The Machine Learning Specialization is a comprehensive, hands-on program designed to equip learners with the skills and knowledge to build and deploy machine learning models in real-world applications. This course provides a structured pathway from foundational programming skills in Python to advanced machine learning techniques, including deep learning and reinforcement learning.
Through interactive coding exercises, practical case studies, and industry-relevant projects, learners will gain expertise in supervised, unsupervised, deep, and reinforcement learning. The course also includes a final capstone project, allowing participants to showcase their ability to apply machine-learning solutions to real-world challenges.
📅 Our Next Cohort Begins On:
MAY 31, 2025
🕒 Duration:
120 Hours
📍 Format of Learning:
Online & Offline
By the end of this specialization, learners will be able to:
✅ Understand the fundamental principles of machine learning and artificial intelligence
✅ Build and optimize machine learning models using Python and industry-standard libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow
✅ Apply supervised learning techniques, including regression and classification models
✅ Implement unsupervised learning techniques, such as clustering and anomaly detection
✅ Develop deep learning models using neural networks and TensorFlow
✅ Design recommender systems and understand reinforcement learning principles
✅ Work on a real-world machine learning project to demonstrate their skills
This course is designed for:
✔ Beginners who want to start a career in AI, machine learning, or data science
✔ Software developers looking to integrate machine learning into applications
✔ Data analysts seeking to enhance their skill set with predictive modeling techniques
✔ Students and researchers interested in AI-driven solutionsPrerequisites: Basic programming experience is recommended, but not required.
REFLECT AND APPLY
Apply your new skills in your workplace with practical examples from technical environments, supported by reflection prompts to deepen your learning.
SHOWCASE YOUR SUCCESS
Earn a Professional Certificate along with Continuing Education Units to highlight your achievements.
LEARN FROM EXPERTS
Benefit from the knowledge and experience of top faculty members and industry leaders.
LEARN AT YOUR OWN PACE
Access the content online anytime and enjoy the flexibility of learning on the go.
INTERACT WITH SPECIALISTS
Engage with an international network of professionals while tackling projects inspired by real-world scenarios.
HANDS-ON LEARNING
Master skills through hands-on simulations, case studies, tools, and assessments.
Course Curriculum
| Track 1: Python for Machine Learning (Beginner Level) | |||
| 1. Introduction to Python & Jupyter Notebooks Details | 00:00:00 | ||
| 2. Python Data Structures & Fundamentals Details | 00:00:00 | ||
| 3. Working with Data in Python Details | 00:00:00 | ||
| 4. APIs and Data Collection Details | 00:00:00 | ||
| Track 2: Supervised Learning – Regression & Classification (Intermediate Level) | |||
| 1. Introduction to Machine Learning Details | 00:00:00 | ||
| 2. Regression Models Details | 00:00:00 | ||
| 3. Classification Models Details | 00:00:00 | ||
| 4. Model Evaluation & Optimization Details | 00:00:00 | ||
| Track 3: Deep Learning & Neural Networks (Advanced Level) | |||
| 1. Neural Networks Fundamentals Details | 00:00:00 | ||
| 2. Training Deep Neural Networks Details | 00:00:00 | ||
| 3. TensorFlow & Keras for Deep Learning Details | 00:00:00 | ||
| 4. Special Topics in Deep Learning Details | 00:00:00 | ||
| Track 4: Unsupervised Learning & Reinforcement Learning (Specialization Level) | |||
| 1. Unsupervised Learning & Clustering Details | 00:00:00 | ||
| 2. Recommender Systems Details | 00:00:00 | ||
| 3. Dimensionality Reduction & Feature Engineering Details | 00:00:00 | ||
| 4. Introduction to Reinforcement Learning Details | 00:00:00 | ||
| Final Capstone Project | |||
| Learners will work on a real-world ML project, applying concepts from all four tracks. Details | 5, 00:00 | ||
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