Hands-on Machine Learning with Python & ChatGPT

Name: Hands-on Machine Learning with Python & ChatGPT
Rating: 4.6 (59 reviews)

Hands-on Machine Learning Tutorial with Pandas, Numpy, Seaborn, Scikit-learn in Python and ChatGPT: A Complete Work-flow

Created byAnalytix AI

Last updated 8/2024

English

What you'll learn

Learn to proficiently use Python for various machine learning tasks, including data cleaning, manipulation, preprocessing, and model development.
Gain expertise in building and implementing supervised machine learning models: Regressions, Random Forest, Decision Tree, SVM, XGBoost, and KNN, etc.
Acquire skills in unsupervised machine learning techniques, including KMeans for effective cluster analysis and pattern recognition.
Learn to create a streamlined and efficient workflow for building machine learning models from scratch, incorporating both Python and ChatGPT.
Develop the ability to measure and evaluate the accuracy and performance of machine learning models, enabling decisions on model selection and optimization.
Explore the integration of ChatGPT into the machine learning workflow, leveraging its capabilities for enhanced data analysis, and generating insights.
Understand strategies for selecting the most suitable machine learning model for a given task, considering factors such as accuracy, and scalability.
Apply acquired knowledge to real-world scenarios, solving diverse machine learning challenges and developing solutions.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

10 sections • 45 lectures • 4h 46m total length

Install Python and Jupyter Notebook0:07
Setting Up ChatGPT for Easy Machine Learning0:06
Get special handbooks0:18

Load your dataset into Python environment7:06
Handling missing values with Scikit-learn12:43
Identify and impute missing values in a dataframe using pandas and scikit-learn, employing isnull and simple imputer with median for numeric data and the most frequent value for categorical data.
Identifying missing values
Imputing missing values with SimpleImputer
Identify and deal with inconsistent data11:17
Removing inconsistent value
Dealing with miss-identified data types7:43
Assigning correct data types
Address and remove duplicated data4:22
Dropping duplicated values
QUIZ 1: Data Cleaning
Solution 1: Data Cleaning0:03

Sorting and arranging dataset5:17
Sorting dataset
Filter data based on conditions10:34
Filtering dataset
Filtering dataset 2
Filtering dataset 3
Filtering dataset 4
Merging or adding of supplementary variables3:47
Merging dataframes
Concatenating or adding of supplementary data3:47
Learn to concatenate supplementary data vertically with pandas pd.concat, merging masked data with extra rows from extra_data.xls to expand the final dataframe.
Concatenating dataframe
QUIZ 2: Data Manipulation
Solution 2: Data Manipulation0:03

Feature engineering: Generating new data16:29
Creating new variable
Extracting day, months, year from date variable4:24
Extracting day, month and year
Feature encoding: Assigning numeric values5:31
Assigning numeric variable
Creating dummy variables for nominal data7:14
Create dummy variables for nominal data using pandas get_dummies to convert country, category, and device type into 0/1 indicators for machine learning models.
Creating dummy variables
Data standardizing and normalizing with StandardScaler12:19
Normalizing variables
Splitting data into training and testing set6:53
Splitting datasets
QUIZ 3: Data Preprocessing
Solution 3: Data Preprocessing0:03

**Read It: IMPORTANT**0:23
Linear regression ML model18:18
Build linear regression model
Decision Tree regression ML model8:08
Build decision tree regression model
Random Forest regression ML model8:04
Build random forest regression model
Support Vector regression ML model6:26
Explore building a support vector regression model to predict refund amounts, compare it with linear regression, decision tree, and random forest using mean absolute percentage error, and observe SVR performance.
Build support vector regression model
XGBoost regression ML model7:37
QUIZ 4: ML Model Application Part 1
Solution 4: ML Model Application Part 10:03

**Read It: IMPORTANT**0:14
Logistic Regression ML model21:44
Build logistic regression model
Decision Tree classification ML model13:12
Build decision tree classification model
Random Forest classification ML model11:50
Build random forest classification model
K Nearest Neighbours classification ML model20:30
Explore k nearest neighbors classification to predict customer type (regular vs loyal), compare with random forest, test multiple k values, and visualize accuracy and confusion matrices.
Build KNN classification model
LightGBM classification ML model13:36
apply Lightgbm to classify customer type as regular or loyal in a binary model, train on data, predict on test, and assess with confusion matrix, accuracy, and error rate.
QUIZ 5: ML Model Application Part 2
Solution 5: ML Model Application Part 20:03

Requirements

No coding Experience is Needed.
Desktop/Laptop

Description

Unlock the fast track to machine learning mastery with our comprehensive course, "Hands-on Machine Learning in Python & ChatGPT." Dive deep into hands-on tutorials utilizing essential tools like Pandas, Numpy, Seaborn, Scikit-learn, Python, and the innovative capabilities of ChatGPT.

This course is designed to guide you seamlessly through every stage of the machine learning process, ensuring a complete workflow that empowers you to tackle tasks such as data cleaning, manipulation, preprocessing, and the development of powerful supervised and unsupervised machine learning models.

In this immersive learning experience, gain proficiency in crafting supervised models, including Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XGBoost, and KNN. Unleash the power of unsupervised models like KMeans and DBSCAN for cluster analysis. The course is strategically structured to enable you to navigate through these complex concepts swiftly, effortlessly, and with precision.

Our primary objective is to equip you with the skills to build machine learning models from scratch, leveraging the combined strength of Python and ChatGPT. You will not only learn the theoretical foundations but also engage in practical exercises that solidify your understanding. By the end of the course, you'll have the expertise to measure the accuracy and performance of your machine learning models, enabling you to make informed decisions and select the best models for your specific use case.

Whether you are a beginner eager to enter the world of machine learning or an experienced professional looking to enhance your skill set, this course caters to all levels of expertise. Join us on this learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world machine learning challenges head-on. Fast-track your way to becoming a proficient machine learning practitioner with our dynamic and comprehensive course.

Who this course is for:

Python Enthusiasts
Data Science Aspirants
Complete Beginners

Hands-on Machine Learning with Python & ChatGPT

What you'll learn

Explore related topics

Coding Exercises

Course content

Setting Up Your Data Analysis Platform3 lectures • 1min

What is Machine Learning?4 lectures • 7min

Master Data Cleaning for Error-free ML Model6 lectures • 43min

Master Data Manipulation for Strong ML Model5 lectures • 23min

Master Data Preprocessing for Perfect ML Model7 lectures • 53min

Hands-on Machine Learning Application Part 1: Regression7 lectures • 49min

Hands-on Machine Learning Application Part 2: Classification7 lectures • 1hr 21min

Hands-on Machine Learning Application Part 3: Clustering3 lectures • 22min

Your Next Journey of Learning1 lecture • 1min

Tips, Tricks and Resources2 lectures • 7min

Requirements

Description

Who this course is for: