Name: Artificial Intelligence and Machine Learning with Python
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Python

Install Python and Set Up a Development Environment

Write a 'Hello, World!' Program

Practice Basic Arithmetic Operations in Python

Use Python’s Interactive Shell for Basic Commands

Explore Python’s Documentation and Help Functions

Section 1: Introduction to Python Basics, Installation, and Basic Syntax

Declare and Use Different Data Types (int, float, str, list, tuple, dict)

Convert Between Data Types

Use type() to Check Variable Types

Practice Variable Assignments and Basic Operations

Implement Basic Arithmetic Operations with Different Data Types

Section 2: Understanding Python Data Types, Variables, and Type Conversion

Write Conditional Statements Using if, elif, and else

Implement Loops (for and while) to Iterate Over Data

Use Break and Continue Statements Within Loops

Practice List Comprehensions with Control Structures

Create a Program That Uses Both Conditionals and Loops

Section 3: Learn About Conditional Statements and Loops in Python

Define and Call a Simple Function

Create Functions with Parameters and Return Values

Practice Using Default Arguments and Keyword Arguments

Implement Variable-Length Argument Functions

Explore Lambda Functions and Their Uses

Section 4: Definition and Usage of Functions, Parameters, and Return Values

Define a Simple Class and Create Instances

Implement Inheritance and Method Overriding

Use Encapsulation and Property Methods

Create and Use Class Methods and Static Methods

Practice Designing a Small OOP Project

Section 5: Introduction to Classes, Objects, Inheritance, and Polymorphism

Create and Use Custom Python Modules

Explore and Import Standard Library Modules

Practice Using Third-Party Packages with pip

Create and Use Packages with Multiple Modules

Understand the __init__.py File in Packages

Section 6: Working with Python Modules and Packages, Importing and Using Libraries

Read and Write Text Files

Handle File Exceptions Using try-except

Read and Write CSV Files Using the csv Module

Work with JSON Files for Data Storage

Practice File Operations (Open, Close, Read, Write)

Section 7: Reading from and Writing to Files, Handling Different File Types

Create and Manipulate Lists and List Comprehensions

Work with Sets and Set Operations

Implement and Use Dictionaries Effectively

Practice Nested Data Structures

Explore Common Data Structure Methods and Operations

Section 8: Python Data Structures

Create and Use Decorators for Functions

Implement Generators and Understand Their Use Cases

Explore Context Managers with the with Statement

Practice Partial Functions and Function Introspection

Use Regular Expressions for Pattern Matching

Section 9: Advanced Topics Including Decorators, Generators, and Context Managers

Install and Use NumPy for Array Operations

Perform Basic Data Manipulation with Pandas

Use Map, Reduce, and Filter Functions

Explore Common Functions and Methods in NumPy and Pandas

Apply Pandas for Data Cleaning and Analysis

Section 10: Introduction to Using External Libraries Such as NumPy and Pandas

Artificial Intelligence and Machine Learning

Define Key Concepts of Machine Learning (ML), Including Supervised, Unsupervised, and Reinforcement Learning

Explore the Machine Learning Pipeline: Data Collection, Preprocessing, Model Building, and Evaluation

Identify and Categorize Different ML Tasks: Classification, Regression, and Clustering

Implement a Basic ML Model (e.g., Linear Regression) Using Scikit-learn

Compare the Approaches and Real-World Applications of Supervised vs. Unsupervised Learning

Section 1: Introduction to Machine Learning

Handle Missing Data by Using Strategies Like Mean Imputation or Removing Missing Values

Normalize and Standardize Data to Improve Model Performance

Perform Feature Encoding Using Methods Like One-Hot Encoding and Label Encoding

Split Data into Training and Testing Sets While Considering Class Imbalances

Apply Feature Scaling and Transformation Techniques to Ensure Uniform Data Distribution

Section 2: Learn Data Preprocessing Techniques

Calculate and Interpret Summary Statistics for a Dataset (Mean, Median, Mode, Variance)

Visualize Data Distribution Using Histograms, Scatter Plots, and Box Plots

Analyze Feature Correlations Using a Correlation Matrix and Heatmaps

Detect and Handle Outliers Using Methods Like IQR and Z-Score

Use Pandas to Clean and Organize Data for Further Analysis

Section 3: Exploratory Data Analysis (EDA)

Implement Logistic Regression to Classify Binary Data

Build and Visualize a Decision Tree Classifier

Train a Random Forest Model and Analyze the Importance of Features

Apply Support Vector Machines (SVM) for Classification on a Real-World Dataset

Evaluate Model Performance Using Metrics Like Accuracy, Precision, Recall, F1 Score, and Confusion Matrix

Section 4: Supervised Learning - Classification

Perform K-Means Clustering and Analyze the Resulting Clusters

Implement Hierarchical Clustering and Visualize the Dendrogram

Reduce Dimensionality Using PCA and Interpret the Results

Implement Association Rule Learning (e.g., Apriori) for Market Basket Analysis

Evaluate Clustering Results Using Metrics Like Silhouette Score and Interpret the Clusters

Section 5: Unsupervised Learning - Clustering and Dimensionality Reduction Techniques

Implement AdaBoost for Improving Model Performance on a Classification Task

Train a Gradient Boosting Model for a Regression Problem

Use Stacking to Combine Predictions from Multiple Models and Improve Accuracy

Compare and Contrast the Performance of Ensemble Methods with Individual Base Models

Section 6: Combine Multiple Models to Create Robust and Accurate Predictions

Plot ROC Curves for Different Classifiers and Calculate the AUC for Each

Use GridSearchCV to Tune Hyperparameters and Identify the Best Model Configuration

Implement k-Fold Cross-Validation to Assess Model Robustness

Compare Different Evaluation Metrics Like RMSE, MAE, and Accuracy

Detect and Mitigate Overfitting Using Techniques Like Regularization and Early Stopping

Section 7: Model Evaluation and Tuning

Build and Train a Simple Feedforward Neural Network Using TensorFlow/Keras

Implement a Convolutional Neural Network (CNN) to Classify Images (e.g., MNIST Dataset)

Develop a Recurrent Neural Network (RNN) for Sequence Prediction Tasks

Apply Transfer Learning Using a Pre-trained Model (e.g., VGG16) for a Custom Task

Fine-tune Deep Learning Models to Improve Accuracy and Reduce Overfitting

Section 8: Deep Learning - Building Neural Networks

Tokenize and Normalize Text Data Using Techniques Like Stemming and Lemmatization

Implement a Text Classification Model Using TF-IDF Features and Naive Bayes

Build a Named Entity Recognition (NER) Model to Extract Entities from Text

Use Word Embeddings (Word2Vec, GloVe) to Analyze the Semantic Meaning of Words

Perform Sentiment Analysis on Text Data and Visualize the Results

Section 9: Natural Language Processing (NLP)

Decompose a Time Series into Trend, Seasonality, and Residual Components

Implement an ARIMA Model to Forecast Future Values of a Time Series

Apply Exponential Smoothing Methods Like Holt-Winters for Time Series Forecasting

Analyze and Detect Seasonality and Cyclic Patterns in a Time Series Dataset

Evaluate Time Series Models Using Metrics Like RMSE and MAE, and Fine-Tune Parameters

Section 10: Time Series Analysis Using Statistical and Machine Learning Techniques