MACHINE-LEARN.AJ1
Python Machine Learning By Example
Master Python Machine Learning by building real-world examples. Learn practical ML algorithms, deployment considerations, and best practices for robust solutions.
- Practice in 32 Laboratorios prácticos — nothing to install
- 16 Lecciones interactivas y 118 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
32 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
16Lecciones interactivas
118Topics
32Laboratorio en vivo
15Vídeos
108Tarjetas didácticas
108Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This Python machine learning course cuts through the theory to deliver hands-on expertise. You'll tackle real-world problems, from building movie recommenders with Naïve Bayes to predicting stock prices using neural networks.
We'll dive into critical topics like data preprocessing, feature engineering, and evaluating model performance, exposing common pitfalls and limitations. Learn to implement decision trees, logistic regression, SVMs, and advanced deep learning architectures like CNNs and RNNs. Understand the trade-offs between model complexity and interpretability. This isn't about perfection; it's about building functional, robust machine learning solutions and understanding their practical constraints.
- Implement and evaluate core machine learning algorithms like Naïve Bayes, Decision Trees, Logistic Regression, and SVMs for classification and regression tasks, understanding their underlying mechanics and practical limitations.
- Develop and deploy deep learning models, including Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models for complex tasks like image classification, sentiment analysis, and text generation.
- Apply essential data preprocessing, feature engineering, and model selection techniques to prepare datasets for machine learning, recognizing the impact of data quality on model performance and generalization.
- Design and build end-to-end machine learning solutions, from data acquisition and model training to evaluation and deployment, adhering to best practices for maintainability and scalability, while acknowledging real-world deployment challenges.
Course Highlights
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16 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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32 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
16 Lecciones interactivas · 118 topics01 Introduction 2 topics +
- Who this course is for
- What this course covers
02 Getting Started with Machine Learning and Python 9 topics +
- An introduction to machine learning
- Knowing the prerequisites
- Getting started with three types of machine learning
- Digging into the core of machine learning
- Data preprocessing and feature engineering
- Combining models
- Installing software and setting up
- Summary
- Exercises
03 Building a Movie Recommendation Engine with Naïve Bayes 8 topics · 2 Laboratorio en vivo +
- Getting started with classification
- Exploring Naïve Bayes
- Implementing Naïve Bayes
- Building a movie recommender with Naïve Bayes
- Evaluating classification performance
- Tuning models with cross-validation
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
04 Predicting Online Ad Click-Through with Tree-Based Algorithms 10 topics · 2 Laboratorio en vivo +
- A brief overview of ad click-through prediction
- Getting started with two types of data – numerical and categorical
- Exploring a decision tree from the root to the leaves
- Implementing a decision tree from scratch
- Implementing a decision tree with scikit-learn
- Predicting ad click-through with a decision tree
- Ensembling decision trees – random forests
- Ensembling decision trees – gradient-boosted trees
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Predicting Online Ad Click-Through with Logistic Regression 8 topics · 5 Laboratorio en vivo +
- Converting categorical features to numerical – one-hot encoding and ordinal encoding
- Classifying data with logistic regression
- Training a logistic regression model
- Training on large datasets with online learning
- Handling multiclass classification
- Implementing logistic regression using TensorFlow
- Summary
- Exercises
5 Laboratorio en vivo in this lesson — see the labs panel →
06 Predicting Stock Prices with Regression Algorithms 10 topics · 6 Laboratorio en vivo +
- What is regression?
- Mining stock price data
- Getting started with feature engineering
- Estimating with linear regression
- Estimating with decision tree regression
- Implementing a regression forest
- Evaluating regression performance
- Predicting stock prices with the three regression algorithms
- Summary
- Exercises
6 Laboratorio en vivo in this lesson — see the labs panel →
07 Predicting Stock Prices with Artificial Neural Networks 7 topics · 2 Laboratorio en vivo +
- Demystifying neural networks
- Building neural networks
- Picking the right activation functions
- Preventing overfitting in neural networks
- Predicting stock prices with neural networks
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
08 Mining the 20 Newsgroups Dataset with Text Analysis Techniques 8 topics · 1 Laboratorio en vivo +
- How computers understand language – NLP
- Touring popular NLP libraries and picking up NLP basics
- Getting the newsgroups data
- Exploring the newsgroups data
- Thinking about features for text data
- Visualizing the newsgroups data with t-SNE
- Summary
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling 6 topics · 2 Laboratorio en vivo +
- Learning without guidance – unsupervised learning
- Getting started with k-means clustering
- Clustering the newsgroups dataset
- Discovering underlying topics in newsgroups
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
10 Recognizing Faces with Support Vector Machine 5 topics · 2 Laboratorio en vivo +
- Finding the separating boundary with SVM
- Classifying face images with SVM
- Estimating with support vector regression
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Machine Learning Best Practices 7 topics · 1 Laboratorio en vivo +
- Machine learning solution workflow
- Best practices in the data preparation stage
- Best practices in the training set generation stage
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
12 Categorizing Images of Clothing with Convolutional Neural Networks 9 topics · 2 Laboratorio en vivo +
- Getting started with CNN building blocks
- Architecting a CNN for classification
- Exploring the clothing image dataset
- Classifying clothing images with CNNs
- Boosting the CNN classifier with data augmentation
- Improving the clothing image classifier with data augmentation
- Advancing the CNN classifier with transfer learning
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
13 Making Predictions with Sequences Using Recurrent Neural Networks 9 topics · 2 Laboratorio en vivo +
- Introducing sequential learning
- Learning the RNN architecture by example
- Training an RNN model
- Overcoming long-term dependencies with LSTM
- Analyzing movie review sentiment with RNNs
- Revisiting stock price forecasting with LSTM
- Writing your own War and Peace with RNNs
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
14 Advancing Language Understanding and Generation with the Transformer Models 6 topics · 1 Laboratorio en vivo +
- Understanding self-attention
- Exploring the Transformer’s architecture
- Improving sentiment analysis with BERT and Transformers
- Generating text using GPT
- Summary
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
15 Building an Image Search Engine Using CLIP: a Multimodal Approach 6 topics · 2 Laboratorio en vivo +
- Introducing the CLIP model
- Getting started with the dataset
- Finding images with words
- Summary
- Exercises
- References
2 Laboratorio en vivo in this lesson — see the labs panel →
16 Making Decisions in Complex Environments with Reinforcement Learning 8 topics · 2 Laboratorio en vivo +
- Setting up the working environment
- Introducing OpenAI Gym and Gymnasium
- Introducing reinforcement learning with examples
- Solving the FrozenLake environment with dynamic programming
- Performing Monte Carlo learning
- Solving the Blackjack problem with the Q-learning algorithm
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
32 Laboratorio en vivos- Implementing Naïve Bayes
- Implementing Naïve Bayes for Movie Review Sentiment Classification
- Implementing a Decision Tree with scikit-learn
- Predicting Sales with a Decision Tree Regressor
- Training a Logistic Regression Model Using Gradient Descent
- Predicting Ad Click-Through with Logistic Regression Using Gradient Descent
- Training a Logistic Regression Model Using SGD
- Performing Feature Selection Using L1 Regularization and Random Forest
- Implementing Logistic Regression Using TensorFlow
- Acquiring Data and Generating Features
- Implementing Linear Regression with scikit-learn
- Implementing Linear Regression with TensorFlow
- Implementing Decision Tree Regression
- Implementing a Regression Forest
- Predicting stock prices with the three regression algorithms
- Building a Neural Network
- Predicting Stock Prices with Neural Networks
- Visualizing the Newsgroups Data with t-SNE
- Implementing k-means from Scratch
- Clustering Newsgroups Data Using k-means with scikit-learn
- Implementing SVM with Multiple Classes
- Implementing SVR
- Selecting and Evaluating Features for Model Training
- Training a CNN with Data Augmentation
- Classifying Images Using Convolutional Neural Networks
- Building an RNN
- Building a simple LSTM network
- Performing Sentiment Analysis with DistilBERT and Transformers
- Architecting the CLIP model
- Performing Zero-Shot Classification with Transformers
- Simulating the FrozenLake Environment
- Solving the Blackjack Problem with the Q-learning Algorithm
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What are the prerequisites for this Python Machine Learning course?+
You should have a solid grasp of Python programming fundamentals, including data structures, functions, and basic object-oriented concepts. Familiarity with linear algebra and calculus is beneficial but not strictly required, as the course focuses on practical application.
How does this course balance theory with practical application?+
This course is heavily example-driven. While we introduce the necessary theoretical concepts for each algorithm, the primary focus is on hands-on implementation using Python and libraries like scikit-learn and TensorFlow/Keras. You'll build real-world projects from scratch.
Will I learn about deploying machine learning models into production environments?+
The course covers machine learning best practices, including stages like data preparation, model training, evaluation, and selection, which are crucial for deployment. While it doesn't delve into specific production infrastructure (e.g., AWS, Azure), it equips you with the knowledge to build robust, deployable models and understand the workflow.
What kind of machine learning problems will I be able to solve after completing this course?+
You'll be equipped to tackle a wide range of problems, including building recommendation engines, predicting ad click-through rates, forecasting stock prices, categorizing images, analyzing text sentiment, and even developing basic image search engines using advanced models like CLIP.
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