Data Science for Beginners Python Azure ML and Tableau

[AD-TEAM]

9.79 GB | 14min 15s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 -Introduction to Course.mp4 (38.03 MB)
2 -Introduction to Udemy and Where to Ask a Question.mp4 (28.91 MB)
3 -The Data Scientist Role.mp4 (94.44 MB)
1 -Signing Up and Getting Started with Azure.mp4 (32.24 MB)
2 -Optimizing and Managing Azure Costs.mp4 (49.43 MB)
3 -Setting Up Your Workspace and Compute Environment.mp4 (48.06 MB)
4 -Creating and Importing Data Assets.mp4 (79.85 MB)
5 -Design the Model in Azure Machine Learning Designer.mp4 (182.74 MB)
6 -Interpreting the Confusion Matrix for Model Evaluation.mp4 (20.38 MB)
7 -Measuring Model Accuracy and AUC.mp4 (38.53 MB)
8 -Evaluating Model Precision, Recall, and F1 Score.mp4 (47.75 MB)
9 -Final Model Evaluation and Insights.mp4 (72.42 MB)
1 -Setting Up the Model Pipeline in Azure ML Designer Part 1.mp4 (77.95 MB)
2 -Setting Up the Regression Model Pipeline in Azure ML Designer Part 2.mp4 (134.35 MB)
3 -Evaluating and Comparing the Regression Models.mp4 (91.36 MB)
4 -Deploying an Inference Pipeline in Azure ML Designer.mp4 (109.48 MB)
1 -Dataset Overview.mp4 (83.31 MB)
2 -Setting Up an AutoML Job.mp4 (88.62 MB)
3 -Evaluating the Best Performing Algorithm.mp4 (74.88 MB)
1 -Creating a Resource Group in Azure.mp4 (85.41 MB)
2 -Setting Up a Language Resource in Azure.mp4 (35.76 MB)
3 -Introduction to Natural Language Processing Concepts.mp4 (33.98 MB)
4 -Extracting Key Information Using Azure Language Studio.mp4 (93.84 MB)
5 -Analyzing Sentiment and Summarizing Text.mp4 (67.31 MB)
6 -Email Enquiries.zip (5.52 KB)
6 -Hands-On Project Training a Text Classification Model with Our Own Data.mp4 (117.67 MB)
7 -Hands-On Project Testing and Evaluating Our Text Classification Model.mp4 (60.45 MB)
1 -Project Introduction and Notebook Setup.mp4 (85.23 MB)
10 -Enhancing Model Performance with Random Over-Sampling.mp4 (180.73 MB)
11 -Summarizing Model Results and Drawing Conclusions.mp4 (88.99 MB)
2 -Exploring Features Investigate the Customer Data.mp4 (62.92 MB)
3 -Exploratory Data Analysis 1 Understanding the Discrete and Categorical Features.mp4 (170.7 MB)
4 -Exploratory Data Analysis 2 Understanding the Continuous Features.mp4 (120.27 MB)
5 -Exploratory Data Analysis 3 Creating Box and Count Plots for Analysis.mp4 (76.41 MB)
6 -Data Preparation Getting the Data Ready for Modeling.mp4 (84.68 MB)
7 -Building a Logistic Regression Model to Predict Customer Churn.mp4 (163.23 MB)
8 -Building a Logistic Regression Model to Predict Customer Churn and Compare.mp4 (47.14 MB)
9 -Applying the XGBoost Algorithm to Our Model.mp4 (116.61 MB)
1 -Download Tableau and Connect to Data.mp4 (45.91 MB)
10 -Building Your Bank Churn Dashboard Part 1.mp4 (86 MB)
11 -Building Your Bank Churn Dashboard Part 2.mp4 (119.32 MB)
12 -Adding Filters and Publishing Your Dashboard.mp4 (108.69 MB)
2 -Creating Cards for Key Metrics.mp4 (83.82 MB)
3 -Calculating the Churn Rate.mp4 (67.54 MB)
4 -Exploring Histograms in Tableau.mp4 (54.29 MB)
5 -Visualizing Credit Scores with a Horizontal Bar Chart.mp4 (90.85 MB)
6 -Grouping and Analyzing Churn by Age with a Bar Chart.mp4 (49.98 MB)
7 -Creating a Donut Chart.mp4 (33.67 MB)
8 -Using a Waterfall Chart to Visualize Tenure.mp4 (57.45 MB)
9 -Creating a Table to Analyze Churn by Products Held Per Customer.mp4 (47.47 MB)
1 -Setting Up Your Python Environment with Anaconda and Jupyter Notebook.mp4 (77.55 MB)
10 -Using For Loops.mp4 (59.35 MB)
11 -Combining For Loops with Conditional Statements.mp4 (120.2 MB)
12 -Defining Functions in Python.mp4 (129.66 MB)
13 -Test your Knowledge Python Basics Q & A.mp4 (140.38 MB)
2 -Jupyter Notebook Overview.mp4 (94.46 MB)
3 -Notebooks.zip (3.9 MB)
3 -Understanding Variables in Python.mp4 (111.41 MB)
4 -Data Types and Their Importance.mp4 (48.01 MB)
5 -Working with Lists.mp4 (39.92 MB)
6 -Exploring Dictionaries.mp4 (54.12 MB)
7 -Tuples and Sets.mp4 (74.07 MB)
8 -Introduction to Arithmetic and Comparison Operators.mp4 (52.19 MB)
9 -Conditional Statements in Python.mp4 (90.88 MB)
1 -Descriptive Statistics Mean, Median, and Mode Explained.mp4 (69.69 MB)
2 -Measuring Spread Standard Deviation and Variance.mp4 (43.89 MB)
3 -Understanding Sampling Techniques in Data Science.mp4 (104.13 MB)
4 -Understanding Variables.mp4 (66.22 MB)
5 -Frequency Distribution Organizing Data for Insights.mp4 (37.8 MB)
1 -Reading CSV Files with Pandas.mp4 (80.16 MB)
10 -Converting Dates in Pandas.mp4 (126.64 MB)
10 -web sales.zip (424.1 KB)
11 -Plotting Data with Pandas.mp4 (73.14 MB)
12 -Test your Knowledge Pandas Q & A.mp4 (125.04 MB)
2 -Using Describe to Summarize Data.mp4 (122.69 MB)
3 -Algebraic Operations in Pandas.mp4 (98.74 MB)
4 -Renaming Columns.mp4 (93.2 MB)
5 -Handling Missing Values.mp4 (153 MB)
6 -Counting Values Understanding Data Distribution.mp4 (58 MB)
7 -Grouping Data Aggregating Insights.mp4 (85.49 MB)
8 -Filtering Data in Pandas.mp4 (138.15 MB)
9 -Applying Functions to Data.mp4 (121.57 MB)
1 -Introduction Signing Up for ChatGPT.mp4 (25.53 MB)
2 -Assigning a Role for ChatGPT.mp4 (60.97 MB)
3 -Crafting Effective Instructions for ChatGPT.mp4 (62.14 MB)
4 -Enhancing Responses by Providing Context.mp4 (91.49 MB)
5 -Improving Responses with Few-Shot Examples.mp4 (112.88 MB)
6 -Limitations and Considerations When Using ChatGPT.mp4 (44.68 MB)
7 -Practical Data Analysis with ChatGPT (Part 1).mp4 (127.7 MB)
8 -Practical Data Analysis with ChatGPT (Part 2).mp4 (66.34 MB)
1 -Introduction to Line Plots.mp4 (146.54 MB)
10 -Visualizing Categorical Data with Bar Plots.mp4 (58.84 MB)
11 -Advanced Scatter Plots with Seaborn.mp4 (80.45 MB)
12 -Correlation Heatmaps.mp4 (124.88 MB)
13 -Using Pair Plots for Multi-Variable Relationships.mp4 (126.19 MB)
14 -Test Your Knowledge Seaborn and Matplotlib Q & A.mp4 (101.67 MB)
2 -Creating Histograms.mp4 (93.07 MB)
3 -Customizing Plot Size (Figsize).mp4 (23.99 MB)
4 -Formatting Your Plots.mp4 (69.07 MB)
5 -Correlation Explained.mp4 (46.63 MB)
6 -Building Basic Scatter Plots.mp4 (116.48 MB)
7 -Creating Subplots.mp4 (158.3 MB)
8 -Box Plots for Data Spread and Outliers.mp4 (57.71 MB)
9 -Using Violin Plots for Distribution.mp4 (26.57 MB)
1 -Understanding the Machine Learning Lifecycle.mp4 (50.33 MB)
2 -Supervised and Unsupervised Learning.mp4 (37.13 MB)
3 -Supervised Learning Explained.mp4 (43.08 MB)
4 -Unsupervised Learning Explained.mp4 (54.73 MB)
5 -Practical Example of Linear Regression in Python - Part 1.mp4 (108.45 MB)
6 -Practical Example of Linear Regression in Python - Part 2.mp4 (146.41 MB)
1 -Data Import and Initial Analysis.mp4 (112.66 MB)
10 -Understanding Mean Squared Error (MSE).mp4 (48.94 MB)
11 -Introduction to Random Forests.mp4 (48.8 MB)
12 -Applying Random Forest to the Housing Project.mp4 (72.44 MB)
13 -Exploring Feature Importance in Random Forests.mp4 (86.64 MB)
2 -Preparing Categorical Data with One-Hot Encoding.mp4 (110.02 MB)
3 -Mapping Geographic Data with Longitude and Latitude.mp4 (46.13 MB)
4 -Scaling Data with Log Transformation.mp4 (71.37 MB)
5 -Feature Engineering.mp4 (26.03 MB)
6 -Understanding Multicollinearity.mp4 (25.75 MB)
7 -Detecting Multicollinearity with a Heatmap.mp4 (140.02 MB)
8 -Training the Regression Model.mp4 (78.65 MB)
9 -Evaluating Model Performance with R-Squared.mp4 (95.23 MB)
1 -Introduction to Hypothesis Testing.mp4 (25.52 MB)
2 -Understanding Null and Alternative Hypotheses.mp4 (32 MB)
3 -Exploring t-Tests and z-Tests.mp4 (16.76 MB)
4 -Understanding the P-Value.mp4 (20.79 MB)
5 -Practical Example of Hypothesis Testing with Python.mp4 (73.24 MB)]
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