Python Machine Learning Immersive

This skillset is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.

This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You'll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.

The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as as Numpy and Pandas libraries.

Prerequisite: This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course. 

What You’ll Learn:

• How to clean and balance your data using the Pandas library
• Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
• Choosing good features to use as input for your algorithms
• Properly splitting data into training, test and cross-validation sets
• Important theoretical concepts like overfitting, variance and bias
• Evaluating the performance of your machine learning models

Course Syllabus

Fundamentals

Basic Regression Analysis

• Linear Regression
• Mean squared error
• Training set vs Test set
• Cross validation

Advanced Regression Analysis

• Multi-linear regression
• Feature engineering
• Overfitting

Classification

Logistic Regression

• Regression vs Classification
• Logistic Regression
• Sigmoid function

K-nearest Neighbors

• K-nearest neighbors
• Model-based vs memory-based
• Parametric vs non-parametric
• Evaluating performance

Decision Trees

Decision Trees

• Decision tree
• Interpretability
• Bias-variance tradeoff

Random forest

• Random forest
• Ensemble methods
  
• Hyperparameters

Learn more about Python Machine Learning Immersive at Practical Programming.