GPT 3.5 4- Data Analysis and Machine Learning in Python

GPT 3.5 & 4: Data Analysis and Machine Learning in Python, available at $84.99, has an average rating of 3.95, with 76 lectures, 40 quizzes, based on 40 reviews, and has 1102 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, Classifications, Random Forest, Decision Tree, SVM, and KNN, etc. Acquire skills in unsupervised machine learning techniques, including KMeans for effective cluster analysis and pattern recognition. Develop the ability to measure and evaluate the accuracy and performance of machine learning models, enabling decisions on model selection and optimization. Apply acquired knowledge to real-world scenarios, solving diverse machine learning challenges and developing solutions. Learn to efficiently prepare and clean datasets using GPT-4, including handling missing data, outliers, and data type conversions. Master the use of GPT-4 for advanced data manipulation tasks, such as merging datasets, creating pivot tables, and applying conditional logic. Develop skills to utilize GPT-4 for creating and interpreting a variety of data visualizations, such as histograms, scatter plots, and line graphs. Learn to apply GPT-4 for predictive analytics, including random forest regressor and other machine learning models. Acquire the ability to automate repetitive data analysis tasks using GPT-4, enhancing efficiency and productivity. This course is ideal for individuals who are Python Enthusiasts enhance their programming with AI or Data Science aspirants looking for hands-on course or Complete Beginners wants to learn machine learning easiest way or Anyone wants to simplify and fasten data analysis workflow with ChatGPT It is particularly useful for Python Enthusiasts enhance their programming with AI or Data Science aspirants looking for hands-on course or Complete Beginners wants to learn machine learning easiest way or Anyone wants to simplify and fasten data analysis workflow with ChatGPT.

Enroll now: GPT 3.5 & 4: Data Analysis and Machine Learning in Python

Summary

Title: GPT 3.5 & 4: Data Analysis and Machine Learning in Python

Price: $84.99

Average Rating: 3.95

Number of Lectures: 76

Number of Quizzes: 40

Number of Published Lectures: 76

Number of Published Quizzes: 40

Number of Curriculum Items: 116

Number of Published Curriculum Objects: 116

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, Classifications, Random Forest, Decision Tree, SVM, and KNN, etc.

Acquire skills in unsupervised machine learning techniques, including KMeans for effective cluster analysis and pattern recognition.

Develop the ability to measure and evaluate the accuracy and performance of machine learning models, enabling decisions on model selection and optimization.

Apply acquired knowledge to real-world scenarios, solving diverse machine learning challenges and developing solutions.

Learn to efficiently prepare and clean datasets using GPT-4, including handling missing data, outliers, and data type conversions.

Master the use of GPT-4 for advanced data manipulation tasks, such as merging datasets, creating pivot tables, and applying conditional logic.

Develop skills to utilize GPT-4 for creating and interpreting a variety of data visualizations, such as histograms, scatter plots, and line graphs.

Learn to apply GPT-4 for predictive analytics, including random forest regressor and other machine learning models.

Acquire the ability to automate repetitive data analysis tasks using GPT-4, enhancing efficiency and productivity.

Who Should Attend

Python Enthusiasts enhance their programming with AI

Data Science aspirants looking for hands-on course

Complete Beginners wants to learn machine learning easiest way

Anyone wants to simplify and fasten data analysis workflow with ChatGPT

Target Audiences

Python Enthusiasts enhance their programming with AI

Data Science aspirants looking for hands-on course

Complete Beginners wants to learn machine learning easiest way

Anyone wants to simplify and fasten data analysis workflow with ChatGPT

Accelerate your journey to mastering data analysis and machine learning with our dynamic course: “Data Analysis and Machine Learning: Python + GPT 3.5 & GPT 4”. Immerse yourself in a comprehensive curriculum that seamlessly integrates essential tools such as Pandas, Numpy, Seaborn, Scikit-learn, Python, and the innovative capabilities of ChatGPT.

Embark on an immersive learning experience designed to guide you through every facet of the machine-learning process. From data cleaning and manipulation to preprocessing and model development, you’ll traverse each stage with precision and confidence.

Dive deep into hands-on tutorials where you’ll gain proficiency in crafting supervised models, including but not limited to Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XGBoost, and KNN.Explore the realm of unsupervised models with techniques like KMeans and DBSCAN for cluster analysis.

Our strategic course structure ensures swift comprehension of complex concepts, empowering you to navigate through machine learning tasks effortlessly. Engage in practical exercises that not only solidify theoretical foundations but also enhance your practical skills in model building.

Measure the accuracy and performance of your models with precision, enabling you to make informed decisions and select the most suitable models for your specific use case. Beyond analysis, learn to create compelling data visualizations and automate repetitive tasks, significantly boosting your productivity.

By the course’s conclusion, you’ll possess a robust foundation in leveraging GPT-4 for data analysis, equipped with practical skills ready to be applied in real-world scenarios. Whether you’re a novice eager to explore machine learning or a seasoned professional seeking to expand your skill set, our course caters to all levels of expertise.

Join us on this transformative learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world data analysis and machine learning challenges head-on with python and GPT. Fast-track your path to becoming a proficient data analysis and machine learning practitioner with our dynamic and comprehensive course.

Course Curriculum

Chapter 1: Setting Up Your Analysis Environment

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting up ChatGPT and GPT 4

Lecture 3: Download Practice datasets

Lecture 4: Get special handboks

Chapter 2: Data Analysis and Its Workflow

Lecture 1: Data Analysis and Its Characteristics

Lecture 2: Complete data analysis workflow

Chapter 3: Statistical Analysis and Its Workflow

Lecture 1: Statistical Analysis and Its Characteristics

Lecture 2: Confidence level, significance level and P-value

Lecture 3: Complete hypothesis testing workflow

Chapter 4: Machine Learning and Its Workflow

Lecture 1: Machine Learning and Its Characteristics

Lecture 2: Complete Machine Learning Work-flow

Chapter 5: Python Programming Basics Level 1

Lecture 1: Your First Python Code

Lecture 2: Variables and naming conventions

Lecture 3: Data types: integers, float, strings, boolean

Lecture 4: Type conversion and casting

Lecture 5: Arithmetic operators (+, -, *, /, %, **)

Lecture 6: Comparison operators (>, =, <=, ==, !=)

Lecture 7: Logical operators (and, or, not)

Chapter 6: Python Programming Basics Level 2

Lecture 1: Lists: creation, indexing, slicing, modifying

Lecture 2: Sets: unique elements, operations

Lecture 3: Dictionaries: key-value pairs, methods

Lecture 4: Conditional statements (if, elif, else)

Lecture 5: Logical expressions in conditions

Lecture 6: Looping structures (for loops, while loops)

Lecture 7: Defining, Creating and Calling functions

Chapter 7: Python + GPT 3.5 – Learn Data Cleaning

Lecture 1: Loading dataset

Lecture 2: Handling missing values

Lecture 3: Deal with inconsistent data

Lecture 4: Dealing with miss-identified data types

Lecture 5: Dealing with duplicated data

Chapter 8: Python + GPT 3.5 – Learn Data Manipulation

Lecture 1: Sorting and arranging dataset

Lecture 2: Filter data based on conditions

Lecture 3: Merging or adding variables

Lecture 4: Concatenating extra data

Chapter 9: Python + GPT 3.5 – Learn Data Preprocessing

Lecture 1: Feature engineering

Lecture 2: Extracting day, months, year

Lecture 3: Feature encoding

Lecture 4: Creating dummy variables

Lecture 5: Data normalizing

Lecture 6: Splitting data

Chapter 10: Python + GPT 3.5 – Learn Regressor Machine Learning

Lecture 1: Linear regression ML model

Lecture 2: Decision Tree regression ML model

Lecture 3: Random Forest regression ML model

Lecture 4: Support Vector regression ML model

Chapter 11: Python + GPT 3.5 – Learn Classification Machine Learning

Lecture 1: Logistic Regression ML model

Lecture 2: Decision Tree classification ML model

Lecture 3: Random Forest classification ML model

Lecture 4: K Nearest Neighbours classification ML model

Chapter 12: Python + GPT 3.5 – Learn Clustering Machine Learning

Lecture 1: KMeans Clustering ML model

Instructors

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

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 2 votes

5 stars: 35 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?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!