Learn Data Science Skills: Python, Pandas, Machine Learning

سرفصل های دوره

Explore tools like : Python,Pandas,Jupyter Notebook, Numpy, Matplotlib, scikit-learn,Seaborn, Machine Learning

1. Introduction to Data Science

1. Introduction.html

2. What is Data Science.html

3. Importance and applications of data science in various industries..html

4. Overview of tools and technologies used in data science..html

5. Introduction to Python programming language..html

6. Essential libraries for data manipulation and analysis (e.g., Pandas, NumPy)..html

7. Ethics and Best Practices in Data Science.html

2. Environment Setup

1. Python Installation on Windows

2. What are virtual environments

3. Creating and activating a virtual environment on Windows

4. Python Installation on macOS.html

5. Creating and activating a virtual environment on macOS.html

6. What is Jupyter Notebook.html

7. Installing Pandas and Jupyter Notebook in the Virtual Environment

8. Starting Jupyter Notebook

9. Exploring Jupyter Notebook Server Dashboard Interface

10. Creating a new Notebook

11. Exploring Jupyter Notebook Source and Folder Files

12. Exploring the Notebook Interface

3. Data Manipulation and visualization with pandas

1. Overview of Pandas.html

2. Pandas Data Structures.html

3. Creating a Pandas Series from a List

4. Creating a Pandas Series from a List with Custom Index

5. Creating a pandas series from a Python Dictionary

6. Accessing Data in a Series using the index by label

7. Accessing Data in a Series By position

8. Slicing a Series by Label

9. Creating a DataFrame from a dictionary of lists

10. Creating a DataFrame From a list of dictionaries

11. Accessing data in a DataFrame

12. Download Dataset

13. Loading Dataset into a DataFrame

14. Inspecting the data

15. Data Cleaning

16. Data transformation and analysis

17. Visualizing data

4. Machine Learning Build ,Train and deploy a machine learning model

1. What is Machine Learning.html

2.1 Importing+Libaries+and+modules.txt

2. Installing and importing libraries

3. Data Preprocessing.html

4. What is a Dataset.html

5. Downloading dataset

6. Exploring the Dataset

7.1 handling+missing+values.txt

7. Handle missing values and drop unnecessary columns.

8.1 Encode+categorical+variables..txt

8. Encode categorical variables.

9. What is Feature Engineering.html

10.1 Create+new+features..txt

10. Create new features.

11.1 Dropping+unnecessary+columns.txt

11. Dropping unnecessary columns

12.1 bar+plot+code.txt

12. Visualize survival rate by gender

13.1 bar+plot+2.txt

13. Visualize survival rate by class

14.1 visualize+numeric+data.txt

14. Visualize numerical features

15.1 Visualize++the+distribution+of+Age.txt

15. Visualize the distribution of Age

16.1 Visualize+number+of+passengers+in+each+passenger+class.txt

16. Visualize number of passengers in each passenger class

17.1 countplot.txt

17. Visualize number of passengers that survived

18.1 heatmap.txt

18. Visualize the correlation matrix of numerical variables

19.1 Visualize++the+distribution+of++Fare..txt

19. Visualize the distribution of Fare.

20. Data Preparation and Training Model.html

21. What is a Model.html

22.1 define+features.txt

22. Define features and target variable.

23.1 Split+data.txt

23. Split data into training and testing set

24.1 Standardize+features.txt

24. Standardize features

25. What is a logistic regression model..html

26.1 regression+model.txt

26. Train logistic regression model.

27. Making Predictions

28. What is accuracy in machine learning.html

29. What is confusion matrix..html

30. What is is classification report..html

31. What is a Heatmap.html

32.1 evaluate.txt

32. Evaluate the model using accuracy, confusion matrix, and classification report.

33.1 confusion+matrix.txt

33. Visualize the confusion matrix.

34.1 save+model.txt

34. Saving the Model

35.1 load+model.txt

35. Loading the model

36. Improving Understanding of the models prediction

37.1 DECISION+TREES.txt

37. Building a decision tree

38.1 RANDOM+FOREST.txt

38. Building a random forest

5. Predicting real house prices using machine learning

1.1 import+more+modules.txt

1. Importing Libraries and modules

2.1 load+housing+dataset.txt

2. Loading dataset and creating a dataframe

3. Checking for missing values

4. Dropping column and splitting data

5.1 standardize+features+for+housing+dataset.txt

5. Standardize the features for housing dataframe

6.1 model+training.txt

6. Initialize and train the regression model

7.1 predictions.txt

7. Make predictions on the test set.

8.1 evaluate+housing+model.txt

8. Evaluating the model for the housing dataset.

9.1 predicting++sample+data.txt

9. Predicting a small sample of data

10.1 scatter+plot+for+housing+data.txt

10. Creating scatter plot

11.1 housing+data+barplot.txt

11. Creating a bar plot

12.1 save+housing+model.txt

12. Saving the housing model

13. Loading the housing model

6. Build a Web App House Price Prediction Tool

1. What is Flask.html

2. Installing Flask

3. Installing Visual Studio Code

4. Creating a minimal flask app

5. How to run a flask app

6. Http and Https Methods.html

7. Loading the saved model and scaler into Python file

8. Define the home route

9. Define the prediction route

10. Creating the template

11. Adding a form to the template

12. Displaying predictions and clearing form inputs

13. Testing the prediction tool

14. Exploring deployment and hosting options.html

15. Create a new account on pythonanywhere

16. Creating a new web app in PythonAnywhere

17. Uploading project files to Pythonanywhere

18. Creating and activating a virtual environment on PythonAnywhere

19. What is a WSGI File.html

20. Configuring WSGI File

21. Running your app in a cloud hosting environment

22.1 app.zip

22.2 index.html

22.3 model scaler.zip

22. Project files.html

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تولید کننده: Udemy