What does the Machine Learning Mastery course cover?
Machine Learning Mastery is covered here in 10 modules: Introduction to Machine Learning: Introduction to deep learning and neural networks, Neural Networks Fundamentals: Activation functions: sigmoid, ReLU, and softmax, Deep Learning Fundamentals and 7 more. The outline lists 48 specific topics, opening with what is machine learning? and closing with future directions: continuing education and staying up-to-date with industry developments.
How do you approach Machine Learning Mastery step by step?
The work is sequenced in 10 stages. It starts with Introduction to Machine Learning: Introduction to deep learning and neural networks, moves through Neural Networks Fundamentals: Activation functions: sigmoid, ReLU, and softmax and Deep Learning Fundamentals, and ends at Final Project and Certification. Each stage carries its own topic list, so the sequence is followed rather than summarised.
What is in Module 1 of the Machine Learning Mastery course?
Module 1 is Introduction to Machine Learning: Introduction to deep learning and neural networks. It works through what is machine learning?, types of machine learning: supervised, unsupervised, and reinforcement learning, machine learning workflow: data preparation, model selection, training, and deployment and 2 more. It sets the vocabulary the remaining 9 modules build on.
How is the Machine Learning Mastery course delivered?
The Machine Learning Mastery course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Machine Learning Mastery course cost?
The Machine Learning Mastery course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Neural Network Design for Real-World Engineering Systems, Deep Learning for Real-World Business Applications, Deep Learning for Real-World Business Solutions, Deep Learning Models for Real-World Applications.
More answers: what you get with every course, refund policy, all help answers.
Machine Learning Mastery: Neural Networks, Deep Learning, and Real-World Applications
Course Overview
This comprehensive course is designed to help participants master the concepts of machine learning, neural networks, and deep learning, and apply them to real-world applications. Upon completion of the course, participants will receive a Certificate of Completion.Course Features
- Interactive and engaging learning experience
- Comprehensive and up-to-date curriculum
- Personalized learning experience
- Practical and real-world applications
- High-quality content and expert instructors
- Certification upon completion
- Flexible learning schedule
- User-friendly and mobile-accessible platform
- Community-driven and interactive discussion forum
- Actionable insights and hands-on projects
- Bite-sized lessons and lifetime access
- Gamification and progress tracking
Course Outline
Module 1. Introduction to Machine Learning: Introduction to deep learning and neural networks
- What is machine learning?
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Machine learning workflow: data preparation, model selection, training, and deployment
- Common machine learning algorithms: linear regression, decision trees, and clustering
- Introduction to deep learning and neural networks
Module 2. Neural Networks Fundamentals: Activation functions: sigmoid, ReLU, and softmax
- Introduction to neural networks: perceptron, multilayer perceptron, and backpropagation
- Activation functions: sigmoid, ReLU, and softmax
- Neural network architectures: feedforward, convolutional, and recurrent
- Neural network training: stochastic gradient descent, batch normalization, and regularization
- Introduction to deep learning frameworks: TensorFlow, PyTorch, and Keras
Module 3: Deep Learning Fundamentals
- Introduction to deep learning: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks
- Deep learning architectures: AlexNet, VGG, and ResNet
- Deep learning techniques: transfer learning, data augmentation, and batch normalization
- Deep learning applications: image classification, object detection, and natural language processing
- Introduction to generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Module 4. Real-World Applications of Machine Learning: Time series forecasting: ARIMA, LSTM, and Prophet
- Image classification: handwritten digit recognition, object detection, and image segmentation
- Natural language processing: text classification, sentiment analysis, and language translation
- Speech recognition: speech-to-text and voice recognition
- Recommendation systems: collaborative filtering and content-based filtering
- Time series forecasting: ARIMA, LSTM, and Prophet
Module 5: Advanced Topics in Machine Learning
- Transfer learning: fine-tuning pre-trained models and domain adaptation
- Attention mechanisms: self-attention and multi-head attention
- Graph neural networks: graph convolutional networks and graph attention networks
- Explainability and interpretability: feature importance, partial dependence plots, and SHAP values
- Adversarial attacks and defenses: evasion attacks, poisoning attacks, and adversarial training
Module 6. Machine Learning with Python: Deep learning with Python: TensorFlow, PyTorch, and Keras
- Introduction to Python for machine learning: NumPy, Pandas, and scikit-learn
- Building and training machine learning models with scikit-learn
- Deep learning with Python: TensorFlow, PyTorch, and Keras
- Using pre-trained models and fine-tuning with transfer learning
- Visualizing and interpreting results with Matplotlib and Seaborn
Module 7. Machine Learning with R: Deep learning with R: Keras and TensorFlow
- Introduction to R for machine learning: dplyr, tidyr, and caret
- Building and training machine learning models with caret
- Deep learning with R: Keras and TensorFlow
- Using pre-trained models and fine-tuning with transfer learning
- Visualizing and interpreting results with ggplot2 and Shiny
Module 8. Machine Learning with Julia: Deep learning with Julia: Flux and JuPyte
- Introduction to Julia for machine learning: MLJ, JuPyte, and Flux
- Building and training machine learning models with MLJ
- Deep learning with Julia: Flux and JuPyte
- Using pre-trained models and fine-tuning with transfer learning
- Visualizing and interpreting results with Plots and GR
Module 9. Case Studies and Projects: Speech recognition: building a speech-to-text system
- Image classification: building a handwritten digit recognition system
- Natural language processing: building a sentiment analysis system
- Speech recognition: building a speech-to-text system
- Recommendation systems: building a movie recommendation system
- Time series forecasting: building a stock price forecasting system
Module 10: Final Project and Certification
- Final project: building a machine learning model for a real-world problem
- Certification: receiving a Certificate of Completion upon finishing the course
- Future directions: continuing education and staying up-to-date with industry developments
Certification
Upon completion of the course, participants will receive a Certificate of Completion. The certificate will be issued by [Institution Name] and will be recognized by the industry.Target Audience
This course is designed for anyone interested in machine learning, neural networks, and deep learning, including:- Data scientists and analysts
- Software engineers and developers
- Researchers and academics
- Business professionals and managers
- Anyone interested in machine learning and artificial intelligence