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
Best-Selling Professor, 400K+ students, 250K+ YT Subs

Created by: SuperDataScience Team
Helping Data Scientists Succeed

Created by: Ligency Team
Helping Data Scientists Succeed

What Will I 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
• Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, (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

Requirements

• Basic knowledge of Python Programming
• Experienced computer user

Target audience

• Data Science Enthusiasts wanting to enhance their machine learning skills
• Python programmers curious about Machine Learning and Data Science
• Programmers or developers who want to make a shift into the lucrative data science and machine learning career path
• Technologists wanting to gain an understanding of how machine learning models work
• Data analysts who want to transition into the Tech industry