Machine Learning Tutorials

Machine Learning provides a comprehensive learning path for artificial intelligence and predictive modeling. Our tutorials cover a wide range of ML algorithms and techniques, suitable for beginners and intermediate data scientists. Through interactive labs and real - world code examples, you'll gain practical experience in building and training models. Our ML playground allows you to test different algorithms and datasets.

Pandas Selecting Data

Pandas Selecting Data

In this lab, you will learn the fundamental techniques for selecting and subsetting data from Pandas DataFrames, including selecting columns, rows, and specific slices of data.
Pandas
NumPy Data Types

NumPy Data Types

This lab will provide a step-by-step guide to understanding the different data types available in NumPy, and how to modify an array's data type. NumPy supports a wide range of numerical types, including booleans, integers, floating point numbers, and complex numbers. Understanding these data types is important for performing various numerical computations and data analysis tasks using NumPy.
NumPyPython
Pandas Reading External Data

Pandas Reading External Data

In this lab, you will learn the fundamentals of reading external data into a Pandas DataFrame. You will use the powerful `read_csv` function and its key parameters to handle various real-world CSV file formats.
Pandas
Matplotlib Subplots Creation

Matplotlib Subplots Creation

In this lab, you will learn how to create and customize multiple subplots in a single figure using Matplotlib, a powerful plotting library in Python. You will practice creating subplots, plotting data on them, and adjusting layouts.
Matplotlib
NumPy Structured Arrays

NumPy Structured Arrays

In this lab, we will learn about structured arrays in NumPy. Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields. They are useful for working with structured data, such as tabular data, where each field represents a different attribute of the data.
NumPyPython
NumPy Broadcasting

NumPy Broadcasting

Broadcasting is a powerful feature in NumPy that allows arrays with different shapes to be used in arithmetic operations. It provides a way to vectorize array operations and improve computational efficiency. This lab will guide you through the basics of broadcasting in NumPy.
NumPyPython
Pandas Sorting Data

Pandas Sorting Data

In this lab, you will learn the essential techniques for sorting data in a Pandas DataFrame. You'll explore sorting by single and multiple columns, controlling the sort order, and managing the DataFrame's index after sorting operations.
Pandas
NumPy Indexing on ndarrays

NumPy Indexing on ndarrays

In this lab, we will explore the basics of indexing in NumPy. Indexing allows us to access and manipulate specific elements or subsets of elements in an array. Understanding how to use indexing effectively is crucial for working with arrays in NumPy.
NumPyPython
Pandas Grouping and Aggregating

Pandas Grouping and Aggregating

In this lab, you will learn the fundamentals of data grouping and aggregation using the Pandas library. You'll practice using groupby() to create groups and apply various aggregation functions.
Pandas
Pandas Creating DataFrames

Pandas Creating DataFrames

In this lab, you will learn the fundamental ways to create Pandas DataFrames, including from dictionaries, and how to customize their columns and indexes.
Pandas
Pandas Descriptive Statistics

Pandas Descriptive Statistics

In this lab, you will learn how to compute various descriptive statistics for a Pandas DataFrame, including mean, median, min/max, and more.
Pandas
NumPy Universal Functions

NumPy Universal Functions

In this lab, we will explore the basics of NumPy Universal Functions (ufuncs). Ufuncs are functions that operate on ndarrays in an element-by-element fashion, supporting array broadcasting, type casting, and other standard features. We will learn about the different methods of ufuncs, broadcasting rules, type casting rules, and how to override ufunc behavior.
NumPyPython
Scikit-learn Cross-Validation

Scikit-learn Cross-Validation

In this lab, you will learn how to perform cross-validation using scikit-learn to evaluate the performance of a machine learning model more robustly.
scikit-learn
Pandas Filtering Data

Pandas Filtering Data

In this lab, you will learn the fundamental techniques for filtering data in Pandas DataFrames, including boolean indexing, combining conditions, using isin, and handling missing values.
Pandas
Pandas Basic Data Cleaning

Pandas Basic Data Cleaning

In this lab, you will learn the fundamental techniques for cleaning data using the Pandas library, including handling missing values, removing duplicates, and correcting data types.
Pandas
NumPy IO Genfromtxt

NumPy IO Genfromtxt

In this lab, we will learn how to import data using the numpy.genfromtxt function. This function allows us to read tabular data from various sources and convert it into NumPy arrays. We will explore different options for defining the input, splitting the lines into columns, choosing columns, setting the data type, and tweaking the conversion.
NumPyPython
Pandas Introduction and Setup

Pandas Introduction and Setup

In this lab, you will get started with Pandas, a powerful data analysis library in Python. You will learn how to verify its installation, import it, create a basic Series, access its elements, and inspect its properties.
Pandas
NumPy Array Creation

NumPy Array Creation

This lab provides a step-by-step guide on how to create arrays using NumPy, a fundamental library for array containers in Python. You will learn different methods for array creation, including converting Python sequences, using intrinsic NumPy array creation functions, replicating and joining existing arrays, and reading arrays from disk.
NumPyPython
  • Prev
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • ...
  • 154
  • Next