Machine Learning with Python

• Introduction to Python
  • History of Python: Origins, development by Guido van Rossum, and key milestones in the
    language's evolution (Python 2 vs Python 3).
  • Why Python?: Why it became one of the most popular languages, its syntax simplicity, readability, and strong community support.
  • Python's Role in Modern Software Development: From web development to AI, data science, automation, and beyond.
• Key Features of Python
  • High-level Language: Automatic memory management, easy-to-understand syntax.
  • Dynamically Typed: No need to declare variable types explicitly.
  • Interpreted Language: Python code is executed line-by-line by the interpreter.
  • Readable and Simple Syntax: Focus on readability and ease of learning.
• Python 2 vs Python 3
  • Syntax Differences: Print statement (Python 2: print "Hello", Python 3: print("Hello")).
  • Library Changes: Differences in standard libraries and functions (e.g., range() function).
  • Key Functionality Shifts: Division behavior (/ operator behavior) and string handling.
• Applications of Python
  • Web Development: Frameworks like Django, Flask.
  • Data Science & Machine Learning: Libraries such as Pandas, NumPy, Scikit-learn.
  • Automation and Scripting: Using Python for tasks like file manipulation, web scraping.
  • Game Development: Using libraries like Pygame for simple 2D games.
• Environment Setup
  • Installing Python: Installing the latest version on different operating systems (Windows, macOS, Linux).
  • Verifying Installation: Checking Python installation via the command line (python --version).
  • Troubleshooting: Common installation issues and their fixes.
  • Setting Up pip: Installing and updating packages using pip, Python's package manager.
• Virtual Environments
  • Using Virtual Environments: Creating and managing virtual environments with venv, virtualenv, conda.
  • Why Virtual Environments?: Isolating project dependencies to avoid conflicts.
• Development Tools and IDEs
  • Google Colab & Jupyter Notebooks: Advantages for data analysis, visualization, and rapid prototyping.
  • PyCharm, VS Code, Spyder, Atom: Pros and cons of each IDE for Python development, workflow tips.
• Debugging and Code Execution
  • Using Built-in Debugging Tools: Using breakpoints, stepping through code, and inspecting variables.
  • Running Scripts: From terminal, IDE, or notebook interface.
• Markdown for Documentation
  • Writing README.md: Basics of markdown syntax for documenting projects: headers, lists, links, images, code blocks.

• Python Syntax and Structure
  • Writing Your First Python Program: "Hello, World!" and running your first script.
  • Code Blocks and Indentation: Importance of indentation in Python for defining code blocks (no braces {}).
  • Comments and Documentation: Using single-line comments (#), multi-line comments (""" """), and docstrings for documenting functions and classes.
     
• Variables and Data Types
  • Variable Naming: Rules and conventions for naming variables in Python (e.g., no spaces, use underscores).
  • Built-in Data Types:
    • int: Integer values.
    • float: Floating point numbers.
    • str: Strings.
    • bool: Boolean (True or False).
    • complex: Complex numbers.
    • None: Special null type.
• Type Conversion: How to convert between types using functions like int(), str(), float().
• Type Checking: Using type() and isinstance() to verify variable types.
• Operators in Python
  • Arithmetic Operators: +, -, *, /, // (integer division), % (modulus), ** (exponentiation).
  • Comparison Operators: ==, !=, >, <, >=, <=.
  • Logical Operators: and, or, not.
  • Bitwise Operators: &, |, ^, ~, <<, >>.
  • Operator Precedence: How operators are evaluated in Python expressions.

• Defining Functions
  • Basic Function Definition: Using def keyword to create a function.
  • Function Arguments: Positional, default, keyword, and arbitrary arguments (*args,
  • **kwargs).
  • Return Values: Returning values from functions using the return statement.
• Advanced Function Concepts
  • Lambda Functions: Creating anonymous functions using lambda (e.g., lambda x: x+1).
  • Recursion: Understanding recursive functions, base case, recursion tree, and stack diagrams.
  • Tail Recursion: Techniques to optimize recursion by avoiding excessive memory usage.
• Error Handling
  • Try/Except Blocks: Handling exceptions using try, except, finally.
  • Custom Exceptions: Creating your own exception classes for more readable error handling.

• Strings and Slicing
  • String Indexing and Slicing:
    • Indexing: Access individual characters, e.g., my_string[0].
    • Slicing: Extract substrings, e.g., my_string[1:5] (from index 1 to 4), my_string[:3] (first 3 characters), my_string[::2] (every second character).
  • String Methods: .lower(), .upper(), .split(), .strip(), .join(), etc.
• Lists and Slicing
  • List Basics: Lists as mutable sequences, defining and modifying lists.
  • List Slicing:
    • Extract sublists, e.g., my_list[1:4], my_list[::2] (every second element).
    • List comprehension for creating new lists based on conditions.
  • List Methods: .append(), .insert(), .remove(), .pop(), etc.
• Tuples and Slicing
  • Tuples vs Lists: Key differences between tuples (immutable) and lists (mutable).
  • Tuple Slicing: Use the same slicing techniques as lists: my_tuple[1:3].
• Dictionaries and Sets
  • Dictionary Basics: Key-value pairs, retrieving values, adding/removing entries.
  • Set Operations: Union, intersection, difference, symmetric difference.

• (OOP) Classes and Objects
  • Defining Classes: The class keyword, attributes, methods, and the   init  () constructor.
  • Inheritance: Single, multiple, and multilevel inheritance.
  • Method Overriding: Overriding methods in derived classes.
• Advanced OOP
  • Polymorphism: Achieving polymorphism through method overriding and interfaces.
  • Encapsulation: Hiding the internal workings of objects and providing public interfaces.
  • Operator Overloading: Implementing custom behavior for operators like +, == using special methods like   add  ,   eq  .
  • Access Specifiers: Managing public, private, and protected attributes.

• File Handling
  • Opening Files: Using open(), understanding file modes (r, w, a).
  • Reading and Writing: Reading with .read(), .readline(), and .readlines(). Writing with .write().
  • Working with CSV Files: Using Python's csv module for reading and writing CSV data.
• Modules and Packages
  • Importing Modules: Importing standard and custom modules with import.
  • Using pip: Installing and managing external packages from the Python Package Index (PyPI).
  • Popular Standard Library Modules: os, sys, math, random, datetime.

• Iterators and Generators
  • Iterators: Creating custom iterators with   iter  () and   next  ().
  • Generators: Using yield for efficient data generation.
• Decorators
  • Function Decorators: Enhancing functions by adding extra behavior (e.g., logging, timing).
  • Chaining Decorators: Applying multiple decorators in a stack.
• Context Managers
  • Using with Statement: Creating and using custom context managers with the   enter  () and  exit () methods.
• Regular Expressions
  • Pattern Matching: Using Python's re module to search, match, and replace patterns in text.

This module covers core Python libraries essential for data analysis, visualization, machine learning, deep learning, and big data processing.

• NumPy (Numerical Computing)
  • Efficient ndarray for vectorized operations, linear algebra, and random sampling.
  • Backbone for scientific computing and ML libraries.
• Pandas (Data Analysis & Manipulation)
  • DataFrame & Series for structured data handling.
• Supports data cleaning, transformation, and aggregation.
  • Reads/writes CSV, Excel, SQL, JSON.
• GeoPandas (Geospatial Data Processing)
  • Extends Pandas for geospatial analysis.
• Supports shapefiles, geospatial joins, and coordinate transformations.
  • Data Visualization Matplotlib & Seaborn
  • Matplotlib: Low-level plotting (line, bar, scatter, histograms).
  • Seaborn: Statistical plots (heatmaps, pair plots, regression plots).
• Plotly & Graphviz
  • Plotly: Interactive charts (3D plots, choropleth maps, dashboards).
  • Graphviz: Visualizes graphs, decision trees, and networks.
• Scikit-learn (Machine Learning)
  • Supervised & unsupervised learning (classification, regression, clustering).
• Feature selection, model evaluation, and hyperparameter tuning.
  • TensorFlow & PyTorch (Deep Learning)
  • TensorFlow: Google-backed, efficient deep learning with GPU/TPU support.
  • PyTorch: Facebook-backed, research-friendly with dynamic computation graphs.
• PySpark (Big Data Processing)
  • Distributed computing for large-scale data analysis.
  • Integrates with Hadoop, Spark SQL, and MLlib.
• Statsmodels (Statistical Analysis)
  • Regression, hypothesis testing, time-series modeling.
• OpenCV (Computer Vision)
  • Image processing, object detection, face recognition.
• NLTK & SpaCy (Natural Language Processing)
  • NLTK: Traditional NLP (tokenization, stemming, sentiment analysis).
  • SpaCy: Optimized for large-scale NLP tasks.
• Requests & BeautifulSoup (Web Scraping)
  • Requests: API interactions via HTTP requests.
  • BeautifulSoup: Parses and scrapes HTML/XML content.
• Dask (Parallel Computing)
  • Scales NumPy & Pandas for large dataset processing.
  • These libraries power modern AI, data science, and autom

• SQL Basics
  • Connecting to Databases: Using sqlite3 for SQLite database connection.
  • Basic SQL Queries: SELECT, INSERT, DELETE, and UPDATE.
• Advanced SQL Operations
  • Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
  • Aggregation and Grouping: Using GROUP BY, COUNT(), SUM(), AVG().

• Git Basics: Initializing repositories, committing changes, and checking status.
• Branching and Merging: Using branches for collaborative development.
• GitHub: Pushing and pulling from repositories, collaborating with other developers.
• Development: Flask and Streamlit
   

• Project Capstone 1
  • Tic-Tac-Toe Game: Build a console-based game with Python's basic constructs.
  • Weather App: Fetch and display live weather data using APIs.
  • Expense Tracker: A simple app to track expenses, categorize them, and visualize spending trends.
  • To-Do List App: A command-line application to manage tasks, with options for task completion and sorting.

• Supervised Learning
  • Regression Algorithms
    • Linear Regression – Fits a straight line to predict outcomes.
    • Polynomial Regression – Uses polynomial functions for curved relationships.
    • Ridge Regression – Linear regression with L2 regularization to prevent overfitting.
    • Lasso Regression – Linear regression with L1 regularization for feature selection.
    • Elastic Net Regression – Combination of Ridge and Lasso for better regularization.
    • Support Vector Regression (SVR) – Uses hyperplanes and kernels for regression.
    • Decision Tree Regression – Splits data into nodes for rule-based prediction.
    • Random Forest Regression – Uses multiple decision trees for better accuracy.
    • Gradient Boosting Regression – Boosted decision trees for improved predictions.
    • XGBoost – Optimized gradient boosting for fast performance.
    • LightGBM – Faster, tree-based boosting for large datasets.
    • CatBoost – Gradient boosting specialized for categorical data.
    • AdaBoost Regression – Adaptive boosting for reducing model errors.
    • Bayesian Linear Regression – Uses probability distributions for regression.
    • Bayesian Ridge Regression – Bayesian approach with Ridge regularization.
    • Gaussian Process Regression (GPR) – Uses probabilistic models for predictions.
    • Kernel Ridge Regression (KRR) – Ridge regression with kernel tricks.
    • Poisson Regression – Used for count-based data predictions.
    • Quantile Regression – Predicts quantiles instead of mean values.
    • Tweedie Regression – Handles mixed discrete and continuous outcomes.
    • Principal Component Regression (PCR) – Combines PCA with regression.
    • Partial Least Squares Regression (PLSR) – Reduces multicollinearity in data.
    • Theil-Sen Estimator Regression – Robust against outliers using median slopes.
    • Huber Regression – Hybrid of least squares and absolute loss for robustness.
    • RANSAC Regression – Robust regression that ignores outliers.
• Classification Algorithms
  • Logistic Regression, K-Nearest Neighbors (KNN).
  • Decision Trees, Random Forests, Naive Bayes.
  • Support Vector Machines (SVM), K-Means.
  • Ensemble Methods: Bagging, Boosting, AdaBoost, Random Forest.
• Unsupervised Learning
  • Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN.
  • Dimensionality Reduction: PCA, t-SNE, Autoencoders.
  • Association Rule Mining: Apriori Algorithm, Eclat Algorithm.
• Model Evaluation
  • Cross-validation, Train-Test Split, Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV).
  • Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC, Confusion Matrix.
• Model optimization and minimizing errors

Model Optimization & Hyperparameter Tuning

• Grid Search
• Random Search
• Bayesian Optimization
• Hyperband
• Evolutionary Algorithms

Cross-Validation Techniques

• K-Fold Cross-Validation
• Stratified Cross-Validation
• Leave-One-Out Cross-Validation (LOOCV)
• Time Series Cross-Validation

Feature Engineering

Feature Scaling

• Standardization (Z-score Scaling)
• Min-Max Scaling (Normalization)
• Robust Scaling

Feature Selection

• Recursive Feature Elimination (RFE)
• Mutual Information
• Chi-Square Test
• Variance Threshold
• Embedded Methods (Lasso, Decision Trees)

Feature Extraction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Independent Component Analysis (ICA)
• Autoencoders
• t-SNE
• UMAP

Regularization Techniques

• L1 Regularization (Lasso)
• L2 Regularization (Ridge)
• Elastic Net Regularization
• Dropout (for Neural Networks)
• Batch Normalization

Ensemble Learning

• Bagging (Random Forest)
• Boosting (AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost)
• Stacking (Stacked Generalization)
• Voting Classifier

Overfitting & Underfitting Solutions

• Early Stopping
• Dropout (for Neural Networks)
• Regularization
• Data Augmentation

Learning Rate Optimization

• Learning Rate Scheduling
• Cyclical Learning Rates
• Adaptive Learning Rate Methods (Adam, RMSprop, Adagrad)

Transfer Learning

• Fine-tuning Pre-trained Models
• Feature Extraction from Pre-trained Models

Optimization Algorithms

• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent
• Adam
• RMSprop
• Adagrad
• Nesterov Accelerated Gradient (NAG)

Explainable AI (XAI) for Model Improvement

• SHAP (SHapley Additive Explanations)
• LIME (Local Interpretable Model-Agnostic Explanations)
• Feature Importance (Decision Trees, XGBoost)

• Ensemble Learning
  • Random Forest, AdaBoost, Gradient Boosting (XGBoost, CatBoost).
  • Stacking, Bagging, Boosting Techniques.
• Dimensionality Reduction & Feature Selection
  • PCA, ICA (Independent Component Analysis).
  • Feature Selection: Recursive Feature Elimination (RFE), Feature Importance.
• Model Deployment
  • Building ML Pipelines (Sklearn Pipelines).
  • Model Deployment: Flask, FastAPI, Docker, Cloud Deployment.
  • API Development for ML Models.
• Time Series Forecasting
  • ARIMA, SARIMA, Exponential Smoothing.
  • Prophet, LSTM (Long Short-Term Memory) for Time-Series Data.

• Introduction to Neural Networks
  • Perceptrons, Activation Functions (Sigmoid, Tanh, ReLU).
  • Backpropagation, Gradient Descent, Vanishing/Exploding Gradient Problems.
• Deep Learning Frameworks
  • TensorFlow, Keras, PyTorch, MXNet.
• Feedforward Neural Networks
  • Multilayer Perceptrons (MLP), Architecture Design.
• CNN (Convolutional Neural Networks)
  • Architecture: Convolution, Pooling, Fully Connected Layers.
  • Applications: Image Classification, Object Detection, Face Recognition.
• RNN (Recurrent Neural Networks)
  • Applications in Sequential Data: Time-Series, Text, Speech.
  • LSTM, GRU for Sequence Prediction.

• Generative Models
  • Generative Adversarial Networks (GANs).
  • Variational Autoencoders (VAEs).
  • Deep Dream, CycleGAN.
• Transformers and Attention Mechanism
  • Self-Attention, Multi-head Attention.
  • Transformer Architecture: BERT, GPT, T5.
  • Applications in NLP, Text Classification, Language Modeling.
• Reinforcement Learning
  • Markov Decision Process (MDP), Q-Learning, Deep Q-Learning.
  • Policy Gradient Methods, Actor-Critic Models.
  • Applications in Robotics, Game AI.

• Text Preprocessing
  • Tokenization, Lemmatization, Stemming.
  • Removing Stopwords, Handling Punctuation.
  • Vectorization: Bag of Words, TF-IDF, Word2Vec, GloVe.
• Text Classification and Sentiment Analysis
  • Sentiment Analysis with Naive Bayes, SVM, Deep Learning.
  • Text Classification: Multi-class, Multi-label Classification.
• Sequence Models
  • RNN, LSTM, GRU for Text Classification, Machine Translation.
  • Attention Mechanism for Machine Translation.
• Pretrained Language Models
  • BERT, GPT, T5 for NLP Tasks: Text Summarization, Question Answering.

• Text Generation
  • Generative Models: LSTM, RNN, GPT-3 for Text Generation.
  • Fine-Tuning Pretrained Models for Custom Text Generation.
• Question Answering Systems
  • Extractive QA, Abstractive QA using BERT, T5.
  • Fine-Tuning Pretrained Transformers for QA.
• Named Entity Recognition (NER)
  • Entity Extraction using SpaCy, BERT.
  • Text Annotation, NER Pipelines.
• Multilingual NLP
  • Language Detection, Translation, Language Modeling.

• Basic Concepts in Image Processing
  • Image Representation, Color Channels (RGB, Grayscale).
  • Image Transformations: Resizing, Cropping, Rotation.
  • Filters, Convolution, Edge Detection.
• Convolutional Neural Networks (CNN)
  • CNN Architecture: Convolution Layer, Pooling, Fully Connected Layers.
  • Applications: Image Classification, Object Detection.
• Object Detection
  • YOLO (You Only Look Once), SSD (Single Shot Multibox Detector).
  • Region-based CNN (R-CNN), Fast R-CNN, Faster R-CNN.

• Image Segmentation
  • Semantic and Instance Segmentation.
  • U-Net for Biomedical Image Segmentation.
  • Mask R-CNN for Instance Segmentation.
• Face Detection and Recognition
  • OpenCV, Dlib for Facial Recognition.
  • Landmark Detection and Face Matching.
• Image Generation with GANs and Style GANs
  • Generative Adversarial Networks (GANs) for Image Synthesis.
  • Applications: Deep Fake Generation, Image-to-Image Translation (CycleGAN).
     

• Capstone Projects

Best Regression Projects:

1. House Price Prediction
2. Sales Forecasting
3. Sleep disorder prediction

Classification Projects:

1. Spam Email Detection