Machine Learning Lifecycle Course guides beginners through end-to-end ML workflow, from data prep to deployment in a structured 100-day curriculum.
The Machine Learning Lifecycle Course is a comprehensive, hands-on 100-day program designed to lead learners—from entry-level to confident practitioners—through the entire machine learning workflow. Structured around CampusX’s acclaimed 100 Days of Machine Learning content, this module is ideal for aspiring data scientists, engineering students, and tech professionals seeking mastery in practical ML skills.
Why Machine Learning Course?
Centered on the machine learning lifecycle, this course fills the gap between knowing ML algorithms and building deployable real-world solutions. It emphasizes critical stages like data preparation, feature engineering, imputation, model evaluation, and deployment—crafted to transform learners into proficient, industry-ready practitioners.
Learn Machine Learning Course
The Machine Learning Lifecycle Course dives into:
Weeks 1–2: Foundation & Math Essentials
- Python basics: syntax, loops, functions
- Core math: linear algebra, calculus, statistics
- Key ML math foundations for modeling
Weeks 3–4: Data Preparation & EDA
- Preprocessing: handling missing values, scaling, encoding
- Exploratory Data Analysis: visualizations, statistics, data profiling
Weeks 5–6: Supervised Learning Fundamentals
- Regression and classification basics
- Algorithms: decision trees, SVM, logistic regression
Weeks 7–9: Unsupervised Learning & Dimensionality Reduction
- Clustering methods, PCA, t-SNE
- Building compact, interpretable representations
Weeks 10–12: Deep Learning Foundations
- Neural networks fundamentals, CNNs, RNNs
- Applying deep learning to image and sequence data
Weeks 13–14: Advanced Topics & Trends
- Reinforcement learning, transfer learning, GANs, attention mechanisms
Weeks 15–17: Practical ML Deployment
- Operationalizing models: MLOps, pipelines, transformers
- Real-world model production and automation
Weeks 18–19: Ethics & Industry Applications
- Real-world AI use cases (healthcare, finance, ecom)
- Ethical considerations, fairness, responsible AI strategies
Weeks 20–21: Capstone Project
- Build and deploy your own end-to-end ML model
- Experience full cycle: from inception to evaluation and deployment
Benefits Machine Learning Lifecycle Course
- Lifecycle-first approach – Learn machine learning as an integrated, real-world process, not isolated algorithms.
- Daily incremental learning – Bite-sized lessons keep momentum and deepen understanding.
- Beginner-friendly yet robust – Requires only basic algebra, Python, and statistical knowledge; builds toward intermediate-level fluency.
- Hands-on & actionable – Encourages labs, notebooks, and applied learning for solid retention.
- SEO strength keywords – Includes terms like “ML lifecycle,” “machine learning course,” “data preprocessing,” “deep learning,” “MLOps,” “capstone project,” and “ethical AI” for search visibility.
How to Succeed in This Machine Learning Lifecycle Course
- Commit every day – Consistency is the foundation of transformation.
- Code along – Use notebooks and write your own versions of the examples.
- Reflect and document – Maintain notes to reinforce your learning.
- Engage with community – Join forums like r/100daysml, Discord, and others cited in the curriculum sources.
- Build the capstone – Apply everything you’ve learned to consolidate knowledge.
So why wait, let’s learn Machine Learning Lifecycle Course
The Machine Learning Lifecycle Course equips learners with much more than algorithm theory—it builds proficiency across the full ML lifecycle, blending data science fundamentals with deployment readiness. Whether you seek a career pivot into AI, want project-based learning, or aim to deepen ML skills, this course offers a robust, search-friendly, and structured pathway to success.
Contact with Mentor: Nitish Singh
