Packt

AI & Python Development Megaclass Specialization

Packt

AI & Python Development Megaclass Specialization

Master Python & AI with Real-World Projects. Master Python, machine learning, and deep learning to create AI applications and solve problems.

Access provided by Instituto profesional Duoc UC

Get in-depth knowledge of a subject
Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn to build AI models using Python, from basic functions to advanced neural networks.

  • Master data manipulation and analysis with Python’s core libraries such as NumPy, Pandas, and Matplotlib.

  • Gain hands-on experience in machine learning algorithms, from regression to classification and clustering.

  • Develop deep learning skills using frameworks like TensorFlow and PyTorch for real-world AI projects.

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Taught in English
Recently updated!

February 2026

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Specialization - 3 course series

What you'll learn

  • Master Python fundamentals and apply them to real-world problems.

  • Utilize NumPy and Pandas for data manipulation and analysis in AI projects.

  • Implement object-oriented programming concepts for scalable Python applications.

  • Build AI-driven applications using Python, web frameworks, and data science techniques.

Skills you'll gain

Category: Programming Principles
Category: Probability & Statistics
Category: UI Components
Category: Web Applications
Category: Statistical Methods
Category: Data Manipulation
Category: Flask (Web Framework)
Category: Data Structures
Category: Object Oriented Programming (OOP)
Category: Data Visualization
Category: Data Analysis Software
Category: Seaborn
Category: Pandas (Python Package)

What you'll learn

  • Implement various supervised and unsupervised machine learning algorithms in Python.

  • Apply ensemble learning techniques like Random Forests, XGBoost, and LightGBM to improve model performance.

  • Master model optimization techniques such as hyperparameter tuning, cross-validation, and regularization.

  • Evaluate machine learning models using advanced metrics and real-world validation techniques.

Skills you'll gain

Category: Model Evaluation
Category: Supervised Learning
Category: Feature Engineering
Category: Machine Learning Algorithms
Category: Performance Tuning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Classification Algorithms
Category: Dimensionality Reduction
Category: Data Preprocessing
Category: Unsupervised Learning
Category: Applied Machine Learning
Category: Predictive Modeling

What you'll learn

  • Build and optimize deep learning models using neural networks, CNNs, and RNNs.

  • Apply transformers and attention mechanisms to perform advanced NLP tasks.

  • Utilize transfer learning to fine-tune pre-trained models for specific tasks.

  • Develop AI-powered applications, including image classifiers, sentiment analyzers, and chatbots.

Skills you'll gain

Category: Data Preprocessing
Category: Embeddings
Category: Natural Language Processing
Category: Convolutional Neural Networks
Category: Model Evaluation
Category: Keras (Neural Network Library)
Category: Large Language Modeling
Category: PyTorch (Machine Learning Library)
Category: Artificial Intelligence
Category: Artificial Neural Networks
Category: Deep Learning
Category: Recurrent Neural Networks (RNNs)
Category: Transfer Learning
Category: Tensorflow
Category: Computer Vision
Category: Machine Learning Methods
Category: Image Analysis

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Instructor

Packt - Course Instructors
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1,525 Courses 399,955 learners

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