Published 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.24 GB | Duration: 8h 57m
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
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