Python for Data Science & Machine Learning: Zero to Hero

سرفصل های دوره

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more!

1. Introduction

1. Welcome to the Python for Data Science & ML bootcamp!

2. Python A Brief Overview

3. The Python Installation Procedure

4. What Jupyter is

5. Set up Anaconda on Different Operating Systems

6. How to integrate Python into Jupyter

7. Handling Directories in Jupyter Notebook

8. Input & Output

9. Working with different datatypes

10. Variables

11. Arithmetic Operators

12. Comparison Operators

13. Logical Operators

14. Conditional statements

15. Loops

16. Sequences Part 1 Lists

17. Sequences Part 2 Dictionaries

18. Sequences Part 3 Tuples

19. Functions Part 1 Built-in Functions

20. Functions Part 2 User-defined Functions

2. The Must-Have Python Data Science Libraries

1. Completing Library Setup

2. Library Importing

3. Pandas A Data Science Library

4. NumPy A Data Science Library

5. NumPy vs. Pandas

6. Matplotlib Library for Data Science

7. Seaborn Library for Data Science

3. NumPy Mastery Everything you need to know about NumPy

1. Intro to NumPy arrays

2. Creating NumPy arrays

3. Indexing NumPy arrays

4. Array shape

5. Iterating Over NumPy Arrays

6. Basic NumPy arrays zeros()

7. Basic NumPy arrays ones()

8. Basic NumPy arrays full()

9. Adding a scalar

10. Subtracting a scalar

11. Multiplying by a scalar

12. Dividing by a scalar

13. Raise to a power

14. Transpose

15. Element-wise addition

16. Element-wise subtraction

17. Element-wise multiplication

18. Element-wise division

19. Matrix multiplication

20. Statistics

4. DataFrames and Series in Python's Pandas

1. What is a Python Pandas DataFrame

2. What is a Python Pandas Series

3. DataFrame vs Series

4. Creating a DataFrame using lists

5. Creating a DataFrame using a dictionary

6. Loading CSV data into python

7. Changing the Index Column

8. Inplace

9. Examining the DataFrame Head & Tail

10. Statistical summary of the DataFrame

11. Slicing rows using bracket operators

12. Indexing columns using bracket operators

13. Boolean list

14. Filtering Rows

15. Filtering rows using & and operators

16. Filtering data using loc()

17. Filtering data using iloc()

18. Adding and deleting rows and columns

19. Sorting Values

20. Exporting and saving pandas DataFrames

21. Concatenating DataFrames

22. groupby()

5. Data Cleaning Techniques for Better Data

1. Introduction to Data Cleaning

2. Quality of Data

3. Examples of Anomalies

4. Median-based Anomaly Detection

5. Mean-based anomaly detection

6. Z-score-based Anomaly Detection

7. Interquartile Range for Anomaly Detection

8. Dealing with missing values

9. Regular Expressions

10. Feature Scaling

6. Exploratory Data Analysis in Python

1. Introduction

2. What is Exploratory Data Analysis

3. Univariate Analysis

4. Univariate Analysis Continuous Data

5. Univariate Analysis Categorical Data

6. Bivariate analysis Continuous & Continuous

7. Bivariate analysis Categorical & Categorical

8. Bivariate analysis Continuous & Categorical

9. Detecting Outliers

10. Categorical Variable Transformation

7. Python for Time-Series Analysis A Primer

1. Introduction to Time Series

2. Getting stock data using yfinance

3. Converting a Dataset into Time Series

4. Working with Time Series

5. Time Series Data Visualization with Python

8. Python for Data Visualization Library Resources, and Sample Graphs

1. Introduction

2. Setting Up Matplotlib

3. Plotting Line Plots using Matplotlib

4. Title, Labels & Legend

5. Plotting Histograms

6. Plotting Bar Charts

7. Plotting Pie Charts

8. Plotting Scatter Plots

9. Plotting Log Plots

10. Plotting Polar Plots

11. Handling Dates

12. Creating multiple subplots in one figure

9. The Basics of Machine Learning

1. Why do we need machine learning

2. Machine Learning Use Cases

3. Approaches to Machine Learning

4. What is Supervised learning

5. What is Unsupervised learning

6. Supervised learning vs Unsupervised learning

10. Simple Linear Regression with Python

1. Introduction to regression

2. How Does Linear Regression Work

3. Line representation

4. Implementation in python Importing libraries & datasets

5. Implementation in python Distribution of the data

6. Implementation in python Creating a linear regression object

11. Multiple Linear Regression with Python

1. Understanding Multiple linear regression

2. Exploring the dataset

3. Encoding Categorical Data

4. Splitting data into Train and Test Sets

5. Training the model on the Training set

6. Predicting the Test Set results

7. Evaluating the performance of the regression model

8. Root Mean Squared Error in Python

12. Classification Algorithms K-Nearest Neighbors

1. Introduction to classification

2. K-Nearest Neighbors algorithm

3. Example of KNN

4. K-Nearest Neighbours (KNN) using python

5. Importing required libraries

6. Importing the dataset

7. Splitting data into Train and Test Sets

8. Feature Scaling

9. Importing the KNN classifier

10. Results prediction & Confusion matrix

13. Classification Algorithms Decision Tree

1. Introduction to decision trees

2. What is Entropy

3. Exploring the dataset

4. Decision tree structure

5. Importing libraries & datasets

6. Encoding Categorical Data

7. Splitting data into Train and Test Sets

8. Results Prediction & Accuracy

14. Classification Algorithms Logistic regression

1. Introduction

2. Implementation steps

3. Importing libraries & datasets

4. Splitting data into Train and Test Sets

5. Pre-processing

6. Training the model

7. Results prediction & Confusion matrix

8. Logistic Regression vs Linear Regression

15. Clustering

1. Introduction to clustering

2. Use cases

3. K-Means Clustering Algorithm

4. Elbow method

5. Steps of the Elbow method

6. Implementation in python

7. Hierarchical clustering

8. Density-based clustering

9. Implementation of k-means clustering in python

10. Importing the dataset

11. Visualizing the dataset

12. Defining the classifier

13. 3D Visualization of the clusters

14. 3D Visualization of the predicted values

15. Number of predicted clusters

16. Recommender System

1. Introduction

2. Collaborative Filtering in Recommender Systems

3. Content-based Recommender System

4. Importing libraries & datasets

5. Merging datasets into one dataframe

6. Sorting by title and rating

7. Histogram showing number of ratings

8. Frequency distribution

9. Jointplot of the ratings and number of ratings

10. Data pre-processing

11. Sorting the most-rated movies

12. Grabbing the ratings for two movies

13. Correlation between the most-rated movies

14. Sorting the data by correlation

15. Filtering out movies

16. Sorting values

17. Repeating the process for another movie

17. Conclusion

1. Conclusion

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