دوره علم داده: آموزش کامل علم داده به سبک بوت‌کمپ ۲۰۲۶

۱. نمونههای بیشتر از هوش مصنوعی مولد

۲. ادامه با هوش تجاری، یادگیری ماشین و هوش مصنوعی

۳. تجزیه بخشهای اینفوگرافیک علم داده

۴. علم داده و اصطلاحات کسبوکار چرا اینقدر زیاد هستند

۶. هوش مصنوعی سنتی در مقابل هوش مصنوعی مولد

7. تحلیل کسبوکار، تحلیل داده و علم داده یک مقدمه

۱. تکنیکهای کار با داده سنتی

2. نمونههای واقعی داده سنتی

3. تکنیکهای کار با کلان داده

5. نمونههای واقعی کلان داده

6. تکنیکهای هوش تجاری (BI)

7. نمونههای واقعی هوش تجاری (BI)

8. تکنیکهای کار با روشهای سنتی

9. نمونههای واقعی روشهای سنتی

10. تکنیکهای یادگیری ماشین (ML)

11. انواع یادگیری ماشین

12. تکامل و آخرین روندهای یادگیری ماشین (ML)

01. حوزه علم داده – زبان برنامه نویسی قسمت یک

۱. فرمول پایه احتمال

2. محاسبه مقادیر مورد انتظار

۳. محاسبه مقادیر مورد انتظار

۱. مبانی ترکیبیات

2. جایگشتها و نحوه استفاده از آنها

3. جایگشتها و نحوه استفاده از آنها

4. حل تغییرات با تکرار

5. حل تغییرات بدون تکرار

6. حل ترکیبها

7. تقارن ترکیبها

8. حل ترکیبها با فضای نمونه مجزا

9. ترکیبیات در زندگی واقعی – بختآزمایی

10. ترکیبیات در زندگی واقعی – بختآزمایی

۱۱. احتمال – استنباط بیزی

3. Ways Sets Can Interact

5. Intersection of Sets

7. Union of Sets

9. Mutually Exclusive Sets

11. Dependence and Independence of Sets

13. The Conditional Probability Formula

15. The Law of Total Probability

16. The Additive Rule

18. The Multiplication Law

20. Bayes' Law

1. Fundamentals of Probability Distributions

3. Types of Probability Distributions

5. Characteristics of Discrete Distributions

7. Discrete Distributions The Uniform Distribution

9. Discrete Distributions The Bernoulli Distribution

11. Discrete Distributions The Binomial Distribution

13. Discrete Distributions The Poisson Distribution

15. Characteristics of Continuous Distributions

17. Continuous Distributions The Normal Distribution

19. Continuous Distributions The Standard Normal Distribution

21. Continuous Distributions The Students' T Distribution

23. Continuous Distributions The Chi-Squared Distribution

25. Continuous Distributions The Exponential Distribution

27. Continuous Distributions The Logistic Distribution

1. Probability in Finance

2. Probability in Statistics

1. Types of Data

3. Levels of Measurement

5. Categorical Variables - Visualization Techniques

8. Numerical Variables - Frequency Distribution Table

11. The Histogram

14. Cross Tables and Scatter Plots

17. Mean, median and mode

19. Skewness

22. Variance

24. Standard Deviation and Coefficient of Variation

27. Covariance

1. Introduction

2. What is a Distribution

4. The Normal Distribution

6. The Standard Normal Distribution

9. Central Limit Theorem

11. Standard error

1. What are Confidence Intervals

3. Confidence Intervals; Population Variance Known; Z-score

5. Confidence Interval Clarifications

6. Student's T Distribution

8. Confidence Intervals; Population Variance Unknown; T-score

10. Margin of Error

12. Confidence intervals. Two means. Dependent samples

14. Confidence intervals. Two means. Independent Samples (Part 1)

16. Confidence intervals. Two means. Independent Samples (Part 2)

1. Null vs Alternative Hypothesis

4. Rejection Region and Significance Level

6. Type I Error and Type II Error

8. Test for the Mean. Population Variance Known

10. p-value

12. Test for the Mean. Population Variance Unknown

14. Test for the Mean. Dependent Samples

16. Test for the mean. Independent Samples (Part 1)

1. Introduction to Programming

3. Why Python

5. Why Jupyter

7. Installing Python and Jupyter

8. Understanding Jupyter's Interface - the Notebook Dashboard

1. Variables

2. Python Coding Exercises - Part I

8. Numbers and Boolean Values in Python

1. Using Arithmetic Operators in Python

11. The Double Equality Sign

14. How to Reassign Values

20. Add Comments

22. Understanding Line Continuation

24. Indexing Elements

1. Comparison Operators

1. The IF Statement

5. The ELSE Statement

7. The ELIF Statement

1. Defining a Function in Python

2. How to Create a Function with a Parameter

5. Defining a Function in Python - Part II

7. How to Use a Function within a Function

9. Conditional Statements and Functions

11. Functions Containing a Few Arguments

1. Lists

8. Using Methods

14. List Slicing

22. Tuples

1. For Loops

5. While Loops and Incrementing

7. Lists with the range() Function

12. Conditional Statements and Loops

16. Conditional Statements, Functions, and Loops

1. Object Oriented Programming

3. Modules and Packages

5. What is the Standard Library

1. The Linear Regression Model

3. Correlation vs Regression

5. Geometrical Representation of the Linear Regression Model

7. Python Packages Installation

8. First Regression in Python

10. Using Seaborn for Graphs

11. How to Interpret the Regression Table

13. Decomposition of Variability

15. What is the OLS

1. Multiple Linear Regression

3. Adjusted R-Squared

6. Test for Significance of the Model (F-Test)

7. OLS Assumptions

9. A1 Linearity

11. A2 No Endogeneity

13. A3 Normality and Homoscedasticity

14. A4 No Autocorrelation

16. A5 No Multicollinearity

18. Dealing with Categorical Data - Dummy Variables

1. What is sklearn and How is it Different from Other Packages

2. How are we Going to Approach this Section

3. Simple Linear Regression with sklearn

4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table

7. Multiple Linear Regression with sklearn

8. Calculating the Adjusted R-Squared in sklearn

10. Feature Selection (F-regression)

12. Creating a Summary Table with P-values

14. Feature Scaling (Standardization)

15. Feature Selection through Standardization of Weights

16. Predicting with the Standardized Coefficients

18. Underfitting and Overfitting

1. Practical Example Linear Regression (Part 1)

2. Practical Example Linear Regression (Part 2)

4. Practical Example Linear Regression (Part 3)

6. Practical Example Linear Regression (Part 4)

1. Introduction to Logistic Regression

2. A Simple Example in Python

3. Logistic vs Logit Function

4. Building a Logistic Regression

6. An Invaluable Coding Tip

7. Understanding Logistic Regression Tables

9. What do the Odds Actually Mean

10. Binary Predictors in a Logistic Regression

12. Calculating the Accuracy of the Model

14. Underfitting and Overfitting

1. Introduction to Cluster Analysis

2. Some Examples of Clusters

3. Difference between Classification and Clustering

1. K-Means Clustering

2. A Simple Example of Clustering

4. Clustering Categorical Data

6. How to Choose the Number of Clusters

8. Pros and Cons of K-Means Clustering

9. To Standardize or not to Standardize

10. Relationship between Clustering and Regression

11. Market Segmentation with Cluster Analysis (Part 1)

12. Market Segmentation with Cluster Analysis (Part 2)

1. Types of Clustering

2. Dendrogram

1. Traditional data science methods and the role of ChatGPT

2. How to install ChatGPT

3. How ChatGPT can boost your productivity

4. Data Preprocessing with ChatGPT

5. First attempt at machine learning with ChatGPT

6. Analyzing a client database with ChatGPT in Python

7. Analyzing a client database with ChatGPT in Python – analyzing top products

8. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM

9. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot

10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec

12. Hypothesis testing with ChatGPT

13. Marvels comic book database Intro to Regular Expressions (RegEx)

14. Decoding comic book data Python Regular Expressions and ChatGPT

16. Algorithm recommendation Movie Database Analysis with ChatGPT

17. Algorithm recommendation recommendation engine for movies with ChatGPT

18. Ethical principles in data and AI utilization

1. Intro to the Case Study

2. The Naive Bayes Algorithm

3. Tokenization and Vectorization

4. Imbalanced Data Sets

5. Overcome Imbalanced Data in Machine Learning

6. Loading the Dataset and Preprocessing

7. Optimizing User Reviews Data Preprocessing & EDA

8. Reg Ex for Analyzing Text Review Data

9. Understanding Differences between Multinomial and Bernouilli Naive Bayes

10. Machine Learning with Naïve Bayes (First Attempt)

11. Machine Learning with Naïve Bayes – converting the problem to a binary one

1. What is a Matrix

3. Scalars and Vectors

5. Linear Algebra and Geometry

7. Arrays in Python - A Convenient Way To Represent Matrices

8. What is a Tensor

10. Addition and Subtraction of Matrices

12. Errors when Adding Matrices

13. Transpose of a Matrix

14. Dot Product

15. Dot Product of Matrices

1. Introduction to Neural Networks

3. Training the Model

5. Types of Machine Learning

7. The Linear Model (Linear Algebraic Version)

9. The Linear Model with Multiple Inputs

11. The Linear model with Multiple Inputs and Multiple Outputs

13. Graphical Representation of Simple Neural Networks

15. What is the Objective Function

17. Common Objective Functions L2-norm Loss

19. Common Objective Functions Cross-Entropy Loss

21. Optimization Algorithm 1-Parameter Gradient Descent

1. Basic NN Example (Part 1)

2. Basic NN Example (Part 2)

3. Basic NN Example (Part 3)

1. How to Install TensorFlow 2.0

2. TensorFlow Outline and Comparison with Other Libraries

3. TensorFlow 1 vs TensorFlow 2

4. A Note on TensorFlow 2 Syntax

5. Types of File Formats Supporting TensorFlow

6. Outlining the Model with TensorFlow 2

7. Interpreting the Result and Extracting the Weights and Bias

1. What is a Layer

2. What is a Deep Net

3. Digging into a Deep Net

4. Non-Linearities and their Purpose

5. Activation Functions

5. Activation Functions

6. Activation Functions Softmax Activation

7. Backpropagation

1. What is Overfitting

2. Underfitting and Overfitting for Classification

3. What is Validation

4. Training, Validation, and Test Datasets

5. N-Fold Cross Validation

1. What is Initialization

2. Types of Simple Initializations

1. Stochastic Gradient Descent

2. Problems with Gradient Descent

3. Momentum

4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate

5. Learning Rate Schedules Visualized

6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

1. Preprocessing Introduction

2. Types of Basic Preprocessing

3. Standardization

4. Preprocessing Categorical Data

1. MNIST The Dataset

2. MNIST How to Tackle the MNIST

3. MNIST Importing the Relevant Packages and Loading the Data

4. MNIST Preprocess the Data - Create a Validation Set and Scale It

6. MNIST Preprocess the Data - Shuffle and Batch

8. MNIST Outline the Model

9. MNIST Select the Loss and the Optimizer

10. MNIST Learning

1. Business Case Exploring the Dataset and Identifying Predictors

2. Business Case Outlining the Solution

3. Business Case Balancing the Dataset

4. Business Case Preprocessing the Data

6. Business Case Load the Preprocessed Data

8. Business Case Learning and Interpreting the Result

9. Business Case Setting an Early Stopping Mechanism

1. Summary on What You've Learned

2. What's Further out there in terms of Machine Learning

4. An overview of CNNs

5. An Overview of RNNs

2. How to Install TensorFlow 1

4. TensorFlow Intro

5. Actual Introduction to TensorFlow

6. Types of File Formats, supporting Tensors

7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases

8. Basic NN Example with TF Loss Function and Gradient Descent

1. MNIST What is the MNIST Dataset

2. MNIST How to Tackle the MNIST

3. MNIST Relevant Packages

4. MNIST Model Outline

5. MNIST Loss and Optimization Algorithm

6. Calculating the Accuracy of the Model

7. MNIST Batching and Early Stopping

8. MNIST Learning

1. Business Case Getting Acquainted with the Dataset

2. Business Case Outlining the Solution

3. The Importance of Working with a Balanced Dataset

4. Business Case Preprocessing

6. Creating a Data Provider

7. Business Case Model Outline

8. Business Case Optimization

9. Business Case Interpretation

10. Business Case Testing the Model

1. What are Data, Servers, Clients, Requests, and Responses

3. What are Data Connectivity, APIs, and Endpoints

5. Taking a Closer Look at APIs

7. Communication between Software Products through Text Files

1. Game Plan for this Python, SQL, and Tableau Business Exercise

2. The Business Task

2. Importing the Absenteeism Data in Python

3. Checking the Content of the Data Set

4. Introduction to Terms with Multiple Meanings

6. Using a Statistical Approach towards the Solution to the Exercise

7. Dropping a Column from a DataFrame in Python

10. Analyzing the Reasons for Absence

11. Obtaining Dummies from a Single Feature

15. More on Dummy Variables A Statistical Perspective

16. Classifying the Various Reasons for Absence

17. Using .concat() in Python

20. Reordering Columns in a Pandas DataFrame in Python

23. Creating Checkpoints while Coding in Jupyter

26. Analyzing the Dates from the Initial Data Set

27. Extracting the Month Value from the Date Column

28. Extracting the Day of the Week from the Date Column

30. Analyzing Several Straightforward Columns for this Exercise

31. Working on Education, Children, and Pets

1. Exploring the Problem with a Machine Learning Mindset

2. Creating the Targets for the Logistic Regression

3. Selecting the Inputs for the Logistic Regression

4. Standardizing the Data

5. Splitting the Data for Training and Testing

6. Fitting the Model and Assessing its Accuracy

7. Creating a Summary Table with the Coefficients and Intercept

8. Interpreting the Coefficients for Our Problem

9. Standardizing only the Numerical Variables (Creating a Custom Scaler)

10. Interpreting the Coefficients of the Logistic Regression

11. Backward Elimination or How to Simplify Your Model

12. Testing the Model We Created

13. Saving the Model and Preparing it for Deployment

2. Analyzing Age vs Probability in Tableau

4. Analyzing Reasons vs Probability in Tableau

2. Deploying the 'absenteeism_module' - Part I

1. Using the .format() Method

2. Python Coding Exercises - Part II

8. Iterating Over Range Objects

9. Introduction to Nested For Loops

10. Triple Nested For Loops

18. List Comprehensions

۱. مقدمهای بر سریهای پانداس

۱۳. کار با متدها در پایتون – بخش اول

۱۴. کار با متدها در پایتون – بخش دوم

۱۷. پارامترها و آرگومانها در پانداس

۱۸. استفاده از .unique() و .nunique()

۱۹. استفاده از .sort_values()

۲۰. مقدمهای بر دیتافریمهای پانداس – بخش اول

۲۱. مقدمهای بر دیتافریمهای پانداس – بخش دوم

۲۲. دیتافریمهای پانداس – ویژگیهای رایج

۲۳. انتخاب داده در دیتافریمهای پانداس

۲۴. دیتافریمهای پانداس – ایندکسگذاری با .iloc