Machine learning in Python with Google Colab

Name: Machine learning in Python with Google Colab
Rating: 4.4 (8 reviews)

Machine Learning with Statistical Analysis and Data Visualization in Python using Google Colab

New

Created byMd Ahshanul Haque

Last updated 5/2026

English

What you'll learn

Prepare, clean, and explore datasets for machine learning
Learn Python basics for machine learning using Google Colab
Build practical machine learning models step by step in Python
Evaluate model performance using accuracy, confusion matrix, and other metrics

Course content

7 sections • 30 lectures • 1h 59m total length

Run an Example Python Code in Google Colab1:24
Open Google Colab and Run Your First Python Command3:45
In this lecture, you will learn how to open Google Colab, create a new notebook, and run your first Python command. This will help you start coding in Python without installing any software.
Reopen Google Colab and Continue Your Notebook1:46
In this lecture, you will learn how to save, close, and reopen your Google Colab notebook. You will also learn how to continue your Python practice after closing the browser or returning later.
Add Headings and Subheadings Using Text Cells1:19
In this lecture, you will learn how to use text cells in Google Colab to add headings, subheadings, and short notes. This will help you organize your notebook clearly and make your work easier to follow.
Variables and Data Types in Python5:48
Download Course Data and Code Files0:32

Open a Dataset from a Folder from PC7:39
In this lecture, you will learn how to upload a dataset from your computer to Google Colab and open it using Pandas. This will help you start working with real data in Python.
Connect Google Drive for Importing Data from Google Drive2:38
In this lecture, you will learn how to connect Google Drive with Google Colab and import data directly from your Drive. This will help you access saved datasets easily and use them in Python with Pandas
Import Data into Colab from Google Drive7:24
In this lecture, you will learn how to import datasets from Google Drive into Google Colab after connecting your Drive. This will help you access saved files easily and load them into Python for data analysis using Pandas.
Basic Tasks After Opening Data in Python6:18
In this lecture, you will learn basic tasks after opening a dataset in Python, such as viewing rows and columns, checking data types, finding missing values, and understanding the structure of the data using Pandas.

Box Plot for Continuous Data | Do Not Just Run the Code: Learn It1:09
I request you to learn and understand all the Python code carefully. Do not just copy and run the code. Try to understand what each line does. If you understand the code step by step, you will be able to create box plots with any dataset by yourself.
Variables for Boxplot in Python5:19
In this lecture, you will learn which variables are needed to create a boxplot in Python. You will understand how to choose a continuous variable and a grouping variable for visualization.
Python Code for Boxplot3:52
In this lecture, you will learn the basic Python code for creating a boxplot. You will practice writing simple code to visualize data distribution clearly.
Boxplot Formatting in Python6:24
In this lecture, you will learn how to format a boxplot by changing size, style, colors, and labels to make the graph clearer and more professional.
Set Axis and Legend Labels for Boxplot10:56
In this lecture, you will learn how to add and customize axis labels, titles, and legend labels for a boxplot in Python.

Variable Preparation for Descriptive Summary5:31
In this lecture, you will learn how to prepare variables for descriptive summary analysis in Python. You will organize categorical and continuous variables before creating summary tables.
Univariate Analysis in Python3:39
In this lecture, you will learn how to summarize one variable at a time using Python. You will explore frequencies, percentages, means, medians, and other basic descriptive statistics.

What is Machine Learning?2:27
In this lecture, you will learn the basic meaning of machine learning, how it works, and why it is useful for data analysis and prediction.
Data Preparation for Machine Learning2:07
In this lecture, you will learn how to prepare data before building a machine learning model, including selecting variables and organizing the dataset.
Data Preprocessing for Machine Learning1:41
In this lecture, you will learn how to preprocess data for machine learning using steps such as handling missing values, encoding categorical variables, and scaling continuous variables.

Python Libraries for Machine Learning Dtata management4:52
Learn how to import the main Python libraries needed for machine learning, data management, and preprocessing.
Define Class and Feature Variables in Python for machine learning5:43
Learn how to define the outcome/class variable and select feature variables for building a machine learning model.
Drop Missing Values3:50
Learn how to identify and remove missing values from your dataset before machine learning analysis.
Prepare the Class and Features1:29
Learn how to organize your target variable and input features so they are ready for model training.
Create Training and Testing Data1:54
Learn how to split your dataset into training and testing sets using a 70% and 30% approach.
Data Preprocessing in Python3:48
Learn how to preprocess data using scaling, encoding, and other preparation steps before applying machine learning models.

Libraries for Classification Models in Python1:47
In this lecture, you will learn how to import the main Python libraries needed for classification models, including tools for model building, prediction, and evaluation.
Logistic Regression Classifier in Python5:17
In this lecture, you will learn how to build a logistic regression classification model in Python. You will train the model, make predictions, and prepare results for model evaluation.
Random Forest Classifier in Python3:43
In this lecture, you will learn how to build a Random Forest classification model in Python. You will train the model, make predictions, and evaluate its performance using accuracy, precision, sensitivity, specificity, F1-score, and AUC.
Build Multiple Classification Models in Python5:01
In this lecture, you will learn how to build and compare multiple classification models in Python, including Logistic Regression, Decision Tree, KNN, SVM, and Random Forest. You will train each model, make predictions, and prepare performance results for comparison.

Requirements

Basic computer knowledge is enough. No previous machine learning experience is required.

Description

Learn Machine Learning in Python with Google Colab in a simple, practical, and beginner-friendly way. This course is designed for students, researchers, data analysts, public health professionals, business learners, and anyone who wants to learn Python, data science, statistical analysis, data visualization, and machine learning without installing any software.

You will start by learning how to use Google Colab to write and run Python code online. You will learn how to create notebooks, save your work, connect Google Drive, import datasets, and manage files easily. Then you will learn basic Python concepts such as variables, data types, and simple coding tasks.

The course also covers data handling with Pandas, including how to open datasets, view rows and columns, check data types, find missing values, summarize data, and prepare data for analysis. These skills are important for data cleaning, data analysis, research data management, business analytics, and machine learning projects.

You will also learn beginner-level statistical analysis and data visualization in Python to understand patterns, trends, and relationships in data. Finally, you will learn core machine learning concepts, including features, target variables, training data, testing data, classification, regression, model building, and model evaluation.

By the end of this course, you will be able to use Python with Google Colab for practical data analysis, statistical analysis, data visualization, and machine learning projects with confidence.

Who this course is for:

Beginners who want to learn machine learning in Python
Students, researchers, and professionals who want practical machine learning skills

Machine learning in Python with Google Colab

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 15min

Data Handling with Pandas in Python4 lectures • 24min

Data Visualization in Python5 lectures • 28min

Data summary descriptive and inferential statistic2 lectures • 9min

Introduction to Machine Learning and Data Preparation3 lectures • 6min

Data Management for Machine Learning in Python6 lectures • 22min

Classification Models in Python - LR, DT, KNN, SVM, RF | 80% /20% Training Test4 lectures • 16min

Requirements

Description

Who this course is for: