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Home > Data > Artificial Intelligence > AI & Python Development Megaclass
AI & Python Development Megaclass
AI & Python Development Megaclass

AI & Python Development Megaclass: AI & Python — From Fundamentals to Deep Learning and Beyond

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Profile Icon Vivian Aranha
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€149.99
Video Sep 2025 57hrs 57mins 1st Edition
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Video Sep 2025 57hrs 57mins 1st Edition
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Key benefits

  • Build strong foundations with Python basics, data structures, file handling, and clean coding practices
  • Apply data science techniques using NumPy, Pandas, and visualization libraries for analysis
  • Implement deep learning architectures including CNNs, RNNs, LSTMs, Transformers, and GANs

Description

This course begins with Python programming essentials, including control flow, functions, data structures, and file handling. You’ll then explore core data science tools such as NumPy for numerical computing, Pandas for data manipulation, and Matplotlib/Seaborn for data visualization. Mathematics foundations—linear algebra, calculus, probability, and statistics—are introduced to support AI learning. Each week ends with mini-projects to apply concepts. As you progress, you’ll dive into machine learning techniques, covering regression, classification, ensemble methods, feature engineering, and hyperparameter tuning. Advanced algorithms such as Random Forests, XGBoost, LightGBM, and CatBoost are explored. Deep learning modules guide you through building neural networks, CNNs for image recognition, RNNs and LSTMs for sequence tasks, and Transformers for NLP applications. The final stages emphasize hands-on projects and real-world deployment. You’ll apply skills in computer vision, NLP, reinforcement learning, and time series forecasting. GANs expand your understanding of generative modeling, while AI in production introduces Docker, CI/CD, and cloud scaling. The course concludes with modules on AI ethics, safety, and governance, ensuring responsible and practical AI expertise.

Who is this book for?

This course is designed for aspiring AI developers, data scientists, and software engineers who want an end-to-end pathway in AI with Python. It suits learners seeking to strengthen programming, mathematics, and data science foundations before diving into machine learning and deep learning. Professionals aiming to build expertise in NLP, computer vision, or reinforcement learning will benefit. Beginners with basic programming knowledge can start confidently. Experienced practitioners can deepen skills in advanced AI techniques and deployment practices.

What you will learn

  • Write Pythonic code and manage data using NumPy, Pandas, and Matplotlib
  • Design supervised, unsupervised, and ensemble machine learning algorithms
  • Construct deep learning models with TensorFlow, Keras, and PyTorch
  • Apply CNNs, RNNs, LSTMs, and Transformers to NLP and computer vision tasks
  • Generate data and solutions using GANs and reinforcement learning frameworks
  • Deploy secure, scalable AI systems in production while addressing ethics and governance

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 04, 2025
Length: 57hrs 57mins
Edition : 1st
Language : English
ISBN-13 : 9781806679652
Category :
Data
Languages :
Python
Concepts :
Artificial Intelligence

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Product Details

Publication date : Sep 04, 2025
Length: 57hrs 57mins
Edition : 1st
Language : English
ISBN-13 : 9781806679652
Category :
Data
Languages :
Python
Concepts :
Artificial Intelligence

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Table of Contents

24 Chapters
Week 1: Python Programming Basics for Artificial Intelligence Chevron down icon Chevron up icon
Introduction to Week 1 Python Programming Basics
Day 1: Introduction to Python and Development Setup
Day 2: Control Flow in Python
Day 3: Functions and Modules
Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)
Day 5: Working with Strings
Day 6: File Handling
Day 7: Pythonic Code and Project Work
Week 2: Data Science Essentials for Artificial Intelligence Chevron down icon Chevron up icon
Introduction to Week 2 Data Science Essentials
Day 1: Introduction to NumPy for Numerical Computing
Day 2: Advanced NumPy Operations
Day 3: Introduction to Pandas for Data Manipulation
Day 4: Data Cleaning and Preparation with Pandas
Day 5: Data Aggregation and Grouping in Pandas
Day 6: Data Visualization with Matplotlib and Seaborn
Day 7: Exploratory Data Analysis (EDA) Project
Week 3: Mathematics for Machine Learning and Artificial Intelligence Chevron down icon Chevron up icon
Introduction to Week 3 Mathematics for Machine Learning
Day 1: Linear Algebra Fundamentals
Day 2: Advanced Linear Algebra Concepts
Day 3: Calculus for Machine Learning (Derivatives)
Day 4: Calculus for Machine Learning (Integrals and Optimization)
Day 5: Probability Theory and Distributions
Day 6: Statistics Fundamentals
Day 7: Math-Driven Mini Project – Linear Regression from Scratch
Week 4: Probability and Statistics for Machine Learning and AI Chevron down icon Chevron up icon
Introduction to Week 4 Probability and Statistics for Machine Learning
Day 1: Probability Theory and Random Variables
Day 2: Probability Distributions in Machine Learning
Day 3: Statistical Inference – Estimation and Confidence Intervals
Day 4: Hypothesis Testing and P-Values
Day 5: Types of Hypothesis Tests
Day 6: Correlation and Regression Analysis
Day 7: Statistical Analysis Project – Analyzing Real-World Data
Week 5: Introduction to Machine Learning Chevron down icon Chevron up icon
Introduction to Week 5 Introduction to Machine Learning
Day 1: Machine Learning Basics and Terminology
Day 2: Introduction to Supervised Learning and Regression Models
Day 3: Advanced Regression Models – Polynomial Regression and Regularization
Day 4: Introduction to Classification and Logistic Regression
Day 5: Model Evaluation and Cross-Validation
Day 6: k-Nearest Neighbors (k-NN) Algorithm
Day 7: Supervised Learning Mini Project
Week 6: Feature Engineering and Model Evaluation Chevron down icon Chevron up icon
Introduction to Week 6 Feature Engineering and Model Evaluation
Day 1: Introduction to Feature Engineering
Day 2: Data Scaling and Normalization
Day 3: Encoding Categorical Variables
Day 4: Feature Selection Techniques
Day 5: Creating and Transforming Features
Day 6: Model Evaluation Techniques
Day 7: Cross-Validation and Hyperparameter Tuning
Week 7: Advanced Machine Learning Algorithms Chevron down icon Chevron up icon
Introduction to Week 7 Advanced Machine Learning Algorithms
Day 1: Introduction to Ensemble Learning
Day 2: Bagging and Random Forests
Day 3: Boosting and Gradient Boosting
Day 4: Introduction to XGBoost
Day 5: LightGBM and CatBoost
Day 6: Handling Imbalanced Data
Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset
Week 8: Model Tuning and Optimization Chevron down icon Chevron up icon
Introduction to Week 8 Model Tuning and Optimization
Day 1: Introduction to Hyperparameter Tuning
Day 2: Grid Search and Random Search
Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization
Day 4: Regularization Techniques for Model Optimization
Day 5: Cross-Validation and Model Evaluation Techniques
Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
Day 7: Optimization Project – Building and Tuning a Final Model
Week 9: Neural Networks and Deep Learning Fundamentals Chevron down icon Chevron up icon
Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
Day 1: Introduction to Deep Learning and Neural Networks
Day 2: Forward Propagation and Activation Functions
Day 3: Loss Functions and Backpropagation
Day 4: Gradient Descent and Optimization Techniques
Day 5: Building Neural Networks with TensorFlow and Keras
Day 6: Building Neural Networks with PyTorch
Day 7: Neural Network Project – Image Classification on CIFAR-10
Week 10: Convolutional Neural Networks (CNNs) Chevron down icon Chevron up icon
Introduction to Week 10 Convolutional Neural Networks (CNNs)
Day 1: Introduction to Convolutional Neural Networks
Day 2: Convolutional Layers and Filters
Day 3: Pooling Layers and Dimensionality Reduction
Day 4: Building CNN Architectures with Keras and TensorFlow
Day 5: Building CNN Architectures with PyTorch
Day 6: Regularization and Data Augmentation for CNNs
Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling Chevron down icon Chevron up icon
Introduction to Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
Day 1: Introduction to Sequence Modeling and RNNs
Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)
Day 3: Long Short-Term Memory (LSTM) Networks
Day 4: Gated Recurrent Units (GRUs)
Day 5: Text Preprocessing and Word Embeddings for RNNs
Day 6: Sequence-to-Sequence Models and Applications
Day 7: RNN Project – Text Generation or Sentiment Analysis
Week 12: Transformers and Attention Mechanisms Chevron down icon Chevron up icon
Introduction to Week 12: Transformers and Attention Mechanisms
Day 1: Introduction to Attention Mechanisms
Day 2: Introduction to Transformers Architecture
Day 3: Self-Attention and Multi-Head Attention in Transformers
Day 4: Positional Encoding and Feed-Forward Networks
Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT
Day 6: Advanced Transformers – BERT Variants and GPT-3
Day 7: Transformer Project – Text Summarization or Translation
Week 13: Transfer Learning and Fine-Tuning Chevron down icon Chevron up icon
Introduction to Week 13: Transfer Learning and Fine-Tuning
Day 1: Introduction to Transfer Learning
Day 2: Transfer Learning in Computer Vision
Day 3: Fine-Tuning Techniques in Computer Vision
Day 4: Transfer Learning in NLP
Day 5: Fine-Tuning Techniques in NLP
Day 6: Domain Adaptation and Transfer Learning Challenges
Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task
Week 1: Python Basics Chevron down icon Chevron up icon
Learn Python from Scratch – Quick Tutorial
Day 1: Welcome Message Generator – Print Statements & "Hello World"
Day 2: Personalized Greeting Program – Variables & Data Types
Day 3: Simple Calculator – User Input & String Formatting
Day 4: Number Comparison Tool – If-Else Statements
Day 5: Countdown Timer – Loops (for & while)
Day 6: Basic Math Quiz Game – Functions
Day 7: Shopping List App – Lists
Week 2: Intermediate Python Chevron down icon Chevron up icon
Day 8: Contact Book – Dictionaries
Day 9: Ingredient Checker – Tuples & Sets
Day 10: Note-Taking App – File Handling
Day 11: Safe Calculator – Exception Handling
Day 12: Temperature Converter – Functions with Return Values
Day 13: Student Grade Manager – List Comprehensions
Day 14: Random Password Generator – Modules & Libraries
Week 3: Working with Data Chevron down icon Chevron up icon
Day 15: Recipe Viewer App – Reading Files
Day 16: Daily Journal Logger – Writing Files
Day 17: Student Report Generator – CSV Files
Day 18: Mini To-Do App – JSON Files
Day 19: Weather App Using API – APIs (Basics)
Day 20: Event Countdown Timer – Dates & Time
Day 21: Wikipedia Article Scraper – Web Scraping
Week 4: Object-Oriented Programming Chevron down icon Chevron up icon
Day 22: Bank Account Simulator – Classes & Objects
Day 23: Library Management System – Constructors & Methods
Day 24: Employee Management System – Inheritance
Day 25: Animal Sound Simulator – Polymorphism
Day 26: Secure User Profile App – Encapsulation
Day 27: Inventory Management System – Static & Class Methods
Day 28: Mini ATM Machine – Final OOP Project
Week 5: GUI Programming Chevron down icon Chevron up icon
Day 29: Simple GUI App – Tkinter Basics
Day 30: Click Counter App – Buttons & Events
Day 31: BMI Calculator – Input Fields
Day 32: Drawing Pad App – Canvas Widgets
Day 33: Simple Login System – Message Boxes
Day 34: To-Do List GUI – Advanced Widgets
Day 35: Expense Tracker App – GUI Capstone
Week 6: Web Development with Python Chevron down icon Chevron up icon
Day 36: Hello Flask App – Flask Basics
Day 37: Personal Blog Website – Routes & Templates
Day 38: Contact Form App – Forms & User Input
Day 39: User Registration App – Database Integration
Day 40: Mini Weather API – REST APIs
Day 41: Deploy Flask App – Deployment
Day 42: Portfolio Website – Flask Capstone
Week 7: Data Science Basics Chevron down icon Chevron up icon
Day 43: Matrix Calculator – NumPy
Day 44: Data Cleaner – Pandas
Day 45: Graph Plotter – Matplotlib
Day 46: Sales Report Analyzer – Data Analysis
Day 47: Temperature Plotter – Plotting Trends
Day 48: Stock Price Tracker – Data Scraping
Day 49: COVID-19 Dashboard – Capstone Project
Days 50–60: Intermediate Projects Chevron down icon Chevron up icon
Day 50: Weather Dashboard App
Day 51: Expense Tracker
Day 52: File Organizer Tool
Day 53: Tic-Tac-Toe Game
Day 54: Mini Chatbot
Day 55: Music Playlist Organizer
Day 56: Personal Budget Planner
Day 57: ASCII Art Generator
Day 58: Pomodoro Timer
Day 59: Markdown to HTML Converter
Day 60: Personal Diary App
Days 61–70: Advanced Intermediate Projects Chevron down icon Chevron up icon
Day 61: Social Media Scraper
Day 62: Automated Backup Tool
Day 63: Movie Recommendation System
Day 64: PDF Merger Tool
Day 65: Portfolio Website Backend
Day 66: Flashcards Learning App
Day 67: Stock Market Dashboard
Day 68: Task Scheduler
Day 69: Currency Converter
Day 70: Data Visualizer App
Days 71–80: AI & Machine Learning Projects Chevron down icon Chevron up icon
Day 71: Spam Email Detector
Day 72: Text Sentiment Analyzer
Day 73: Handwriting Digit Recognition
Day 74: Voice Assistant
Day 75: Face Detection App
Day 76: Simple Recommendation System
Day 77: AI Chatbot with NLP
Day 78: Object Detection App
Day 79: Language Translator Tool
Day 80: Fake News Detector
Machine Learning Algorithms and Implementation in Python Chevron down icon Chevron up icon
Introduction to Machine Learning Algorithms and Implementation in Python
1. Supervised Learning Algorithms: Linear Regression Implementation
2. Supervised Learning Algorithms: Ridge and Lasso Regression Implementation
3. Supervised Learning Algorithms: Polynomial Regression Implementation
4. Supervised Learning Algorithms: Logistic Regression Implementation
5. Supervised Learning Algorithms: K-Nearest Neighbors (KNN) Implementation
6. Supervised Learning Algorithms: Support Vector Machines (SVM) Implementation
7. Supervised Learning Algorithms: Decision Trees Implementation
8. Supervised Learning Algorithms: Random Forests Implementation
9. Supervised Learning Algorithms: Gradient Boosting Implementation
10. Supervised Learning Algorithms: Naive Bayes Implementation
11. Unsupervised Learning Algorithms: K-Means Clustering Implementation
12. Unsupervised Learning Algorithms: Hierarchical Clustering Implementation
13. Unsupervised Learning Algorithms: DBSCAN
14. Unsupervised Learning Algorithms: Gaussian Mixture Models (GMM)
15. Unsupervised Learning Algorithms: Principal Component Analysis (PCA)
16. Unsupervised Learning Algorithms: t-Distributed Stochastic Neighbor Embedding
17. Unsupervised Learning Algorithms: Autoencoders Implementation
18. Self-Training Implementation
19. Q-Learning Implementation
20. Deep Q-Networks (DQN) Implementation
21. Policy Gradient Methods Implementation
22. One-Class SVM Implementation
23. Isolation Forest Implementation
24. Convolutional Neural Networks (CNNs) Implementation
25. Recurrent Neural Networks (RNNs) Implementation
26. Long Short-Term Memory (LSTM) Implementation
27. Transformers Implementation

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About the author

Profile icon Vivian Aranha
Vivian Aranha
LinkedIn icon
Vivian Aranha is a seasoned technology professional with nearly two decades of experience in the industry, specializing in Artificial Intelligence. With a Bachelor's in Information Technology (2004) and a Master's in Computer Science (2006), he has built a strong foundation in computing and innovation. For the past eight years, he has been deeply involved in AI, working on machine learning, deep learning, and intelligent systems while also mentoring aspiring professionals. His ability to simplify complex concepts and bridge theory with real-world application makes him a sought-after expert in the AI landscape.
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