Skip to main content
Architecting Intelligent Systems; Mastering Machine Learning for Real-World Applications

Architecting Intelligent Systems; Mastering Machine Learning for Real-World Applications

$199.00
When you get access:
Course access is prepared after purchase and delivered via email
How you learn:
Self-paced • Lifetime updates
Your guarantee:
30-day money-back guarantee — no questions asked
Who trusts this:
Trusted by professionals in 160+ countries
Toolkit Included:
Includes a practical, ready-to-use toolkit with implementation templates, worksheets, checklists, and decision-support materials so you can apply what you learn immediately - no additional setup required.
Adding to cart… The item has been added

Architecting Intelligent Systems: Mastering Machine Learning for Real-World Applications



Course Overview

This comprehensive course is designed to equip participants with the skills and knowledge needed to architect intelligent systems using machine learning for real-world applications. Through a combination of interactive lessons, hands-on projects, and expert instruction, participants will gain a deep understanding of the concepts, tools, and techniques required to succeed in this rapidly evolving field.



Course Objectives

  • Understand the fundamentals of machine learning and its applications in real-world systems
  • Design and implement intelligent systems using machine learning algorithms and techniques
  • Develop skills in data preprocessing, feature engineering, and model evaluation
  • Apply machine learning to solve complex problems in various industries, including healthcare, finance, and customer service
  • Stay up-to-date with the latest advancements and trends in machine learning and AI


Course Outline

Module 1: Introduction to Machine Learning

  • Definition and types of machine learning
  • History and evolution of machine learning
  • Applications of machine learning in real-world systems
  • Key concepts: supervised and unsupervised learning, regression, classification, clustering

Module 2: Data Preprocessing and Feature Engineering

  • Data types and structures
  • Data preprocessing techniques: handling missing values, data normalization, feature scaling
  • Feature engineering: feature extraction, feature selection, dimensionality reduction
  • Introduction to data visualization tools and techniques

Module 3: Supervised Learning Algorithms

  • Linear regression: simple and multiple linear regression, cost functions, gradient descent
  • Logistic regression: binary and multiclass classification, sigmoid function, cross-entropy loss
  • Decision trees: classification and regression trees, tree pruning, ensemble methods
  • Random forests: bagging, boosting, hyperparameter tuning

Module 4: Unsupervised Learning Algorithms

  • K-means clustering: centroid-based clustering, k-means++, hierarchical clustering
  • Hierarchical clustering: agglomerative and divisive clustering, dendrograms
  • Principal component analysis (PCA): dimensionality reduction, eigenvalues, eigenvectors
  • t-SNE: non-linear dimensionality reduction, perplexity, early exaggeration

Module 5: Deep Learning Fundamentals

  • Introduction to deep learning: neural networks, convolutional neural networks, recurrent neural networks
  • Activation functions: sigmoid, ReLU, tanh, softmax
  • Backpropagation: gradient descent, stochastic gradient descent, mini-batch gradient descent
  • Introduction to deep learning frameworks: TensorFlow, PyTorch, Keras

Module 6: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs: convolutional layers, pooling layers, fully connected layers
  • Convolutional layers: filters, kernel size, stride, padding
  • Pooling layers: max pooling, average pooling, spatial pyramid pooling
  • Case studies: image classification, object detection, segmentation

Module 7: Recurrent Neural Networks (RNNs)

  • Introduction to RNNs: simple RNNs, LSTM, GRU
  • Simple RNNs: recurrent connections, hidden states, output layers
  • LSTM: long short-term memory, gates, cell state
  • GRU: gated recurrent units, reset gate, update gate

Module 8: Transfer Learning and Fine-Tuning

  • Introduction to transfer learning: pre-trained models, fine-tuning
  • Pre-trained models: VGG16, ResNet50, InceptionV3
  • Fine-tuning: weight freezing, weight decay, learning rate scheduling
  • Case studies: image classification, object detection, segmentation

Module 9: Model Evaluation and Hyperparameter Tuning

  • Introduction to model evaluation: metrics, cross-validation
  • Metrics: accuracy, precision, recall, F1 score, mean squared error
  • Cross-validation: k-fold cross-validation, stratified cross-validation
  • Hyperparameter tuning: grid search, random search, Bayesian optimization

Module 10: Real-World Applications of Machine Learning

  • Introduction to real-world applications: healthcare, finance, customer service
  • Case studies: medical diagnosis, stock market prediction, chatbots
  • Challenges and limitations: data quality, interpretability, ethics
  • Future directions: emerging trends, new applications, research areas


Certificate of Completion

Upon completing this course, participants will receive a Certificate of Completion issued by The Art of Service. This certificate will demonstrate their expertise and knowledge in architecting intelligent systems using machine learning for real-world applications.



Course Features

  • Interactive and engaging lessons
  • Comprehensive and up-to-date content
  • Personalized learning experience
  • Expert instructors with industry experience
  • Hands-on projects and case studies
  • Bite-sized lessons and flexible learning schedule
  • Lifetime access to course materials
  • Gamification and progress tracking
  • Community-driven discussion forums
  • Actionable insights and takeaways
,
!