Data Analysis And Machine Learning - Python + Gpt 3.5 and Gpt 4

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[ FreeCourseWeb.com ] Data Analysis And Machine Learning - Python + Gpt 3.5 and Gpt 4
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Setting Up Your Analysis Environment
    • 1 - Install Python and Jupyter Notebook.html (0.2 KB)
    • 1 - Mac.pdf (1.5 MB)
    • 1 - Windows.pdf (929.7 KB)
    • 2 - Setting up ChatGPT and GPT 4.mp4 (6.9 MB)
    • 3 - Download Practice datasets.html (0.3 KB)
      • Practice datasets for GPT 4
      • Cleaned_Data.xlsx (74.6 KB)
      • Data+for+cleaning.xlsx (74.4 KB)
      • data+1.xlsx (41.9 KB)
      • data+2.xlsx (45.2 KB)
      • practice_filtering+data.xlsx (11.5 KB)
      Practice datasets
      • Extra Data.xlsx (10.5 KB)
      • Extra Variable.xlsx (33.0 KB)
      • sales data.xlsx (85.0 KB)
      __MACOSX Practice datasets for GPT 4
      • _Cleaned_Data.xlsx (0.8 KB)
      • _Data+for+cleaning.xlsx (0.9 KB)
      • _data+1.xlsx (0.8 KB)
      • _data+2.xlsx (0.8 KB)
      • _practice_filtering+data.xlsx (0.9 KB)
      Practice datasets
      • _Extra Data.xlsx (0.3 KB)
      • _Extra Variable.xlsx (0.3 KB)
      • _sales data.xlsx (0.3 KB)
      10 - Python GPT 35 Learn Regressor Machine Learning
      • 40 - Linear regression ML model.mp4 (127.6 MB)
      • 41 - Decision Tree regression ML model.mp4 (36.8 MB)
      • 42 - Random Forest regression ML model.mp4 (59.8 MB)
      • 43 - Support Vector regression ML model.mp4 (46.8 MB)
      11 - Python GPT 35 Learn Classification Machine Learning
      • 44 - Logistic Regression ML model.mp4 (194.8 MB)
      • 45 - Decision Tree classification ML model.mp4 (105.9 MB)
      • 46 - Random Forest classification ML model.mp4 (75.9 MB)
      • 47 - K Nearest Neighbours classification ML model.mp4 (151.2 MB)
      12 - Python GPT 35 Learn Clustering Machine Learning
      • 48 - KMeans Clustering ML model.mp4 (169.8 MB)
      13 - Python GPT 4 Rapid Data Cleaning
      • 49 - Getting Started with GPT4 Data Analyst.mp4 (6.8 MB)
      • 50 - Identify missing values.mp4 (14.7 MB)
      • 51 - Impute missing values.mp4 (25.0 MB)
      • 52 - Exploring data types.mp4 (12.8 MB)
      • 53 - Finding inconsistent values.mp4 (10.5 MB)
      • 54 - Dropping inconsistent values.mp4 (16.8 MB)
      • 55 - Dealing with duplicates.mp4 (76.8 MB)
      14 - Python GPT 4 Instant Data Manipulation
      • 56 - Sorting dataset.mp4 (21.5 MB)
      • 57 - Filtering datasets.mp4 (20.4 MB)
      • 58 - Inner joining method.mp4 (28.1 MB)
      • 59 - Other joining methods.mp4 (22.0 MB)
      • 60 - Boxcox transformation.mp4 (32.9 MB)
      • 61 - Feature binning.mp4 (15.3 MB)
      • 62 - Feature encoding.mp4 (15.5 MB)
      • 63 - Creating dummy variables.mp4 (15.1 MB)
      15 - Python GPT 4 Fasttrack Data Analysis
      • 64 - Nominal data analysis.mp4 (17.5 MB)
      • 65 - Descriptive analysis.mp4 (56.8 MB)
      • 66 - Group by data analysis.mp4 (12.7 MB)
      • 67 - Crosstabulation analysis.mp4 (75.9 MB)
      • 68 - Correlation analysis.mp4 (45.4 MB)
      16 - Python GPT 4 Quick Hypothesis Testing
      • 69 - Oneway ANOVA analysis.mp4 (85.8 MB)
      • 70 - Pearson correlation analysis.mp4 (18.5 MB)
      • 71 - Regression analysis.mp4 (74.6 MB)
      17 - Python GPT 4 Build Machine Learning Models
      • 72 - Feature scaling and preprocessing.mp4 (49.0 MB)
      • 73 - Splitting data into train and test sets.mp4 (26.8 MB)
      • 74 - Build and evaluate ML models.mp4 (50.3 MB)
      2 - Data Analysis and Its Workflow
      • 4 - Data Analysis and Its Characteristics.mp4 (46.7 MB)
      • 5 - Complete data analysis workflow.mp4 (6.3 MB)
      3 - Statistical Analysis and Its Workflow
      • 6 - Statistical Analysis and Its Characteristics.mp4 (19.7 MB)
      • 7 - Confidence level significance level and Pvalue.mp4 (20.7 MB)
      • 8 - Complete hypothesis testing workflow.mp4 (19.2 MB)
      4 - Machine Learning and Its Workflow
      • 10 - Complete Machine Learning Workflow.mp4 (5.9 MB)
      • 9 - Machine Learning and Its Characteristics.mp4 (6.6 MB)
      5 - Python Programming Basics Level 1
      • 11 - Your First Python Code.mp4 (11.3 MB)
      • 12 - Variables and naming conventions.mp4 (28.8 MB)
      • 13 - Data types integers float strings boolean.mp4 (29.8 MB)
      • 14 - Type conversion and casting.mp4 (36.5 MB)
      • 15 - Arithmetic operators.mp4 (32.3 MB)
      • 16 - Comparison operators.mp4 (37.0 MB)
      • 17 - Logical operators and or not.mp4 (39.9 MB)
      6 - Python Programming Basics Level 2
      • 18 - Lists creation indexing slicing modifying.mp4 (83.5 MB)
      • 19 - Sets unique elements operations.mp4 (28.9 MB)
      • 20 - Dictionaries keyvalue pairs methods.mp4 (45.2 MB)
      • 21 - Conditional statements if elif else.mp4 (19.9 MB)
      • 22 - Logical expressions in conditions.mp4 (47.4 MB)
      • 23 - Looping structures for loops while loops.mp4 (34.0 MB)
      • 24 - Defining Creating and Calling functions.mp4 (16.4 MB)
      7 - Python GPT 35 Learn Data Cleaning
      • 25 - Loading dataset.mp4 (43.5 MB)
      • 26 - Handling missing values.mp4 (106.7 MB)
      • 27 - Deal with inconsistent data.mp4 (69.5 MB)
      • 28 - Dealing with missidentified data types.mp4 (45.2 MB)
      • 29 - Dealing with duplicated data.mp4 (23.7 MB)
      8 - Python GPT 35 Learn Data Manipulation
      • 30 - Sorting and arranging dataset.mp4 (27.1 MB)
      • 31 - Filter data based on conditions.mp4 (40.9 MB)
      • 32 - Merging or adding variables.mp4 (47.8 MB)
      • 33 - Concatenating extra data.mp4 (49.5 MB)
      9 - Python GPT 35 Learn Data Preprocessing
      • 34 - Feature engineering.mp4 (116.6 MB)

      • Description

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

        https://FreeCourseWeb.com

        Published 3/2024
        MP4 | Video: h264, 1920x1080 | 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



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Data Analysis And Machine Learning - Python + Gpt 3.5 and Gpt 4

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3.2 GB
seeders:2
leechers:49
Data Analysis And Machine Learning - Python + Gpt 3.5 and Gpt 4

Torrent hash: 3E8C982AE6AF09224D2BE5DDA2B06CD25739352D