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Machine Learning · Course Syllabus · Week by Week 2026
Machine Learning Course Modules Explained Week by Week
Quick summary — Machine Learning course modules week by week
A job-ready Machine Learning course in 2026 runs about 12-16 weeks and moves through four phases: Python and data foundations (weeks 1-3), statistics and classical ML (weeks 4-8), deep learning and NLP (weeks 9-11), and model deployment plus a capstone project (weeks 12-16). At Uncodemy, the programme costs ₹34,500 and includes live labs, real datasets, 1:1 mentorship, and placement support. Below is the exact module-by-module breakdown so you know what you'll learn each week before you enroll.
In this guide you will learn:
- Weeks 1-3 — Python and data foundations.
- Weeks 4-8 — statistics and classical machine learning.
- Weeks 9-11 — deep learning, NLP, and model tuning.
- Weeks 12-16 — deployment, MLOps basics, and capstone.
- Tools and libraries covered across the course.
- Common mistakes — what to avoid.
SECTION 01Weeks 1-3 — Python and data foundations
Every strong ML course starts with Python and data handling. You can't build models on messy data, so these first three weeks are about getting comfortable with the tools you'll use for the rest of the course.
Week 1 — Python for Data Science
Python syntax refresher, data structures (lists, dicts, sets), functions, comprehensions, file I/O, virtual environments, and Jupyter notebooks. Focus is on writing clean, reusable code.
Week 2 — NumPy and Pandas
NumPy arrays, vectorised operations, broadcasting. Pandas Series and DataFrames, loading CSV/Excel/JSON, filtering, groupby, merge/join, handling missing values, and dtype management.
Week 3 — Data Cleaning and Visualization
Outlier detection, feature scaling, encoding categorical variables, and exploratory data analysis (EDA) with Matplotlib and Seaborn. You build your first end-to-end EDA notebook on a real dataset.
SECTION 02Weeks 4-8 — statistics and classical machine learning
This is the heart of the course. You move from understanding data to building and evaluating predictive models.
Weeks 4-5 — Statistics & Math
- Descriptive statistics
- Probability distributions
- Hypothesis testing
- Correlation & covariance
- Linear algebra basics
- Gradient descent intuition
Weeks 6-8 — Classical ML
- Linear & logistic regression
- Decision trees & random forests
- KNN, SVM, Naive Bayes
- K-means & PCA
- Cross-validation & grid search
- Metrics: accuracy, precision, recall, F1, ROC-AUC
Week 6 — Supervised Learning Part 1
Linear regression, polynomial regression, regularisation (Ridge, Lasso), and logistic regression. You implement each from scratch in NumPy and then with scikit-learn.
Week 7 — Supervised Learning Part 2
Decision trees, random forests, gradient boosting (XGBoost/LightGBM), SVM, and KNN. Emphasis on when to use which algorithm and how to tune hyperparameters.
Week 8 — Unsupervised Learning & Evaluation
K-means, hierarchical clustering, PCA, and t-SNE. Model evaluation with cross-validation, confusion matrices, and ROC curves. You complete a full supervised ML project.
SECTION 03Weeks 9-11 — deep learning, NLP, and model tuning
Once classical ML is solid, you move into neural networks — the technology behind modern AI products.
Week 9 — Neural Networks Fundamentals
Perceptrons, activation functions, backpropagation, loss functions, and optimisers. You build a neural network from scratch and then in TensorFlow/Keras.
Week 10 — CNNs and Computer Vision
Convolutional layers, pooling, dropout, and transfer learning with pretrained models (ResNet, MobileNet). You build an image classification project.
Week 11 — RNNs, LSTMs, and NLP Basics
Sequence models, RNNs, LSTMs, embeddings, and an introduction to transformers. You build a text classification or sentiment analysis project.
SECTION 04Weeks 12-16 — deployment, MLOps basics, and capstone
The final phase is what separates job-ready candidates from certificate collectors. You learn to ship models, not just train them.
Weeks 12-13 — Deployment
- Model serialisation (pickle, joblib)
- REST APIs with Flask/FastAPI
- Streamlit dashboards
- Docker basics
- Cloud deployment (AWS/Render)
- Model monitoring basics
Weeks 14-16 — Capstone & Career
- End-to-end ML project
- Problem framing & data sourcing
- Model tuning & evaluation
- Deployed demo + GitHub README
- Resume rebuild & portfolio review
- 1:1 mock interviews
Week 12 — Model Deployment
Wrap your model in a FastAPI service, build a simple frontend, and deploy it to the cloud. This is the single most in-demand skill for junior ML roles.
Week 13 — MLOps Basics
Experiment tracking, versioning, basic CI/CD for ML, and monitoring. Understand how models behave in production — the gap between coursework and real jobs.
Weeks 14-16 — Capstone Project & Placement Prep
Build a complete ML product end-to-end — data, model, API, UI, deployment, and documentation. Then move into resume rebuilds, portfolio review, and mock interviews with Uncodemy's placement team.
SECTION 05Tools and libraries covered across the course
Here's the complete toolkit a job-ready Machine Learning course should cover. Check this against any syllabus before you enroll.
Libraries and tools by phase:
- Data handling: NumPy, Pandas, OpenPyXL, SQL basics
- Visualization: Matplotlib, Seaborn, Plotly
- Classical ML: scikit-learn, XGBoost, LightGBM, statsmodels
- Deep learning: TensorFlow, Keras, PyTorch basics
- NLP: NLTK, spaCy, Hugging Face transformers
- Deployment: FastAPI, Flask, Streamlit, Docker, GitHub Actions
- Cloud: AWS SageMaker basics, Render/Vercel for demos
- Version control: Git & GitHub for all projects
SECTION 06Common mistakes — what to avoid
Avoid these traps when following any Machine Learning course curriculum:
- Skipping statistics: You can't tune models or interpret results without a solid statistics base. Don't rush weeks 4-5.
- Watching instead of building: For every hour of lecture, spend two hours coding. Projects are what employers evaluate.
- Ignoring deployment: A model in a notebook is not a product. Deployment is the skill that separates hires from rejections.
- Not documenting projects: A GitHub repo without a clear README might as well not exist to a recruiter.
- Learning tools in isolation: Always tie each library to a real project. Abstract learning doesn't stick.
SECTION 07Test yourself — is this path right for you?
Five questions. No sign-up.
0 / 5Pick an answer to see why it is right or wrong.
SECTION 08Frequently asked questions
How long is a Machine Learning course?
A job-ready Machine Learning course typically runs 12-16 weeks, covering Python foundations, statistics, classical ML, deep learning, deployment, and a capstone project.
What modules are covered week by week?
Weeks 1-3: Python and data foundations. Weeks 4-8: statistics and classical ML. Weeks 9-11: deep learning and NLP. Weeks 12-16: deployment, MLOps basics, and capstone with placement preparation.
Do I need maths for Machine Learning?
Yes, but not advanced maths. Linear algebra, probability, and statistics fundamentals are enough to start. The course covers these in weeks 4-5.
Is deployment covered in the course?
At Uncodemy, yes — weeks 12-13 cover Flask/FastAPI, Docker, Streamlit, and cloud deployment so your models become live products, not just notebooks.
What projects will I build?
You build 3-4 supporting projects (EDA, supervised ML, computer vision, NLP) plus one fully deployed capstone ML product published on GitHub.
SECTION 09Related reads
Classroom & online · Noida
Machine Learning Course — from Python to deployed capstone project
Our Machine Learning using Python course covers Python, statistics, scikit-learn, TensorFlow, NLP, deployment, and a capstone — with live labs, mentorship, and placement support.
₹34,500 · full programme- Python, NumPy, Pandas & statistics
- scikit-learn, XGBoost & deep learning
- NLP & computer vision projects
- Deployment with FastAPI & Docker
- Capstone + placement support