Hands-on Data Science Skills(Python Machine Learning,Pandas)

سرفصل های دوره

Explore Python, Pandas, Machine Learning Models; Train and deploy ML Model via a web app

1. Understanding Data Science and Its Importance

1. Introduction.html

2. What is Data Science.html

3. Data Science vs. Data Engineering vs. Data Analysis.html

4. Applications and Industry Impact.html

2. Essential Tools and Technologies

1. Overview of Programming Languages Python, R.html

2. Introduction to SQL.html

3. Data Science Libraries Pandas, NumPy, Matplotlib, Seaborn.html

4. Types of Data Structured vs. Unstructured.html

5. APIs and Data Retrieval.html

3. 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

4. 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

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

1. What is Machine Learning.html

2. Supervised Learning.html

3. Unsupervised learning.html

4. Reinforcement learning.html

5. What is an Algorithm.html

6. Installing and importing libraries

7. Data Preprocessing.html

8. What is a Dataset.html

9. Downloading dataset

10. Loading the dataset and creating a dataframe

11. Exploring the Dataset

12. Handle missing values and drop unnecessary columns.

13. Encode categorical variables.

14. What is Feature Engineering.html

15. Create new features.

16. Dropping unnecessary columns

17. Visualize survival rate by gender

18. Visualize survival rate by class

19. Visualize numerical features

20. Visualize the distribution of Age

21. Visualize number of passengers in each passenger class

22. Visualize number of passengers that survived

23. Visualize the correlation matrix of numerical variables

24. Visualize the distribution of Fare.

25. Data Preparation and Training Model.html

26. What is a Model.html

27. Define features and target variable.

28. Split data into training and testing sets.

29. Standardize features.

30. What is a logistic regression model..html

31. Train logistic regression model.

32. Making Predictions

33. What is accuracy in machine learning.html

34. What is confusion matrix..html

35. What is is classification report..html

36. What is a Heatmap.html

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

38. Visualize the confusion matrix.

39. Saving the Model

40. Loading the model

41. Improving Understanding of the models prediction

42. Building a decision tree

43. Building a random forest

6. Predicting real house prices using machine learning

1. Importing Libraries and modules

2. Loading dataset and creating a dataframe

3. Checking for missing values

4. Dropping column and splitting data

5. Standardize the features for housing dataframe

6. Initialize and train the regression model

7. Make predictions on the test set.

8. Evaluating the model for the housing dataset.

9. Predicting a small sample of data

10. Creating scatter plot

11. Creating a bar plot

12. Saving the housing model

13. Loading the housing model

7. 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 Http 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. Creating and activating a virtual environment on PythonAnywhere

18. What is a WSGI File.html

19. Configuring WSGI File

20. Running your app in a cloud hosting environment

21. Project files.html

8. Python Crash Course

1. Python Expressions

2. Python Statements

3. Python Code Comments

4. Python Data Types

5. Casting Data Types

6. Python Variables

7. Python List

8. Python Tuple

9. Python Dictionaries

10. Python Operators

11. Python Conditional Statements

12. Python Loops

13. Python Functions

54,900 تومان

خرید اشتراک افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: Udemy