Machine Learning in Python: From Zero to Hero in 10 Hours

Machine Learning & Data Science in Python with real life projects. Source codes included.

Created by: Sanjay Singh
Data and Machine Learning Professional

What Will I Learn?

• Hands-on explanation of every major Machine Learning techniques.
• Model Development, Deployment and Monitoring.
• Regression: Simple, Polynomial, and Multinomial
• Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes
• Ensemble Modeling: Voting Classifier, Bagging, Boosting, Stacking, Random Forest
• Implementation of every concepts explained in the course. Source codes are made available to you for your use.
• Data Visualization with MatPlotLib and Seaborn
• Use train, test and Cross Validation to choose and tune data
• Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
• Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
• Understand how to implement Machine Learning at massive scale
• Understand math and statistics behind Machine Learning models

Requirements

• To be able to operate computer
• A lot of curiosity!
• Some knowledge of Python programming and high school level math will be an asset

Target audience

• Students and professionals who want to become Machine Learning Expert or Data Scientist.
• IT Professionals, Mathematicians, Statisticians.
• Machine learning enthusiasts.
• Project Managers, Data Analytics, and Business Intelligence Professionals.
• Python developers.