Data Analysis And Machine Learning: Python + Gpt 3.5 & Gpt 4

Hands-on Data Analysis and Machine Learning in Python + GPT 3.5. Apply GPT-4 to Analyze and Develop ML Models Smoothly.

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, 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.

Requirements

No coding Experience is Needed.

Laptop/Desktop and Internet

Description

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.

Overview

Section 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

Section 2: Data Analysis and Its Workflow

Lecture 4 Data Analysis and Its Characteristics

Lecture 5 Complete data analysis workflow

Section 3: Statistical Analysis and Its Workflow

Lecture 6 Statistical Analysis and Its Characteristics

Lecture 7 Confidence level, significance level and P-value

Lecture 8 Complete hypothesis testing workflow

Section 4: Machine Learning and Its Workflow

Lecture 9 Machine Learning and Its Characteristics

Lecture 10 Complete Machine Learning Work-flow

Section 5: Python Programming Basics Level 1

Lecture 11 Your First Python Code

Lecture 12 Variables and naming conventions

Lecture 13 Data types: integers, float, strings, boolean

Lecture 14 Type conversion and casting

Lecture 15 Arithmetic operators (+, -, *, /, %, **)

Lecture 16 Comparison operators (>, =, <=, ==, !=)

Lecture 17 Logical operators (and, or, not)

Section 6: Python Programming Basics Level 2

Lecture 18 Lists: creation, indexing, slicing, modifying

Lecture 19 Sets: unique elements, operations

Lecture 20 Dictionaries: key-value pairs, methods

Lecture 21 Conditional statements (if, elif, else)

Lecture 22 Logical expressions in conditions

Lecture 23 Looping structures (for loops, while loops)

Lecture 24 Defining, Creating and Calling functions

Section 7: Python + GPT 3.5 - Learn Data Cleaning

Lecture 25 Loading dataset

Lecture 26 Handling missing values

Lecture 27 Deal with inconsistent data

Lecture 28 Dealing with miss-identified data types

Lecture 29 Dealing with duplicated data

Section 8: Python + GPT 3.5 - Learn Data Manipulation

Lecture 30 Sorting and arranging dataset

Lecture 31 Filter data based on conditions

Lecture 32 Merging or adding variables

Lecture 33 Concatenating extra data

Section 9: Python + GPT 3.5 - Learn Data Preprocessing

Lecture 34 Feature engineering

Lecture 35 Extracting day, months, year

Lecture 36 Feature encoding

Lecture 37 Creating dummy variables

Lecture 38 Data normalizing

Lecture 39 Splitting data

Section 10: Python + GPT 3.5 - Learn Regressor Machine Learning

Lecture 40 Linear regression ML model

Lecture 41 Decision Tree regression ML model

Lecture 42 Random Forest regression ML model

Lecture 43 Support Vector regression ML model

Section 11: Python + GPT 3.5 - Learn Classification Machine Learning

Lecture 44 Logistic Regression ML model

Lecture 45 Decision Tree classification ML model

Lecture 46 Random Forest classification ML model

Lecture 47 K Nearest Neighbours classification ML model

Section 12: Python + GPT 3.5 - Learn Clustering Machine Learning

Lecture 48 KMeans Clustering ML model

Section 13: Python + GPT 4 - Rapid Data Cleaning

Lecture 49 Getting Started with GPT-4 Data Analyst

Lecture 50 Identify missing values

Lecture 51 Impute missing values

Lecture 52 Exploring data types

Lecture 53 Finding inconsistent values

Lecture 54 Dropping inconsistent values

Lecture 55 Dealing with duplicates

Section 14: Python + GPT 4 - Instant Data Manipulation

Lecture 56 Sorting dataset

Lecture 57 Filtering datasets

Lecture 58 Inner joining method

Lecture 59 Other joining methods

Lecture 60 Box-cox transformation

Lecture 61 Feature binning

Lecture 62 Feature encoding

Lecture 63 Creating dummy variables

Section 15: Python + GPT 4 - Fast-track Data Analysis

Lecture 64 Nominal data analysis

Lecture 65 Descriptive analysis

Lecture 66 Group by data analysis

Lecture 67 Crosstabulation analysis

Lecture 68 Correlation analysis

Section 16: Python + GPT 4 - Quick Hypothesis Testing

Lecture 69 One-way ANOVA analysis

Lecture 70 Pearson correlation analysis

Lecture 71 Regression analysis

Section 17: Python + GPT 4 - Build Machine Learning Models

Lecture 72 Feature scaling and preprocessing

Lecture 73 Splitting data into train and test sets

Lecture 74 Build and evaluate ML models

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