-8%

Mastering Machine Learning: A Comprehensive 8-Week Program

Dropledge Academy

Last Update September 12, 2024

0 already enrolled

About This Course

This intensive 8-week program is designed to equip you with a thorough understanding of machine learning concepts, tools, and applications. Led by experienced instructors in a live, interactive format, the course is structured to take you from foundational principles to advanced techniques, ensuring that you gain practical, hands-on experience along the way.

Whether you’re a student looking to build a solid foundation or an industry professional aiming to deepen your expertise, this course offers a tailored curriculum that meets your learning needs.

Time Commitment: 8 weeks
Learning Type: Live, Instructor-Led

Have questions or seeking personalized options? Whether it’s one-on-one learning for individuals or a customized program for your organization, we’re here to support you.

Write to us media@dropledge.org

Learning Objectives

Master Machine Learning Essentials: Build a solid foundation in machine learning, covering supervised, unsupervised, and reinforcement learning, along with key concepts like features, labels, models, and predictions.

Hands-On Learning: Gain practical experience through real-world case studies and projects, enabling you to apply theoretical knowledge to real-world problems.

Tailored Curriculum: Follow a flexible learning path customized to your background, whether you're a beginner or advancing to professional-level techniques.

Advanced Techniques: Delve into deep learning, neural networks, transfer learning, and GANs, staying current with the latest industry advancements.

Ongoing Support: Benefit from continuous guidance from expert instructors, with personalized mentorship to help you stay on track and meet your learning goals.

Career & Industry Insights: Access sessions focused on career paths, industry trends, and best practices in AI to help you make informed decisions in a rapidly evolving field.

Structured Progression: Learn systematically from basic to advanced levels, ensuring a comprehensive grasp of machine learning concepts and techniques.

Real-World Applications: Apply your skills in hands-on projects and a capstone project, proving your ability to build and deploy machine learning models in practical settings.

Stay Competitive: Equip yourself with the latest tools and techniques, ensuring you remain at the forefront of a fast-paced, technology-driven industry.

Target Audience

Students: Must understand math basics (linear algebra, calculus, probability), be proficient in programming (especially Python), and have a good grasp of algorithms and data structures.
Industry Professionals: Should possess programming skills, be well-versed in data tools, and have practical machine learning experience.

Curriculum

28 Lessons

Week 1: Fundamentals of Machine Learning

Introduction to machine learning and its applications00:00:00

Understanding supervised, unsupervised, and reinforcement learning00:00:00

Overview of key concepts: features, labels, models, and predictions00:00:00

Week 2: Data Preprocessing

Week 3: Supervised Learning

Week 4: Unsupervised Learning

Week 5: Introduction to Deep Learning

Week 6: Deep Learning Advancement

Week 7: Machine Learning Pipeline

Week 8: Machine Learning – Case Studies

Digital transformation concept. Binary code. AI (Artificial Intelligence).

£289.00~~£315.00~~

8% off

Level

Intermediate

Lectures

28 lectures

Subject

Machine Learning

Mastering Machine Learning: A Comprehensive 8-Week Program

About This Course

Learning Objectives

Target Audience

Curriculum

Week 1: Fundamentals of Machine Learning

Introduction to machine learning and its applications00:00:00

Understanding supervised, unsupervised, and reinforcement learning00:00:00

Overview of key concepts: features, labels, models, and predictions00:00:00

Week 2: Data Preprocessing

Data cleaning and handling missing values00:00:00

Feature scaling and normalization techniques00:00:00

Handling categorical data and encoding00:00:00

Exploratory data analysis (EDA) and data visualization00:00:00

Week 3: Supervised Learning

Linear and polynomial regression00:00:00

Logistic regression for classification00:00:00

Decision trees and ensemble methods (bagging, boosting)00:00:00

Evaluation metrics: accuracy, precision, recall, F1 score00:00:00

Week 4: Unsupervised Learning

Clustering algorithms: K-means, hierarchical clustering00:00:00

Dimensionality reduction: PCA (Principal Component Analysis)00:00:00

Anomaly detection techniques00:00:00

Evaluation methods for unsupervised learning00:00:00

Week 5: Introduction to Deep Learning

Basics of neural networks: layers, activation functions, backpropagation00:00:00

Building a simple neural network model00:00:00

Convolutional Neural Networks (CNNs) for image classification00:00:00

Recurrent Neural Networks (RNNs) for sequential data analysis00:00:00

Week 6: Deep Learning Advancement

Deep learning advancements: GANs (Generative Adversarial Networks), transfer learning00:00:00

Fine-tuning techniques for pre-trained models00:00:00

Week 7: Machine Learning Pipeline

Overview of the machine learning pipeline steps00:00:00

Considerations & benefits of a machine learning pipeli00:00:00

Machine learning pipeline tools00:00:00

Model monitoring, maintenance & compliance00:00:00

Week 8: Machine Learning – Case Studies

Real-world machine learning systems00:00:00

Applications in cutting-edge fields00:00:00

Capstone Project00:00:00

Login