Embark on a comprehensive journey into the world of machine learning. This path starts with the fundamentals of Machine Learning using Sklearn and then progresses to advanced concepts in deep learning through Tensorflow.
Overview
Syllabus
- Data Cleaning and Preprocessing in Machine Learning
- Explore essential machine learning preparation using the Titanic Dataset. Gain skills in cleaning and preprocessing historical data with Python and Pandas, readying it for ML models and accurate analytics.
- Foundational Machine Learning Models with Sklearn
- Learn fundamental machine learning models with Sklearn, centered on the Iris Dataset. This course covers key algorithms like linear and logistic regression, and decision trees. Master implementation, evaluation, and optimization to pave the way for advanced machine learning concepts.
- Feature Engineering for Machine Learning
- Explore feature engineering using UCI's Abalone Dataset in this course. Enhance your skills in feature extraction, selection, and transformation to boost machine learning model performance. Learn to craft valuable features, apply different selection strategies, and use feature combinations to uncover data patterns.
- Intro to Model Optimization in Machine Learning
- Delve into machine learning fundamentals using the Wisconsin Breast Cancer Dataset. This course focuses on key ML techniques like data exploration, model tuning, and evaluation. Master hyperparameter tuning, regularization, and ensemble methods through practical exercises to boost your predictive models' accuracy and reliability.
- Introduction to Neural Networks with TensorFlow
- Start your exploration of neural networks with a beginner's course on TensorFlow, using the scikit-learn Digits Dataset. Learn neural network basics and deep learning by developing, training, and evaluating models with TensorFlow. Understand different neural network architectures and improve them, emphasizing the importance of data preparation in deep learning.
Courses
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Explore essential machine learning preparation using the Titanic Dataset. Gain skills in cleaning and preprocessing historical data with Python and Pandas, readying it for ML models and accurate analytics.
-
Learn fundamental machine learning models with Sklearn, centered on the Iris Dataset. This course covers key algorithms like linear and logistic regression, and decision trees. Master implementation, evaluation, and optimization to pave the way for advanced machine learning concepts.
-
Explore feature engineering using UCI's Abalone Dataset in this course. Enhance your skills in feature extraction, selection, and transformation to boost machine learning model performance. Learn to craft valuable features, apply different selection strategies, and use feature combinations to uncover data patterns.
-
Delve into machine learning fundamentals using the Wisconsin Breast Cancer Dataset. This course focuses on key ML techniques like data exploration, model tuning, and evaluation. Master hyperparameter tuning, regularization, and ensemble methods through practical exercises to boost your predictive models' accuracy and reliability.
-
Start your exploration of neural networks with a beginner's course on TensorFlow, using the scikit-learn Digits Dataset. Learn neural network basics and deep learning by developing, training, and evaluating models with TensorFlow. Understand different neural network architectures and improve them, emphasizing the importance of data preparation in deep learning.
