Machine Learning with Python

Machine Learning is broad and fast-growing sub-field of Artificial Intelligence. This course introduces students to the basic concepts and techniques of Machine Learning that make students to more skilled.

Getting Started

Welcome to the course

What will you get through these courses?

Python fundamentals

Introduction to Python

Data Structures in Python

Python operators

Loops (for, while)

Functions

Web scraping

Regular expressions

Writing and reading files

Introduction to OOP

Foundational math, probability, statistics

Introduction to Linear Algebra

Numpy arrays

Vectors and Vector Arithmetic

Matrices and Matrix Arithmetic

Types of Matrices

Matrix Operations

Basics of Probability Theory

Joint, Marginal, and Conditional Probability

Bayes Theorem

Introduction to Statistics

Descriptive Statistics

Overview of statistical concepts

Statistical hypothesis testing

Wrap-Up

Basic SQL commands for Machine Learning

Introduction to SQL

Querying and filtering data in SQL

Simple aggregations in SQL

Grouping, filtering, and sorting

Feature Engineering I

pandas

Variable Problems When Building ML Models

Performing mean or median imputation

Mode or frequent category imputation

Adding a missing value indicator variable

Performing multivariate imputation by chained equations

Assembling an imputation pipeline

Creating binary variables through one-hot encoding

Replacing categories with counts or frequency of observations

Encoding with the mean of the target

Encoding with the Weight of Evidence

Grouping rare or infrequent categories

Performing binary encoding, feature hashing

Feature Engineering II

Transforming variables with the logarithm

Transforming variables with the reciprocal function

Using square and cube root to transform variables

Using power transformations on numerical variables

Performing Box-Cox transformation on numerical variables

Performing Yeo-Johnson transformation on numerical variables

Dividing the variable into intervals of equal width

Sorting the variable values in intervals of equal frequency

Performing discretization followed by categorical encoding

Allocating the variable values in arbitrary intervals

Performing discretization with k-means clustering

Using decision trees for discretization

Trimming outliers from the dataset

Performing winsorization

Capping the variable at arbitrary maximum and minimum values

Performing zero-coding – capping the variable values at zero

Extracting date and time parts from a datetime variable

Capturing the elapsed time between datetime variables

Standardizing the features

Performing mean normalization

Implementing maximum absolute scaling

Scaling to vector unit length

Feature Engineering III

Combining multiple features with statistical operations

Combining pairs of features with mathematical functions

Performing polynomial expansion

Deriving new features with decision trees

Aggregating transactions with mathematical operations

Aggregating transactions in a time window

Determining the number of local maxima and minima

Deriving time elapsed between time-stamped events

Creating features from transactions with Featuretools

Machine Learning

Data Preparation without Data Leakage

Bias–variance tradeoff

Building Simple Linear model from stratch

Building Simple Logistic Regression model from stratch

Classification and Regression Trees

K-Nearest Neighbors

Support Vector Machines

Unsupervised Learning

Semi-supervised Learning algorithms

Handling with imbalanced dataset

Feature selection methods

Hyperparameter optimization (random search, grid search, halving)

Hyperparameter optimization (Bayesian)

© 2023 Nijat Zeynalov