Machine Learning Classification Bootcamp in Python, Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, Mitchell Bouchard, SuperDataScience Team
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What you'll learn
- Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews
- Understand the theory and intuition behind several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
- Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
- Build an e-mail spam classifier using Naive Bayes classification Technique
- Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification
- Develop Models to predict customer behavior towards targeted Facebook Ads
- Classify data using K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
- Build an in-store feature to predict customer's size using their features
- Develop a fraud detection classifier using Machine Learning Techniques
- Master Python Seaborn library for statistical plots
- Understand the difference between Machine Learning, Deep Learning and Artificial Intelligence
- Perform feature engineering and clean your training and testing data to remove outliers
- Master Python and Scikit-Learn for Data Science and Machine Learning
- Learn to use Python Matplotlib library for data Plotting
