Hands-on Machine Learning with Python ChatGPT

Hands-on Machine Learning with Python & ChatGPT, available at $89.99, has an average rating of 4.4, with 46 lectures, 33 quizzes, based on 52 reviews, and has 1555 subscribers.

You will learn about 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. This course is ideal for individuals who are Python Enthusiasts or Data Science Aspirants or Complete Beginners It is particularly useful for Python Enthusiasts or Data Science Aspirants or Complete Beginners.

Enroll now: Hands-on Machine Learning with Python & ChatGPT

Summary

Title: Hands-on Machine Learning with Python & ChatGPT

Price: $89.99

Average Rating: 4.4

Number of Lectures: 46

Number of Quizzes: 33

Number of Published Lectures: 46

Number of Published Quizzes: 33

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Python Enthusiasts

Data Science Aspirants

Complete Beginners

Target Audiences

Python Enthusiasts

Data Science Aspirants

Complete Beginners

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.

Course Curriculum

Chapter 1: Setting Up Your Data Analysis Platform

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting Up ChatGPT for Easy Machine Learning

Lecture 3: Connect with my youtube channel

Lecture 4: Get special handbooks

Chapter 2: What is Machine Learning?

Lecture 1: Machine Learning and Its Characteristics

Lecture 2: Complete Machine Learning Work-flow

Lecture 3: Practice datasets

Lecture 4: Instructions for Quizzes: IMPORTANT

Chapter 3: Master Data Cleaning for Error-free ML Model

Lecture 1: Load your dataset into Python environment

Lecture 2: Handling missing values with Scikit-learn

Lecture 3: Identify and deal with inconsistent data

Lecture 4: Dealing with miss-identified data types

Lecture 5: Address and remove duplicated data

Lecture 6: Solution 1: Data Cleaning

Chapter 4: Master Data Manipulation for Strong ML Model

Lecture 1: Sorting and arranging dataset

Lecture 2: Filter data based on conditions

Lecture 3: Merging or adding of supplementary variables

Lecture 4: Concatenating or adding of supplementary data

Lecture 5: Solution 2: Data Manipulation

Chapter 5: Master Data Preprocessing for Perfect ML Model

Lecture 1: Feature engineering: Generating new data

Lecture 2: Extracting day, months, year from date variable

Lecture 3: Feature encoding: Assigning numeric values

Lecture 4: Creating dummy variables for nominal data

Lecture 5: Data standardizing and normalizing with StandardScaler

Lecture 6: Splitting data into training and testing set

Lecture 7: Solution 3: Data Preprocessing

Chapter 6: Hands-on Machine Learning Application Part 1: Regression

Lecture 1: **Read It: IMPORTANT**

Lecture 2: Linear regression ML model

Lecture 3: Decision Tree regression ML model

Lecture 4: Random Forest regression ML model

Lecture 5: Support Vector regression ML model

Lecture 6: XGBoost regression ML model

Lecture 7: Solution 4: ML Model Application Part 1

Chapter 7: Hands-on Machine Learning Application Part 2: Classification

Lecture 1: **Read It: IMPORTANT**

Lecture 2: Logistic Regression ML model

Lecture 3: Decision Tree classification ML model

Lecture 4: Random Forest classification ML model

Lecture 5: K Nearest Neighbours classification ML model

Lecture 6: LightGBM classification ML model

Lecture 7: Solution 5: ML Model Application Part 2

Chapter 8: Hands-on Machine Learning Application Part 3: Clustering

Lecture 1: KMeans Clustering ML model

Lecture 2: Final Solution: Fast-Track ML in Python & ChatGPT

Lecture 3: Utilize Python in real-world data analysis application

Chapter 9: Your Next Journey of Learning

Lecture 1: Resources for enhancing data analytics skill

Chapter 10: Tips, Tricks and Resources

Lecture 1: ChatGPT: Your best code companion

Lecture 2: Course resources

Instructors

Analytix AI
Unleashing the Power of Data with AI for Informed Insights.

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2 stars: 0 votes

3 stars: 3 votes

4 stars: 9 votes

5 stars: 40 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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