Python ChatGPT for A-Z Data Science and Machine Learning

Python & ChatGPT for A-Z Data Science and Machine Learning, available at $64.99, has an average rating of 4.85, with 80 lectures, 60 quizzes, based on 37 reviews, and has 3222 subscribers.

You will learn about Leverage ChatGPT for generating exact python code required for each tasks of data analysis and data science workflow and even for machine learning. Acquire the skills to clean raw data effectively, covering techniques for handling missing values, addressing different data types, and managing outliers etc. Master data manipulation by learning essential techniques such as sorting, filtering, merging, concatenating, and others using Pythons pandas library. Learn exploratory data analysis techniques include frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships. Dive into the world of data preprocessing with hands-on experience in feature engineering, selection, and scaling to prepare datasets for ML models. Apply your knowledge through a series of practical projects, reinforcing your understanding of each step in the data science workflow. Develop expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more. Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm. Explore unsupervised learning by understanding and implementing clustering models like KMeans for uncovering hidden patterns in data. Learn Pythons syntax, data types, variables, and operators to construct simple programs and execute basic functions. Become proficient in using essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy. Test your knowledge and reinforce your learning through a series of seven-layered quizzes that cover various aspects of the data science workflow. Learn to regulate program flow, use loops and conditional statements like if, elif, and else. Experience the integration of ChatGPT to rise your understanding of data science applications through interactive conversations and real-world problem-solving. Acquire skills?to use Python lists, dictionaries, tuples, and sets. This course is ideal for individuals who are Beginners Data Scientists or Anyone Curious About Data Science or Python and Data Enthusiast It is particularly useful for Beginners Data Scientists or Anyone Curious About Data Science or Python and Data Enthusiast.

Enroll now: Python & ChatGPT for A-Z Data Science and Machine Learning

Summary

Title: Python & ChatGPT for A-Z Data Science and Machine Learning

Price: $64.99

Average Rating: 4.85

Number of Lectures: 80

Number of Quizzes: 60

Number of Published Lectures: 80

Number of Published Quizzes: 60

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 140

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Leverage ChatGPT for generating exact python code required for each tasks of data analysis and data science workflow and even for machine learning.

Acquire the skills to clean raw data effectively, covering techniques for handling missing values, addressing different data types, and managing outliers etc.

Master data manipulation by learning essential techniques such as sorting, filtering, merging, concatenating, and others using Pythons pandas library.

Learn exploratory data analysis techniques include frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships.

Dive into the world of data preprocessing with hands-on experience in feature engineering, selection, and scaling to prepare datasets for ML models.

Apply your knowledge through a series of practical projects, reinforcing your understanding of each step in the data science workflow.

Develop expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more.

Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm.

Explore unsupervised learning by understanding and implementing clustering models like KMeans for uncovering hidden patterns in data.

Learn Pythons syntax, data types, variables, and operators to construct simple programs and execute basic functions.

Become proficient in using essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy.

Test your knowledge and reinforce your learning through a series of seven-layered quizzes that cover various aspects of the data science workflow.

Learn to regulate program flow, use loops and conditional statements like if, elif, and else.

Experience the integration of ChatGPT to rise your understanding of data science applications through interactive conversations and real-world problem-solving.

Acquire skills?to use Python lists, dictionaries, tuples, and sets.

Who Should Attend

Beginners Data Scientists

Anyone Curious About Data Science

Python and Data Enthusiast

Target Audiences

Beginners Data Scientists

Anyone Curious About Data Science

Python and Data Enthusiast

Embark on a comprehensive journey through the fascinating realm of data science and machine learning with our course, “Data Science and Machine Learning with Python and GPT 3.5.” This course is meticulously designed to equip learners with the essential skills required to excel in the dynamic fields of data science and machine learning.

Throughout this immersive learning experience, you will delve deep into the core concepts of data science and machine learning, leveraging the power of Python programming alongside the cutting-edge capabilities of ChatGPT 3.5. Our course empowers you to seamlessly navigate the entire data science workflow, from data acquisition and cleaning to exploratory data analysis and model deployment.

You will master the art of cleaning raw data effectively, employing techniques tailored to handle missing values, diverse data types, and outliers, thus ensuring the integrity and quality of your datasets. Through hands-on exercises, you will become proficient in data manipulation using Python’s pandas library, mastering essential techniques such as sorting, filtering, merging, and concatenating.

Exploratory data analysis techniques will be thoroughly explored, empowering you to uncover valuable insights through frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships. Additionally, you will gain practical experience in data preprocessing, honing your skills in feature engineering, selection, and scaling to optimize datasets for machine learning models.

The course curriculum features a series of engaging projects designed to reinforce your understanding of key data science and machine learning concepts. You will develop expertise in building and evaluating supervised regression and classification models, utilizing a diverse array of algorithms including linear regression, random forest, decision tree, xgboost, logistic regression, KNN, lightgbm, and more.

Unsupervised learning techniques will also be explored, enabling you to uncover hidden patterns within data through the implementation of clustering models like KMeans and DBSCAN. Throughout the course, you will familiarize yourself with Python syntax, data types, variables, and operators, empowering you to construct robust programs and execute fundamental functions seamlessly.

Essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy, will be extensively utilized, enabling you to tackle real-world challenges with confidence. Interactive quizzes, integrated seamlessly with ChatGPT, will test your knowledge and reinforce your learning across various aspects of the data science workflow.

By the conclusion of this transformative course, you will possess the requisite skills to communicate your findings effectively, translating complex data science results into clear and actionable insights for stakeholders. Join us on this exhilarating journey and unlock the boundless potential of data science and machine learning today!

Course Curriculum

Chapter 1: Setting Up Your Data Analysis Platform

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting Up ChatGPT for SMART Analysis

Lecture 3: Connect with my youtube channel

Lecture 4: Get special handbooks

Chapter 2: Necessary Development of Python Programming Part 1

Lecture 1: Stepping into the Python programming

Lecture 2: Assigning variables and the rules of names

Lecture 3: Various data types in python programming

Lecture 4: Data type conversion and casting in python

Lecture 5: Applying arithmetic operations in python

Lecture 6: Utilizing comparison operators in python

Lecture 7: Using logical operators in python

Chapter 3: Necessary Development of Python Programming Part 2

Lecture 1: Applying list for indexing, slicing and more

Lecture 2: Creating unique elements of sets and operations

Lecture 3: All about python dictionaries

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

Lecture 5: Nesting logical expressions in conditional operations

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

Lecture 7: Defining, Creating and Calling functions

Chapter 4: Understanding Data Science – Develop the Fundamentals

Lecture 1: Data Science and its characteristics

Lecture 2: Data Science v/s Data Analysis

Lecture 3: Complete Data Science work-flow

Lecture 4: Download datasets for practice and quizzes

Lecture 5: Instructions for Quizzes: IMPORTANT

Chapter 5: Step-by-step Data Cleaning Process in Python

Lecture 1: Getting started with a dataset

Lecture 2: Impute missing values with Simple-Imputer

Lecture 3: Rectify inconsistent variables and values

Lecture 4: Identify and assign correct data types

Lecture 5: Abolish duplicated data from the dataset

Lecture 6: Solution 1: Full Data Cleaning

Chapter 6: Various Aspects of Data Manipulation in Python

Lecture 1: Sorting and arranging dataset

Lecture 2: Conditional filtering (and, or, not etc.)

Lecture 3: Merging dataset with extra features

Lecture 4: Concatenating data with extra data

Lecture 5: Solution 2: Full Data Manipulation

Chapter 7: Comprehensive Exploratory Data Analysis in Python

Lecture 1: Understanding exploratory data analysis

Lecture 2: Investigating Value Counts Analysis Technique

Lecture 3: Delving into Descriptive Statistics Analysis Technique

Lecture 4: Understanding Group By Analysis Method

Lecture 5: Mastering Pivot Table Analysis Method

Lecture 6: Unpacking Crosstabulation Analysis Method

Lecture 7: Exploring Correlation Analysis Method

Lecture 8: Solution 3: Full Exploratory Data Analysis

Chapter 8: Understanding Statistical Data Analysis and Concepts

Lecture 1: Various aspects of hypothesis testing

Lecture 2: Understand confidence, significance level and p-value

Lecture 3: Statistical data analysis and hypothesis testing

Chapter 9: Various Data Transformation Techniques in Python

Lecture 1: Testing normal distribution of numeric variables

Lecture 2: Square root data transformation method

Lecture 3: Logarithm data transformation method

Lecture 4: Box-cox data transformation method

Lecture 5: Yeo-Johnson data transformation method

Lecture 6: Solution 5: Data Transformation Methods

Chapter 10: Hypothesis Testing (ANOVA, Pearson Correlation, Regression)

Instructors

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

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3 stars: 1 votes

4 stars: 7 votes

5 stars: 29 votes

Frequently Asked Questions

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

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