What does the Machine Learning Mastery course cover?
Machine Learning Mastery is covered here in 8 modules: Introduction to Machine Learning, Data Preprocessing and Feature Engineering, Supervised Learning and 5 more. The outline lists 24 specific topics, opening with Defining Machine Learning : Understanding the basics of machine learning and its applications and closing with recommendation Systems : Building personalized recommendation engines using collaborative filtering and matrix factorization.
How do you approach Machine Learning Mastery step by step?
The work is sequenced in 8 stages. It starts with Introduction to Machine Learning, moves through Data Preprocessing and Feature Engineering and Supervised Learning, and ends at Business Applications and Case Studies. 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. It works through Defining Machine Learning : Understanding the basics of machine learning and its applications, Types of Machine Learning : Supervised, unsupervised, and reinforcement learning and machine Learning Workflow : Data preparation, model selection, training, and deployment. It sets the vocabulary the remaining 7 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: Machine Adjustment in Process Optimization Techniques, Machine Learning Techniques in Data mining, Optimization Techniques in Machine Learning for Business, AI Model Governance for Machine Learning Scientists.
More answers: what you get with every course, refund policy, all help answers.
Machine Learning Mastery: Advanced Techniques for Data Scientists and Business Leaders
Course Overview
This comprehensive course is designed to equip data scientists and business leaders with the advanced techniques and skills needed to master machine learning. Participants will gain hands-on experience with real-world applications, expert instruction, and personalized feedback. Upon completion, participants will receive a certificate issued by The Art of Service.Course Curriculum
Module 1: Introduction to Machine Learning
- Defining Machine Learning: Understanding the basics of machine learning and its applications
- Types of Machine Learning: Supervised, unsupervised, and reinforcement learning
- Machine Learning Workflow: Data preparation, model selection, training, and deployment
Module 2: Data Preprocessing and Feature Engineering
- Data Cleaning and Preprocessing: Handling missing values, outliers, and data normalization
- Feature Scaling and Transformation: Standardization, normalization, and feature extraction
- Feature Selection and Dimensionality Reduction: Techniques for selecting relevant features and reducing dimensionality
Module 3: Supervised Learning
- Linear Regression: Simple and multiple linear regression, cost functions, and optimization methods
- Logistic Regression: Binary and multiclass classification, cost functions, and optimization methods
- Decision Trees and Random Forests: Tree-based models, ensemble methods, and hyperparameter tuning
Module 4: Unsupervised Learning
- K-Means Clustering: Clustering algorithms, evaluation metrics, and applications
- Principal Component Analysis (PCA): Dimensionality reduction, eigenvalues, and eigenvectors
- t-Distributed Stochastic Neighbor Embedding (t-SNE): Non-linear dimensionality reduction and visualization
Module 5: Deep Learning
- Introduction to Neural Networks: Basic architecture, activation functions, and backpropagation
- Convolutional Neural Networks (CNNs): Image classification, convolutional layers, and pooling
- Recurrent Neural Networks (RNNs): Sequence modeling, recurrent layers, and long short-term memory (LSTM) networks
Module 6: Model Evaluation and Selection
- Metrics for Evaluation: Accuracy, precision, recall, F1 score, mean squared error, and R-squared
- Cross-Validation: Techniques for evaluating model performance and preventing overfitting
- Model Selection: Choosing the best model for a given problem and dataset
Module 7: Advanced Topics in Machine Learning
- Transfer Learning: Using pre-trained models for new tasks and datasets
- Attention Mechanisms: Focusing on relevant input data for improved model performance
- Generative Adversarial Networks (GANs): Generating new data samples that resemble existing data
Module 8: Business Applications and Case Studies
- Marketing and Customer Segmentation: Using clustering and dimensionality reduction for customer insights
- Fraud Detection and Prevention: Using supervised and unsupervised learning for anomaly detection
- Recommendation Systems: Building personalized recommendation engines using collaborative filtering and matrix factorization
Course Features
- Interactive and Engaging: Hands-on projects, quizzes, and discussions to keep you engaged and motivated
- Comprehensive and Personalized: Expert instruction, personalized feedback, and tailored learning paths
- Up-to-date and Practical: Real-world applications, case studies, and industry-relevant projects
- High-quality Content: Expertly crafted lessons, videos, and resources to ensure your success
- Expert Instructors: Experienced data scientists and business leaders with industry expertise
- Certification: Receive a certificate upon completion, issued by The Art of Service
- Flexible Learning: Learn at your own pace, on your own schedule, and on any device
- User-friendly and Mobile-accessible: Accessible on desktop, tablet, or mobile devices
- Community-driven: Join a community of like-minded professionals for support and networking
- Actionable Insights: Apply your knowledge to real-world problems and projects
- Hands-on Projects: Work on industry-relevant projects to build your portfolio and skills
- Bite-sized Lessons: Learn in manageable chunks, with each lesson building on the previous one
- Lifetime Access: Access to course materials and updates for life
- Gamification and Progress Tracking: Track your progress, earn badges, and compete with peers