Breaking into Data Science & Machine Learning with Python

سرفصل های دوره

If you have any quantitative, STEM or business background this course is for you to break into data science using Python

1. Data Science Tool Box

1. Welcome to the course!

2.1 Anaconda download link for windows.html

2.2 anaconda_installation.pdf

2. Installing Anaconda

3.1 exploring_jupyter_notebook.pdf

3. Exploring Jupyter Notebook

4.1 first_try.zip

4. Let's Try Some Coding Together in Jupyter Note Book

2. Python Crash Course

1. Python Crash Course Overview

2.1 python-vocab.pdf

2.2 welcome_to_python.zip

2. Simple Input and Output in Python

3.1 strings.zip

3. String in Python

4.1 numerics.zip

4. Playing with Numbers

5.1 list.zip

5. List in Python

6.1 tuple.zip

6. Tuple

7.1 dictionary.zip

7. Dictionary in Python

8.1 dictionary_2.zip

8. More on Python Dictionary

9.1 boolean.zip

9. Boolean in Python

10.1 comparison_operators.zip

10. Example of Boolean Data Types

11.1 if_else.zip

11. Conditional Statement in Python if else

12.1 loop.zip

12. Loop in Python

13.1 function.zip

13. How to Write Function in Python

3. Obtaining Data

1. Overview of data obtaining, cleaning and exploratory analysis

2.1 loading_data_from_flat_files.zip

2. Reading Data From CSV File Part 1

3.1 loading_data_from_flat_files.zip

3. Reading Data From CSV File Part 2

4.1 loading_data_from_excel.zip

4. Reading Data From Excel File

5.1 load_from_sql.zip

5. Obtaining Data From SQL Server

6.1 loading data from api.zip

6. Obtaining Data From API

4. Cleaning Data

1.1 data sanity check.zip

1. Sanity Check

2.1 data_cleaning.zip

2. Data Cleaning

3.1 ex_cleaning_eda.zip

3. Data Cleaning Excercise

4.1 sol_ex_cleaning_eda-copy1.zip

4. Solution to Data Cleaning Exercise, Part 1

5.1 apply_function.zip

5. Pandas Apply Function

6.1 sol_ex_cleaning_eda-copy1.zip

6. Solution to Data Cleaning Exercise, Part 2

5. Exploratory Data Analysis (EDA)

1. Exploratory Data Analysis Part 1

2. Exploratory Data Analysis Part 2

3. Exercise on EDA

4. Panda's Group By Function

5. Solution to EDA Exercise

6. Data Visualization

1. Introduction to Data Visualization

2. Line Plots

3. Different Types of Chart

4. Categorical Data Visualization Part 1 - Distribution Plots

5. Categorical Data Visualization Part 2 - Violin Plots

6. Categorical Data Visualization Part 3 - Violin Plots

7. Categorical Data Visualization Part 4 - Bar Plots and more

8. Spatial Data Visualization Part 1

9. Spatial Data Visualization Part 2

10. Time Series Data Visualization Part 1

11. Time Series Data Visualization Part 2 - Seaborn Example

12. Time Series Data Visualization Part 3 - Plotly Example

13. Plotly Installation Guideline

7. Data WranglingManipulation

1. Data Wrangling Introduction

2. SlicingFiltering Part 1

3. SlicingFiltering Part 2

4. SlicingFiltering Part 3

5. SlicingFiltering Part 4

6. SlicingFiltering Part 5

7. SlicingFiltering Part 6

8. Aggregation

9. Aggregation Excercise

10. Aggregation Exercise Solution

11. Reshaping Part 1- Pivot

12. Reshaping Part 2 (Stacking)

13. Reshaping Part 3 (Unstacking)

14. MergeJoinConcatenation

15. Reshaping Exercise

16. Reshaping Exercise Solution

8. Predictive Analysis with Machine Learning

1.1 iris.zip

1.2 machine_learning_overview.zip

1. Introduction to Machine Learning with an Example

2. Different Types of Machine Learning

9. Linear Regression

1. Introduction to Linear Regression

2.1 diabetes.csv

2.2 linear_regression_1.zip

2. Linear Regression Part 1

3.1 diabetes.csv

3.2 linear_regression_2.zip

3. Linear Regression Part 2

4. Model Metrics

5.1 kc_house_data.csv

5.2 lin_reg_ex.zip

5. Excercise

6.1 kc_house_data.csv

6.2 lin_reg_ex_eda.zip

6. Exploratory Data Analysis for the Excercise

7.1 kc_house_data.csv

7.2 lin_reg_ex_fe.zip

7. Solution of Exercise Feature Engineering

8.1 lin_reg_ex_model.zip

8. Solution of Exercise Model Building

9.1 lin_reg_ex_model_improvement.zip

9. Solution of Exercise Model Enhancement

10. Logistic Regression

1.1 diabetes.csv

1.2 log_reg1.zip

1. Introduction to Logistic Regression With an Example

2.1 log_reg2_sigmoid.zip

2. Explaining Sigmoid Function The Math Behind the Magic

3.1 log_reg2_sigmoid.zip

3. Explaining Math of LogitLogistic Function

4.1 diabetes.csv

4.2 log_reg_model.zip

4. Logistic Regression Model Building

5.1 log_reg_model_eval.zip

5. Model Evaluation

6.1 log_reg_model_eval.zip

6. Model Evaluation Part 2

7.1 log_reg_model_accuracy.zip

7. Explaining Math of Model Accuracy Calculation

8.1 log_reg_model_confusion_matrix.zip

8. Confusion Matrix Math and Code

9.1 log_reg_precision_recall.zip

9. PrecisionRecall Calculation

10.1 log_reg_precision_recall.zip

10. F-1 Score

11.1 log_reg_roc.zip

11. ROCAUC

12.1 log_reg_metrices.zip

12. Summarizing Model Performance Metrices

13.1 log_reg_cv.zip

13. Cross Validation

14.1 log_reg_model_selection.zip

14. Model Selection

11. Multinomial Logistic Regression

1. Introduction to Multinomial Logistic Regression

2. Exercise

3.1 human_acticvity_calssifier.zip

3.2 UCI HAR Dataset.zip

3. Solution Model Building

4.1 human_acticvity_calssifier_excercise.zip

4. Accuracy Calculation

5.1 human_acticvity_calssifier_excercise.zip

5. Confusion Matrix

6.1 human_acticvity_calssifier_excercise.zip

6. PrecisionRecall

7. ROC and AUC

12. Naive Bayes Algorithm

1. Introduction to Naive Bayes classifier

2. Model Building

3. Naive Bayes vs Logistic Regression

4. Multinomial Naive Bayes classifier

5. Text Classifier A Practical Example

13. Decision Tree Based Algorithm

1. Decision Tree Based Algorithm

2. Random Forest

3. Tree Visualization for Random Forest

4. Comparison of Classifiers

14. K-NN Classifier

1. K-NN Classifier

2. Excercise

15. Important Machine Learning Concepts

1. Bias

2. Variance

16. Journey to be a Data Scientist

1. Data Science Career Prospects and Path

2. Data Science Job Search

3. Thank you!

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تولید کننده: Udemy-Training