Ementorhub

Skills you'll gain

Data Cleansing

Unsupervised Learning

Data Validation

Data Transformation

Verification And Validation

Supervised Learning

Data Quality

Exploratory Data Analysis

Feature Engineering

Applied Machine Learning

Machine Learning

Machine Learning Algorithms

Data Processing

There are 4 modules in this course

Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

Preparing your Data for Machine Learning Success

Feature Engineering for MORE Fun & Profit

Bad Data