Masterclass Certificate in Machine Learning: Targeting Frontiers
-- viewing nowThe Masterclass Certificate in Machine Learning: Targeting Frontiers is a comprehensive course that equips learners with essential skills for career advancement in the machine learning industry. This course is crucial in a time when machine learning has become a critical component of modern business, with applications in various sectors such as finance, healthcare, and technology.
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Course Details
• Unit 1: Introduction to Machine Learning – Frontiers in ML, Supervised & Unsupervised Learning, Reinforcement Learning
• Unit 2: Data Preprocessing – Data Cleaning, Feature Engineering, Data Scaling & Normalization
• Unit 3: Supervised Learning Algorithms – Linear Regression, Logistic Regression, Decision Trees
• Unit 4: Unsupervised Learning Algorithms – K-Means Clustering, Hierarchical Clustering, Principal Component Analysis
• Unit 5: Deep Learning – Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
• Unit 6: Natural Language Processing – Text Preprocessing, Sentiment Analysis, Named Entity Recognition
• Unit 7: Computer Vision – Image Preprocessing, Object Detection, Semantic Segmentation
• Unit 8: Evaluation Metrics – Accuracy, Precision, Recall, F1 Score, ROC Curve
• Unit 9: Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization
• Unit 10: Deploying Machine Learning Models – Model Serving, Batch Predictions, Real-time Inference
Career Path
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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