Artificial Intelligence(AI)

Overview of AI

Definition and History

Applications of AI in Various Industries

AI vs. Machine Learning vs. Deep Learning

Setting Up the Environment

Installing Python

Introduction to Jupyter Notebooks

Installing Required Libraries (NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras)

Basic Python Syntax

Variables and Data Types

Basic Operators

Conditional Statements

Loops

Python Data Structures

Lists

Tuples

Dictionaries

Sets

Functions and Modules

Defining and Calling Functions

Importing Modules

Using Built-in Functions and Libraries

Data Collection

Importing Data from CSV, Excel, and SQL Databases

Web Scraping Basics with BeautifulSoup and Scrapy

Using APIs for Data Collection (requests)

Data Wrangling

Handling Missing Values

Data Cleaning Techniques

Data Transformation and Normalization

Combining and Merging Datasets

Introduction to EDA

Importance of EDA

Steps in EDA

Descriptive Statistics

Measures of Central Tendency (Mean, Median, Mode)

Measures of Dispersion (Standard Deviation, Variance, Range)

Skewness and Kurtosis

Data Visualization

Introduction to Matplotlib and Seaborn

Creating Basic Plots (Line, Bar, Histogram, Box Plot)

Customizing Plots (Titles, Labels, Legends)

Introduction to Supervised Learning

Definition and Examples

Steps in Building Supervised Learning Models

Regression Algorithms

Linear Regression

Polynomial Regression

Support Vector Regression (SVR)

Classification Algorithms

Logistic Regression

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVM)

Decision Trees and Random Forests

Model Evaluation

Train-Test Split

Cross-Validation

Performance Metrics (Accuracy, Precision, Recall, F1-Score)

Introduction to Unsupervised Learning

Definition and Examples

Steps in Building Unsupervised Learning Models

Clustering Algorithms

k-Means Clustering

Hierarchical Clustering

DBSCAN

Dimensionality Reduction

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Autoencoders

Introduction to Neural Networks

Biological Inspiration

Perceptron and Multilayer Perceptrons (MLP)

Activation Functions

Deep Learning Frameworks

Introduction to TensorFlow and Keras

Setting Up the Environment

Building Neural Networks

Creating and Training Neural Networks with Keras

Evaluating Neural Networks

Tuning Hyperparameters

Introduction to CNNs

Convolutional Layers

Pooling Layers

Fully Connected Layers

Building CNNs with Keras

Image Classification with CNNs

Data Augmentation Techniques

Transfer Learning with Pre-trained Models

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Introduction to RNNs

Recurrent Layers and LSTMs

GRUs (Gated Recurrent Units)

Building RNNs with Keras

Sequence Prediction

Time Series Forecasting

Text Generation

Introduction to NLP

Text Preprocessing (Tokenization, Lemmatization, Stopwords Removal)

Bag of Words and TF-IDF

NLP with NLTK and SpaCy

Sentiment Analysis

Named Entity Recognition (NER)

Text Classification

Introduction to Reinforcement Learning

Key Concepts (Agent, Environment, Rewards)

Markov Decision Processes (MDP)

Deep Reinforcement Learning

Q-Learning

Deep Q-Networks (DQN)

Policy Gradients

AI for Image Recognition

Object Detection and Image Segmentation

Facial Recognition

AI for Text Processing

Chatbots and Virtual Assistants

Language Translation

AI for Time Series Analysis

Stock Market Prediction

Anomaly Detection

Ethics in AI

Bias and Fairness

Privacy Concerns

Transparency and Accountability

Best Practices

Model Deployment

Model Monitoring and Maintenance

Continuous Learning and Improvement

Practice Exercises for Each Module

Real-world Problem Solving

Analyzing Real-world Datasets

Interpreting Results and Drawing Conclusions

Building Small AI Applications

Implementing AI Models in Real-world Scenarios

Comprehensive Project Covering Multiple Modules

Real-world Problem Solving and Implementation

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