Parte 1:

1. Introduction to SQLite and its features.
2. Understanding relational databases and SQL.
3. Installing and setting up SQLite.
4. Creating tables and defining relationships.
5. Inserting, updating, and deleting data.
6. Querying data with SELECT statements.
7. Filtering data with WHERE clause.
8. Joining tables with INNER, OUTER, and CROSS JOIN.
9. Sorting data with ORDER BY clause.
10. Aggregating data with COUNT, SUM, AVG, MAX, and MIN functions.
11. Using SQLite in Python.

Parte 2:

1. Reading CSV files using pandas:

• • pd.read_csv(): reads a CSV file into a pandas DataFrame.

2. Writing CSV files using pandas:

• • df.to_csv(): writes a pandas DataFrame to a CSV file.

3. Reading Excel files using pandas:

• • pd.read_excel(): reads an Excel file into a pandas DataFrame.

4. Writing Excel files using pandas:

• • df.to_excel(): writes a pandas DataFrame to an Excel file.

5. Reading and writing CSV files using the csv module:

• • csv.reader(): reads a CSV file using the csv module.
  • csv.writer(): writes to a CSV file using the csv module.

6. Reading and writing Excel files using the openpyxl module:

• • openpyxl.load_workbook(): loads an Excel file using the openpyxl module.
  • ws.iter_rows(): iterates over the rows of an Excel worksheet using the openpyxl module.
  • ws.append(): writes to an Excel worksheet using the openpyxl module.
  • wb.save(): saves an Excel workbook using the openpyxl module.

1. Support Vector Machine (SVM):

• • Introduction to SVM.
  • Types of SVM Kernels.
  • Mathematical formulation of SVM.
  • SVM Hyperparameters.
  • Implementing SVM in Python with scikit-learn.

2. K-Nearest Neighbors (KNN):

• • Introduction to KNN.
  • Euclidean distance and other distance measures.
  • Choosing the value of K.
  • Implementing KNN in Python with scikit-learn.

1. Decision Trees:

• • Introduction to Decision Trees.
  • Types of Decision Trees.
  • Decision Tree Algorithms.
  • Overfitting and pruning.
  • Implementing Decision Trees in Python with scikit-learn.

2. Random Forest:

• • Introduction to Random Forest.
  • Decision Trees and Ensemble Learning.
  • Random Forest Algorithm.
  • Hyperparameters tuning.
  • Implementing Random Forest in Python with scikit-learn.

1. Perceptron Algorithm:

• • Introduction to Perceptron algorithm.
  • Mathematical formulation of the Perceptron algorithm.
  • Perceptron algorithm for binary classification.
  • Limitations of the Perceptron algorithm.
  • Implementing Perceptron algorithm in Python with NumPy.

1. Convolutional Neural Networks (CNN):

• • Introduction to CNN.
  • Convolutional Layers, Pooling Layers, and Fully Connected Layers.
  • ReLU Activation Function.
  • Backpropagation.
  • Understanding the Importance of Loss Functions.
  • Implementing CNN in Python with Keras.

1. Data Augmentation for Classification:

• • Introduction to Data Augmentation.
  • Techniques for Data Augmentation.
  • Benefits and Limitations of Data Augmentation.
  • Implementing Data Augmentation in Python with Keras.

2. Transfer Learning for Classification:

• • Introduction to Transfer Learning.
  • Types of Transfer Learning.
  • Fine-tuning and Feature Extraction.
  • Choosing a Pretrained Model.
  • Implementing Transfer Learning in Python with Keras.