Machine Learning Engineer – Mastering Predictive Models
Advance your expertise in machine learning with the Machine Learning Engineer course. Dive deep into regression, classification, clustering, and ensemble techniques, while mastering neural networks and feature engineering. Learn to leverage TensorFlow and PyTorch for developing and deploying robust models. This course equips you with the practical skills to design, build, and optimize cutting-edge machine learning solutions for real-world challenges.
- Dive deeper into advanced machine learning techniques.
- Master data preparation, neural networks, and top AI tools like TensorFlow and PyTorch.
- Apply your skills to create a retail recommendation engine.
- Course duration: 6 weeks
- Become a Certified Machine Learning Engineer.
Machine Learning Engineer Course
Machine Learning Engineer Course Overview
- Comprehensive Curriculum – Covers advanced machine learning, deep learning fundamentals, data preparation, and AI tool mastery.
- Machine Learning Techniques – Master regression, classification, clustering, and ensemble methods like Random Forest and XGBoost.
- Neural Networks & Deep Learning – Understand neural architectures, activation functions, backpropagation, and gradient descent.
- Data Preparation & Feature Engineering – Learn encoding, outlier handling, scaling methods, and effective feature extraction.
- AI Tools: TensorFlow & PyTorch – Gain hands-on experience in model building, training, and optimization using leading AI frameworks.
Machine Learning Engineer Course Outline
Module 1: Advanced Machine Learning Techniques
- Explore regression, classification, and clustering algorithms.
- Learn ensemble methods like Bagging (Random Forest) and Boosting (XGBoost).
- Dive into dimensionality reduction with PCA.
- Practical: Build a Random Forest classifier for customer churn analysis.
Module 2: Neural Networks and Deep Learning Basics
- Understand neural network architecture, layers, and activation functions.
- Learn backpropagation and gradient descent principles.
- Practical: Implement a basic neural network using TensorFlow or PyTorch.
Module 3: Data Preparation and Feature Engineering
- Master feature engineering techniques like encoding and outlier handling.
- Learn scaling methods like Min-Max and z-score normalization.
- Practical: Engineer features from a real-world dataset.
Module 4: AI Tools: TensorFlow and PyTorch
- Get hands-on with TensorFlow’s computational graphs and PyTorch’s dynamic operations.
- Build and train models with both frameworks.
- Practical: Develop a classification model using TensorFlow and PyTorch.
- Location – Leeds, UK
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £6300
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £2880
What’s Included in the Machine Learning Engineer Course:
- Flexible Learning / Flexible Delivery
- Self-paced online modules with instructor-led sessions for deeper understanding.
- Hands-on projects and real-world applications accessible anytime, anywhere.
- Exam Preparation
- Practice tests and mock exams simulating real certification assessments.
- Detailed review sessions covering key concepts, problem-solving strategies, and exam techniques.
- Certification
- Industry-recognized certification upon successful course completion.
- Guidance on official Machine Learning certifications, including exam registration and preparation resources.
Prerequisites for Machine Learning Engineer Course
- Programming Knowledge
- Familiarity with Python programming, including concepts like loops, functions, and data structures.
- Mathematics and Statistics
- Understanding of linear algebra, calculus, and probability.
- Familiarity with statistical concepts such as mean, variance, and standard deviation.
- Basic Machine Learning Concepts
- Knowledge of supervised and unsupervised learning.
- Awareness of machine learning lifecycle and algorithms.
- Data Handling Skills
- Experience with data manipulation and analysis using tools like Pandas, NumPy, or Excel.
- Basic Computer Science Knowledge
- Understanding of file systems, data storage, and algorithms.
- Hardware and Software Requirements
- A computer with at least 8GB RAM and an i5 processor.
- Stable internet connection for online tools and resources.
- Optional but Helpful
- Prior experience with any machine learning frameworks or libraries (e.g., TensorFlow, PyTorch).
- Exposure to data visualization tools like Matplotlib or Seaborn.
- Bridge Course (Optional for Beginners)
- A preparatory “AI Foundations Bootcamp” covering Python basics, math for AI, and introductory data handling.
Each participant of this course will have to attend and pass one project and one exam to complete the module and attain the certification.
Module Project (50%)
- Duration: Throughout the course
- Type: Practical project based on the contents of the module- Create a recommendation engine for a retail dataset.
- Pass mark: 65%
Certification Exam (50%)
- Duration: 60 minutes
- Type: 40 multiple choice questions
- Pass mark: 65% (26/40)