Data Science with Python and Machine Learning For Beginners

سرفصل های دوره

Learn how to use Python,Pandas,NumPy,Matplotlib,Seaborn, Data Wrangling,Learnbuild models, train and deploy models.

1 - Introduction to Data Science

1 - Introduction.html

2 - What is Data Science.html

3 - Importance of Data Science in Todays World.html

4 - Overview of Python for Data Science.html

5 - Basics of statistics for data analysis.html

6 - Ethical Considerations in Data Science.html

7 - Introduction to Python Programming.html

2 - Environment Setup

8 - Python Installation on Windows

9 - What are virtual environments

10 - Creating and activating a virtual environment on Windows

11 - Python Installation on macOS.html

12 - Creating and activating a virtual environment on macOS.html

13 - What is Jupyter Notebook.html

14 - Installing Pandas and Jupyter Notebook in the Virtual Environment

15 - Starting Jupyter Notebook

16 - Exploring Jupyter Notebook Server Dashboard Interface

17 - Creating a new Notebook

18 - Exploring Jupyter Notebook Source and Folder Files

19 - Exploring the Notebook Interface

3 - Data Manipulation and visualization with Python and pandas

20 - Overview of Pandas.html

21 - Pandas Data Structures.html

22 - Creating a Pandas Series from a List

23 - Creating a Pandas Series from a List with Custom Index

24 - Creating a pandas series from a Python Dictionary

25 - Accessing Data in a Series using the index by label

26 - Accessing Data in a Series By position

27 - Slicing a Series by Label

28 - Creating a DataFrame from a dictionary of lists

29 - Creating a DataFrame From a list of dictionaries

30 - Accessing data in a DataFrame

31 - AAPL.csv

31 - Download Dataset

32 - Loading Dataset into a DataFrame

33 - Inspecting the data

34 - Data Cleaning

35 - Data transformation and analysis

36 - Visualizing data

4 - Introduction to Machine Learning Build and Train a Machine Learning Model

37 - What is Machine Learning.html

38 - Installing and importing libraries

39 - Data Preprocessing.html

40 - What is a Dataset.html

41 - Downloading dataset

42 - Exploring the Dataset

43 - Handle missing values and drop unnecessary columns

43 - handling-missing-values.txt

44 - Encode categorical variables

44 - Encode-categorical-variables.txt

45 - What is Feature Engineering.html

46 - Create new features

46 - Create-new-features.txt

47 - Dropping unnecessary columns

47 - Dropping-unnecessary-columns.txt

48 - Visualize survival rate by gender

48 - bar-plot-code.txt

49 - Visualize survival rate by class

49 - bar-plot-2.txt

50 - Visualize numerical features

50 - visualize-numeric-data.txt

51 - Visualize the distribution of Age

51 - Visualize-the-distribution-of-Age.txt

52 - Visualize number of passengers in each passenger class

52 - Visualize-number-of-passengers-in-each-passenger-class.txt

53 - Visualize number of passengers that survived

53 - countplot.txt

54 - Visualize the correlation matrix of numerical variables

55 - Visualize the distribution of Fare

55 - Visualize-the-distribution-of-Fare.txt

56 - Data Preparation and Training Model.html

57 - What is a Model.html

58 - Define features and target variable

58 - define-features.txt

59 - Split data into training and testing sets

59 - Split-data.txt

60 - Standardize features

60 - Standardize-features.txt

61 - What is a logistic regression model.html

62 - Train logistic regression model

62 - regression-model.txt

63 - Making Predictions

64 - What is accuracy in machine learning.html

65 - What is confusion matrix.html

66 - What is is classification report.html

67 - What is a Heatmap.html

68 - Evaluate the model using accuracy confusion matrix and classification report

68 - evaluate.txt

69 - Visualize the confusion matrix

69 - confusion-matrix.txt

70 - Saving the Model

70 - save-model.txt

71 - Loading the model

71 - load-model.txt

72 - Improving Understanding of the models prediction

73 - Building a decision tree

73 - DECISION-TREES.txt

74 - Building a random forest

74 - RANDOM-FOREST.txt

5 - Predicting real house prices using machine learning

75 - Importing Libraries and modules

75 - import-more-modules.txt

76 - Loading dataset and creating a dataframe

76 - load-housing-dataset.txt

77 - Checking for missing values

78 - Dropping column and splitting data

79 - Standardize the features for housing dataframe

79 - standardize-features-for-housing-dataset.txt

80 - Initialize and train the regression model

80 - model-training.txt

81 - Make predictions on the test set

81 - predictions.txt

82 - Evaluating the model for the housing dataset

82 - evaluate-housing-model.txt

83 - Predicting a small sample of data

83 - predicting-sample-data.txt

84 - Creating scatter plot

84 - scatter-plot-for-housing-data.txt

85 - Creating a bar plot

85 - housing-data-barplot.txt

86 - Saving the housing model

86 - save-housing-model.txt

87 - Loading the housing model

6 - Build a Web App House Price Prediction Tool

88 - What is Flask.html

89 - Installing Flask

90 - Installing Visual Studio Code

91 - Creating a minimal flask app

92 - How to run a flask app

93 - Http and Http Methods.html

94 - Loading the saved model and scaler into Python file

95 - Define the home route

96 - Define the prediction route

97 - Creating the template

98 - Adding a form to the template

99 - Displaying predictions and clearing form inputs

100 - Testing the prediction tool

101 - Exploring deployment and hosting options.html

102 - Create a new account on pythonanywhere

103 - Creating a new web app in PythonAnywhere

104 - Uploading files to Pythonanywhere

105 - Creating and activating a virtual environment on PythonAnywhere

106 - What is a WSGI File.html

107 - Configuring WSGI File

108 - Running your app in a cloud hosting environment

109 - Project files.html

109 - app.zip

109 - index.html

109 - model-scaler.zip

7 - Python Basics for Data Science

110 - Python Expressions

111 - Python Statements

112 - Python Code Comments

113 - Python Data Types

114 - Casting Data Types

115 - Python Variables

116 - Python List

117 - Python Tuple

118 - Python Dictionaries

119 - Python Operators

120 - Python Conditional Statements

121 - Python Loops

122 - Python Functions

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تولید کننده: Udemy