Python AI, ML etc

### 1. Input and Output in Python

   - The `input()` Function:

     - Accepting user input as strings or converting them to other types (int, float, etc.)

     - Using input for collecting user-defined data

   - The `print()` Function:

     - Displaying output to the console

     - Printing multiple values, strings, numbers, and variables

   - String Formatting:

     - Using f-strings for inserting variables into strings

     - Performing calculations and formatting numbers with f-strings

   - Examples:

     ```python

     name = input("Enter your name: ")

     age = int(input("Enter your age: "))

     print(f"Namaste {name}, you are {age} years old.")

     ```

### 2. Python Basics

   - Introduction to Python

   - Data Types and Variables

   - Control Structures (if-else, loops)

   - Functions and Modules

   - List Comprehensions and Lambda Functions

### 3. Python Data Structures

   - Lists, Tuples, Dictionaries, and Sets

   - String Operations

   - File Handling

   - Using `input()` for dynamic data structure creation and `print()` for displaying them

### 4. Numpy for Numerical Computing

   - Numpy Arrays and Array Operations

   - Mathematical and Statistical Functions

   - Broadcasting and Vectorization

### 5. Pandas for Data Manipulation

   - Series and DataFrames

   - Data Cleaning and Preprocessing

   - Handling Missing Data

   - Grouping, Aggregation, and Pivot Tables

### 6. Data Visualization

   - Introduction to Matplotlib and Seaborn

   - Line Plots, Bar Charts, Histograms

   - Heatmaps, Pairplots, and Scatterplots

### 7. Introduction to Machine Learning

   - Overview of Machine Learning

   - Supervised vs. Unsupervised Learning

   - Key Terminologies (Features, Labels, Models)

### 8. Scikit-Learn for Machine Learning

   - Linear and Logistic Regression

   - K-Nearest Neighbors (KNN)

   - Decision Trees and Random Forests

   - Support Vector Machines (SVM)

   - Model Evaluation (Cross-Validation, Confusion Matrix, ROC-AUC)

### 9. Clustering and Dimensionality Reduction

   - K-Means Clustering

   - Hierarchical Clustering

   - Principal Component Analysis (PCA)

### 10. Working with Large Datasets

   - Data Wrangling with Pandas

   - Feature Engineering and Feature Selection

   - Dealing with Imbalanced Data (SMOTE, Oversampling)

### 11. Deep Learning with TensorFlow and Keras

   - Introduction to Neural Networks

   - Backpropagation and Gradient Descent

   - Building Neural Networks with Keras

   - Convolutional Neural Networks (CNNs)

   - Recurrent Neural Networks (RNNs)

### 12. Natural Language Processing (NLP)

   - Text Preprocessing (Tokenization, Stop Words)

   - Bag of Words (BoW) and TF-IDF

   - Word Embeddings (Word2Vec, GloVe)

   - Text Classification with NLP

### 13. Time Series Analysis

   - Time Series Data and Trends

   - ARIMA and SARIMA Models

   - Forecasting Future Trends

### 14. Reinforcement Learning (Optional Advanced)

   - Introduction to Reinforcement Learning

   - Q-Learning and Deep Q-Networks (DQN)

   - Markov Decision Processes

### 15. Model Deployment and Monitoring

   - Exporting Models for Production (Pickle, Joblib)

   - Model Serving with Flask or FastAPI

   - Monitoring Model Performance Over Time

### 16. Ethics and Bias in AI

   - Understanding Bias in Data and Models

   - Fairness and Transparency in AI

   - Responsible AI Practices

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