Course Name: Machine Learning With Python Power Bootcamp

Course Duration: 5 Days

Prerequisite: Python, Data Science With Python

Course Index

Day 1:

Module 1: Introduction to Machine Learning

• -What is Machine Learning
• -What is Deep Learning
• -What is Artificial Intelligence
• -What is the relation between Data Science and Machine Learning ?
• -What is the relation between Machine Learning and Deep Learning ?
• -What is the difference between Teaching Machine and Machine Learning ?
• -Types Of Machine Learning: Supervised Learning, UnSupervised Learning, Reinforcement Learning

Module 2: Classification:

• -Heart Atttack Prediction With Keras and TensorFlow

Day 2: Regressions:

Module 3: Regressions:

• -What is Intercept ?
• -What is Slope ?
• -What is R as Regression Coefficiency ?
• -What is P Hypothesis ?
• -What is Standart Error ?
• -Linear Regression: SciPy Library Exmples of University Graduation Notes
• -Where and How Can We Use Linear Regression ?
• -Where and Why We can not use Linear Regression ?
• -Polnynomial Regression: SciKitLearn Library: Speed in Traffic According to Time of Day
• -Polnynomial Regression: SciKitLearn Library: IQ and Age Corelation
• -Multiple Regression: The Effect of Car Model, Engine's Wegiht and Engine's Kg into CO2 production

Module 4: Bias-Variance Trade-off

Day 3:

Module 5: Dimensionality Reduction

Module 6: Cross Validation

Module 7: Training Models

Day 4:

Module 8: Logistic Regression

Module 9: K-Nearest Neighbors

Module 10: Support Vector Machines

Module 11: Decision Trees

Day 5:

Module 12: Random Forest

Module 13: Unsupervised Learning

Module 14: K-means Clustering