| 1. ChatGPT Your best code companion.mp4 | 60.2 MB | ||
| 1. Feature engineering Generating new data.mp4 | 103.9 MB | ||
| 1. Install Python and Jupyter Notebook.html | 204.8 B | ||
| 1. KMeans Clustering ML model.mp4 | 127.7 MB | ||
| 1. Load your dataset into Python environment.mp4 | 39.1 MB | ||
| 1. Machine Learning and Its Characteristics.mp4 | 10.6 MB | ||
| 1. Read It IMPORTANT.html | 307.2 B | ||
| 1. Sorting and arranging dataset.mp4 | 30.9 MB | ||
| 1.1 Mac.pdf | 1.5 MB | ||
| 1.2 Windows.pdf | 929.7 KB | ||
| 2. Complete Machine Learning Work-flow.mp4 | 9.4 MB | ||
| 2. Course resources.html | 0 B | ||
| 2. Extracting day, months, year from date variable.mp4 | 32.4 MB | ||
| 2. Filter data based on conditions.mp4 | 71.7 MB | ||
| 2. Final QUIZ ML Model Application Part 3.html | 204.8 B | ||
| 2. Handling missing values with Scikit-learn.mp4 | 81.1 MB | ||
| 2. Linear regression ML model.mp4 | 115.9 MB | ||
| 2. Logistic Regression ML model.mp4 | 139.3 MB | ||
| 2. Setting Up ChatGPT for Easy Machine Learning.html | 204.8 B | ||
| 2.1 Complete ML workflow.pptx | 47.6 KB | ||
| 2.1 Instructions of setting up ChatGPT.pdf | 409.1 KB | ||
| 2.2 ML.pptx | 39.9 KB | ||
| 3. Decision Tree classification ML model.mp4 | 77.4 MB | ||
| 3. Decision Tree regression ML model.mp4 | 57.2 MB | ||
| 3. Feature encoding Assigning numeric values.mp4 | 33.4 MB | ||
| 3. Final Solution Fast-Track ML in Python & ChatGPT.html | 0 B | ||
| 3. Identify and deal with inconsistent data.mp4 | 61.7 MB | ||
| 3. Merging or adding of supplementary variables.mp4 | 31.1 MB | ||
| 3. Practice datasets.html | 307.2 B | ||
| 3.1 Fast-Track ML in Python & ChatGPT (Solution).ipynb | 750.1 KB | ||
| 4. Concatenating or adding of supplementary data.mp4 | 31.3 MB | ||
| 4. Creating dummy variables for nominal data.mp4 | 47.2 MB | ||
| 4. Dealing with miss-identified data types.mp4 | 40.3 MB | ||
| 4. Instructions for Quizzes IMPORTANT.html | 307.2 B | ||
| 4. Random Forest classification ML model.mp4 | 68.7 MB | ||
| 4. Random Forest regression ML model.mp4 | 56.6 MB | ||
| 5. Address and remove duplicated data.mp4 | 28.2 MB | ||
| 5. Data standardizing and normalizing with StandardScaler.mp4 | 84.6 MB | ||
| 5. K Nearest Neighbours classification ML model.mp4 | 120.5 MB | ||
| 5. QUIZ 2 Data Manipulation.html | 204.8 B | ||
| 5. Support Vector regression ML model.mp4 | 42.7 MB | ||
| 6. LightGBM classification ML model.mp4 | 81.7 MB | ||
| 6. QUIZ 1 Data Cleaning.html | 204.8 B | ||
| 6. Solution 2 Data Manipulation.html | 102.4 B | ||
| 6. Splitting data into training and testing set.mp4 | 38.7 MB | ||
| 6. XGBoost regression ML model.mp4 | 48.1 MB | ||
| 6.1 Data Manipulation (Solution).ipynb | 85.4 KB | ||
| 7. QUIZ 3 Data Preprocessing.html | 204.8 B | ||
| 7. QUIZ 4 ML Model Application Part 1.html | 204.8 B | ||
| 7. QUIZ 5 ML Model Application Part 2.html | 204.8 B | ||
| 7. Solution 1 Data Cleaning.html | 102.4 B | ||
| 7.1 Data Cleaning (Solution).ipynb | 34.8 KB | ||
| 8. Solution 3 Data Preprocessing.html | 102.4 B | ||
| 8. Solution 4 ML Model Application Part 1.html | 102.4 B | ||
| 8. Solution 5 ML Model Application Part 2.html | 102.4 B | ||
| 8.1 Data Preprocessing (Solution).ipynb | 138 KB | ||
| 8.1 ML model application Part 1 (Solution).ipynb | 515.6 KB | ||
| 8.1 ML model application Part 2 (Solution).ipynb | 695.7 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 61 total files | |||
Fast-Track Machine Learning in Python & ChatGPT
https://DevCourseWeb.com
Published 10/2023
Created by Md Shahriar
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 42 Lectures ( 4h 46m ) | Size: 1.73 GB
Hands-on Machine Learning Tutorial with Pandas, Numpy, Seaborn, Scikit-learn in Python and ChatGPT: A Complete Work-flow
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, Random Forest, Decision Tree, SVM, XGBoost, and KNN, etc.
Acquire skills in unsupervised machine learning techniques, including KMeans for effective cluster analysis and pattern recognition.
Learn to create a streamlined and efficient workflow for building machine learning models from scratch, incorporating both Python and ChatGPT.
Develop the ability to measure and evaluate the accuracy and performance of machine learning models, enabling decisions on model selection and optimization.
Explore the integration of ChatGPT into the machine learning workflow, leveraging its capabilities for enhanced data analysis, and generating insights.
Understand strategies for selecting the most suitable machine learning model for a given task, considering factors such as accuracy, and scalability.
Apply acquired knowledge to real-world scenarios, solving diverse machine learning challenges and developing solutions.
Requirements
No coding Experience is Needed.
Desktop/Laptop
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