised learning techniques with a focus on clustering algorithms. This course covers k-means clustering, hierarchical clustering, and DBSCAN. Students will learn to implement these algorithms in Python, understand their applications, and
Unsupervised Learning: Clustering
Study unsupervised learning techniques with a focus on clustering algorithms. This course covers k-means clustering, hierarchical clustering, and DBSCAN. Students will learn to implement these algorithms in Python, understand their applications, and
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