| 1 - Install Python and Jupyter Notebook.html | 204.8 B | ||
| 1 - Mac.pdf | 1.5 MB | ||
| 1 - Windows.pdf | 929.7 KB | ||
| 10 - Complete Machine Learning Workflow.mp4 | 5.9 MB | ||
| 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 MB | ||
| 17 - Logical operators and or not.mp4 | 39.9 MB | ||
| 18 - Lists creation indexing slicing modifying.mp4 | 83.5 MB | ||
| 19 - Sets unique elements operations.mp4 | 28.9 MB | ||
| 2 - Setting up ChatGPT and GPT 4.mp4 | 6.9 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 MB | ||
| 24 - Defining Creating and Calling functions.mp4 | 16.4 MB | ||
| 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 | ||
| 3 - Download Practice datasets.html | 307.2 B | ||
| 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 | ||
| 34 - Feature engineering.mp4 | 116.6 MB | ||
| 35 - Extracting day months year.mp4 | 42 MB | ||
| 36 - Feature encoding.mp4 | 38.9 MB | ||
| 37 - Creating dummy variables.mp4 | 53.3 MB | ||
| 38 - Data normalizing.mp4 | 120.3 MB | ||
| 39 - Splitting data.mp4 | 52.2 MB | ||
| 4 - Data Analysis and Its Characteristics.mp4 | 46.7 MB | ||
| 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 | ||
| 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 | ||
| 48 - KMeans Clustering ML model.mp4 | 169.8 MB | ||
| 49 - Getting Started with GPT4 Data Analyst.mp4 | 6.8 MB | ||
| 5 - Complete data analysis workflow.mp4 | 6.3 MB | ||
| 50 - Identify missing values.mp4 | 14.7 MB | ||
| 51 - Impute missing values.mp4 | 25 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 | ||
| 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 MB | ||
| 6 - Statistical Analysis and Its Characteristics.mp4 | 19.7 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 | ||
| 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 | ||
| 69 - Oneway ANOVA analysis.mp4 | 85.8 MB | ||
| 7 - Confidence level significance level and Pvalue.mp4 | 20.7 MB | ||
| 70 - Pearson correlation analysis.mp4 | 18.5 MB | ||
| 71 - Regression analysis.mp4 | 74.6 MB | ||
| 72 - Feature scaling and preprocessing.mp4 | 49 MB | ||
| 73 - Splitting data into train and test sets.mp4 | 26.8 MB | ||
| 74 - Build and evaluate ML models.mp4 | 50.3 MB | ||
| 8 - Complete hypothesis testing workflow.mp4 | 19.2 MB | ||
| 9 - Machine Learning and Its Characteristics.mp4 | 6.6 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Cleaned_Data.xlsx | 74.6 KB | ||
| Data+for+cleaning.xlsx | 74.4 KB | ||
| Extra Data.xlsx | 10.5 KB | ||
| Extra Variable.xlsx | 33 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| _Cleaned_Data.xlsx | 819.2 B | ||
| _Data+for+cleaning.xlsx | 921.6 B | ||
| _Extra Data.xlsx | 307.2 B | ||
| _Extra Variable.xlsx | 307.2 B | ||
| _data+1.xlsx | 819.2 B | ||
| _data+2.xlsx | 819.2 B | ||
| _practice_filtering+data.xlsx | 921.6 B | ||
| _sales data.xlsx | 307.2 B | ||
| data+1.xlsx | 41.9 KB | ||
| data+2.xlsx | 45.2 KB | ||
| practice_filtering+data.xlsx | 11.5 KB | ||
| sales data.xlsx | 85 KB | ||
| ▲ 94 total files | |||
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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 3.8 GB | freecoursewb | 1 month | 23 | 3 | |
| 1.3 GB | freecoursewb | 1 month | 14 | 2 | |
| 2 GB | freecoursewb | 1 month | 7 | 5 | |
| 1.1 GB | freecoursewb | 1 month | 4 | 7 | |
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Udemy - SAP Crystal Reports - Architecture, Design, and Data Analysis Posted by
freecoursewb in Other
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