{ "generated_at": "2026-09-25T22:01:46+00:00", "courses": [ { "id": 16, "slug": "deep-learning-foundations", "title": "Deep Learning Foundations with PyTorch", "url": "https://capabilitynext.com/course-deep-learning-foundations", "apply_url": "https://capabilitynext.com/contact?course=deep-learning-foundations&title=Deep%20Learning%20Foundations%20with%20PyTorch", "summary": "Tensors, autograd, training loops and the core architectures — build MLPs and CNNs from scratch, then with PyTorch.", "tagline": "Tensors, autograd, training loops and the core architectures — build MLPs and CNNs from scratch, then with PyTorch.", "pitch": "Stop copy-pasting training loops. In this hands-on course you build a neural network from first principles, then rebuild it cleanly in PyTorch — and learn the debugging habits that separate people who use deep learning from people who can rely on it.", "track": "Foundations", "track_slug": "foundations", "weeks": 9, "from_fee_inr_ex_gst": 28000, "skills": [ "Deep learning with PyTorch", "Deep learning, CNNs and transformers" ], "roles": [ "AI / Machine Learning Engineer", "Computer Vision / NLP Engineer" ], "tags": [ "Computer vision", "Deep learning", "Neural networks", "PyTorch", "Transformers" ], "prerequisites": [ "intro-to-ml", "python-maths-for-ml" ] }, { "id": 15, "slug": "intro-to-ml", "title": "Introduction to Machine Learning", "url": "https://capabilitynext.com/course-intro-to-ml", "apply_url": "https://capabilitynext.com/contact?course=intro-to-ml&title=Introduction%20to%20Machine%20Learning", "summary": "Supervised and unsupervised learning end to end — train, tune, evaluate and explain models with scikit-learn.", "tagline": "Supervised and unsupervised learning end to end — train, tune, evaluate and explain models with scikit-learn.", "pitch": "Learn machine learning the way it is practised: baselines, pipelines, honest validation and metrics that match real decisions — and finish with a model card a stakeholder can rely on.", "track": "Foundations", "track_slug": "foundations", "weeks": 8, "from_fee_inr_ex_gst": 25000, "skills": [ "Bias, leakage & explainability", "Supervised & unsupervised learning, evaluation", "Supervised & unsupervised ML, evaluation" ], "roles": [ "AI / Machine Learning Engineer" ], "tags": [ "Foundations", "Machine learning", "Model evaluation", "Responsible AI", "scikit-learn" ], "prerequisites": [ "python-maths-for-ml" ] }, { "id": 14, "slug": "python-maths-for-ml", "title": "Python & Maths for Machine Learning", "url": "https://capabilitynext.com/course-python-maths-for-ml", "apply_url": "https://capabilitynext.com/contact?course=python-maths-for-ml&title=Python%20%26%20Maths%20for%20Machine%20Learning", "summary": "The working toolkit behind every model: NumPy, pandas, and the linear algebra, calculus and probability you actually use in machine learning — taught through code.", "tagline": "The working toolkit behind every model: NumPy, pandas, and the linear algebra, calculus and probability you actually use in machine learning — taught through code.", "pitch": "The maths and Python that machine learning is built on — taught through code you run, not proofs you memorise. Finish by building your own small ML library, with tests.", "track": "Foundations", "track_slug": "foundations", "weeks": 8, "from_fee_inr_ex_gst": 25000, "skills": [ "Linear algebra & calculus for ML", "Probability & statistics for ML", "Python, NumPy / pandas, the ML maths" ], "roles": [ "AI / Machine Learning Engineer", "Data Engineer" ], "tags": [ "Foundations", "Gradient descent", "Mathematics", "NumPy", "Python" ], "prerequisites": [] }, { "id": 19, "slug": "fine-tuning-and-evaluation", "title": "Fine-tuning & Model Evaluation", "url": "https://capabilitynext.com/course-fine-tuning-and-evaluation", "apply_url": "https://capabilitynext.com/contact?course=fine-tuning-and-evaluation&title=Fine-tuning%20%26%20Model%20Evaluation", "summary": "LoRA / QLoRA, instruction tuning, preference data, and building evaluation suites you can trust before you ship.", "tagline": "LoRA / QLoRA, instruction tuning, preference data, and building evaluation suites you can trust before you ship.", "pitch": "Learn when to fine-tune, how LoRA and QLoRA work, how to build clean datasets — and, above all, how to evaluate and gate every model change with suites, LLM judges and statistics you can defend.", "track": "GenAI & LLM", "track_slug": "genai", "weeks": 7, "from_fee_inr_ex_gst": 22000, "skills": [ "Fine-tuning (LoRA) & model evaluation", "Fine-tuning (LoRA), guardrails, cost / latency", "Tool use, agents, evaluation suites" ], "roles": [ "LLM / Generative-AI Engineer" ], "tags": [ "Fine-tuning", "LLM engineering", "LLM evaluation", "LoRA", "Responsible AI" ], "prerequisites": [ "llm-application-engineering", "deep-learning-foundations" ] }, { "id": 34, "slug": "intro-to-rag", "title": "Introduction to Retrieval-Augmented Generation (RAG)", "url": "https://capabilitynext.com/course-intro-to-rag", "apply_url": "https://capabilitynext.com/contact?course=intro-to-rag&title=Introduction%20to%20Retrieval-Augmented%20Generation%20%28RAG%29", "summary": "A zero-code, example-rich introduction to RAG: what it is, how every component fits together, how to set up a working pipeline with hosted no-code tools, and how to ship a real assistant as your capstone.", "tagline": "No coding required — go from \"what even is RAG?\" to shipping a real, working assistant.", "pitch": "Eight weeks to go from \"I've heard of RAG\" to \"I've built, tuned, evaluated and pitched one\" — with zero lines of code.", "track": "GenAI & LLM", "track_slug": "genai", "weeks": 8, "from_fee_inr_ex_gst": 25000, "skills": [], "roles": [], "tags": [ "Beginner friendly", "Business AI", "Generative AI", "No-code", "RAG" ], "prerequisites": [ "python-maths-for-ml" ] }, { "id": 17, "slug": "llm-application-engineering", "title": "LLM Application Engineering", "url": "https://capabilitynext.com/course-llm-application-engineering", "apply_url": "https://capabilitynext.com/contact?course=llm-application-engineering&title=LLM%20Application%20Engineering", "summary": "A 10-week mentor-reviewed course on building production LLM applications: prompting, RAG, tool use, agents, guardrails, evaluation and cost control. Syllabus, prerequisites and capstone brief.", "tagline": "Design, build, evaluate and ship an LLM product end to end — retrieval, tool use, agents, guardrails, evaluation and cost control. Ten weeks, part-time, mentor-reviewed.", "pitch": "Ten weeks to go from \"I can call an LLM API\" to \"I can ship, evaluate and safely operate an LLM product.\"", "track": "GenAI & LLM", "track_slug": "genai", "weeks": 10, "from_fee_inr_ex_gst": 31000, "skills": [ "Prompt design & structured output", "Prompting, RAG, agents & guardrails", "Tool use & agentic workflows" ], "roles": [ "LLM / Generative-AI Engineer" ], "tags": [ "AI agents", "Guardrails", "LLM engineering", "Production AI", "RAG" ], "prerequisites": [ "intro-to-ml" ] }, { "id": 18, "slug": "rag-and-vector-search", "title": "RAG & Vector Search in Production", "url": "https://capabilitynext.com/course-rag-and-vector-search", "apply_url": "https://capabilitynext.com/contact?course=rag-and-vector-search&title=RAG%20%26%20Vector%20Search%20in%20Production", "summary": "Chunking, embeddings, hybrid search, re-ranking and evaluation — build a retrieval layer that holds up under real queries.", "tagline": "Chunking, embeddings, hybrid search, re-ranking and evaluation — build a retrieval layer that holds up under real queries.", "pitch": "Build retrieval-augmented systems the way production teams do: structure-aware chunking, hybrid search, re-ranking, permission filters, an evaluation set and grounded, cited answers that know when to say \"I don't know\".", "track": "GenAI & LLM", "track_slug": "genai", "weeks": 6, "from_fee_inr_ex_gst": 18500, "skills": [ "Retrieval & vector search in production", "Retrieval evaluation & grounded citations", "Retrieval-augmented generation, vector search" ], "roles": [ "LLM / Generative-AI Engineer" ], "tags": [ "LLM engineering", "Production AI", "RAG", "Retrieval evaluation", "Vector search" ], "prerequisites": [ "llm-application-engineering" ] }, { "id": 20, "slug": "data-engineering-for-ml", "title": "Data Engineering for Machine Learning", "url": "https://capabilitynext.com/course-data-engineering-for-ml", "apply_url": "https://capabilitynext.com/contact?course=data-engineering-for-ml&title=Data%20Engineering%20for%20Machine%20Learning", "summary": "Batch and streaming pipelines, orchestration, data quality and feature stores — get clean, reliable data to your models.", "tagline": "Batch and streaming pipelines, orchestration, data quality and feature stores — get clean, reliable data to your models.", "pitch": "Build the data foundation ML depends on: dimensional models, idempotent batch pipelines, streaming basics, automated data-quality tests, lake and warehouse design, and leakage-free feature pipelines.", "track": "Data & MLOps", "track_slug": "data", "weeks": 9, "from_fee_inr_ex_gst": 28000, "skills": [ "Batch & streaming pipelines, orchestration", "Data quality, testing, cloud warehouses", "Feature stores for ML", "Pipelines & orchestration, feature stores", "Pipelines, orchestration & data quality", "SQL & Python, data modelling" ], "roles": [ "Data Engineer", "MLOps Engineer" ], "tags": [ "Data engineering", "Data pipelines", "Data quality", "Feature stores", "Foundations for ML" ], "prerequisites": [ "python-maths-for-ml" ] }, { "id": 22, "slug": "llmops-and-serving", "title": "LLMOps & Inference at Scale", "url": "https://capabilitynext.com/course-llmops-and-serving", "apply_url": "https://capabilitynext.com/contact?course=llmops-and-serving&title=LLMOps%20%26%20Inference%20at%20Scale", "summary": "Serving frameworks, batching, caching, quantisation, and the cost / latency trade-offs that decide whether an LLM project is viable.", "tagline": "Serving frameworks, batching, caching, quantisation, and the cost / latency trade-offs that decide whether an LLM project is viable.", "pitch": "Understand how LLM inference really works — prefill and decode, continuous batching, KV cache, quantisation, caching, routing and GPU autoscaling — and learn to turn benchmarks into a defensible cost, latency and quality decision.", "track": "Data & MLOps", "track_slug": "data", "weeks": 6, "from_fee_inr_ex_gst": 18500, "skills": [ "LLM serving & inference at scale", "Model serving, batching, drift & retraining", "Quantisation, distillation & inference optimisation" ], "roles": [ "DevOps Engineer → Platform / MLOps", "LLM / Generative-AI Engineer", "MLOps Engineer" ], "tags": [ "Cost optimisation", "GPU", "LLM inference", "LLMOps", "Production AI" ], "prerequisites": [ "mlops-on-the-cloud", "llm-application-engineering" ] }, { "id": 21, "slug": "mlops-on-the-cloud", "title": "MLOps on the Cloud", "url": "https://capabilitynext.com/course-mlops-on-the-cloud", "apply_url": "https://capabilitynext.com/contact?course=mlops-on-the-cloud&title=MLOps%20on%20the%20Cloud", "summary": "Package, deploy, monitor and retrain models with pipelines, containers and CI/CD you can reuse at work.", "tagline": "Package, deploy, monitor and retrain models with pipelines, containers and CI/CD you can reuse at work.", "pitch": "Turn a trained model into a reliable service: registries and promotion gates, containerised endpoints, CI/CD for ML, drift monitoring, safe automated retraining, and cost and SLO management — cloud-neutral and hands-on.", "track": "Data & MLOps", "track_slug": "data", "weeks": 9, "from_fee_inr_ex_gst": 28000, "skills": [ "CI/CD for ML, model registries", "CI/CD, containers, Kubernetes, IaC", "Cloud deployment & ML-platform basics", "Cloud, observability, on-call", "Containerised serving, autoscaling", "Deploy, monitor & retrain models (CI/CD)", "ML pipelines, feature stores, model registries", "Model serving, monitoring & retraining", "Monitoring, drift detection, retraining", "Turning a model into a service" ], "roles": [ "AI / Machine Learning Engineer", "DevOps Engineer → Platform / MLOps", "MLOps Engineer" ], "tags": [ "CI/CD", "Cloud", "MLOps", "Model monitoring", "Production AI" ], "prerequisites": [ "intro-to-ml" ] }, { "id": 23, "slug": "applied-computer-vision", "title": "Applied Computer Vision", "url": "https://capabilitynext.com/course-applied-computer-vision", "apply_url": "https://capabilitynext.com/contact?course=applied-computer-vision&title=Applied%20Computer%20Vision", "summary": "Classification, detection and segmentation with modern backbones — data pipelines, augmentation, transfer learning and deployment.", "tagline": "Classification, detection and segmentation with modern backbones — data pipelines, augmentation, transfer learning and deployment.", "pitch": "Build practical computer-vision skills: data pipelines and augmentation, transfer learning, detection, segmentation, honest evaluation with IoU and mAP, and deployment within real latency budgets.", "track": "CV & NLP", "track_slug": "cvnlp", "weeks": 9, "from_fee_inr_ex_gst": 28000, "skills": [ "Data pipelines, augmentation, transfer learning", "Detection / segmentation, or sequence tasks", "Vision models — detection, segmentation" ], "roles": [ "Computer Vision / NLP Engineer" ], "tags": [ "Computer vision", "Edge AI", "Object detection", "Segmentation", "Transfer learning" ], "prerequisites": [ "deep-learning-foundations" ] }, { "id": 25, "slug": "multimodal-systems", "title": "Multimodal & Vision-Language Systems", "url": "https://capabilitynext.com/course-multimodal-systems", "apply_url": "https://capabilitynext.com/contact?course=multimodal-systems&title=Multimodal%20%26%20Vision-Language%20Systems", "summary": "Combine image and text models: captioning, visual question answering, document understanding and OCR-driven pipelines.", "tagline": "Combine image and text models: captioning, visual question answering, document understanding and OCR-driven pipelines.", "pitch": "Learn how vision-language systems work and how to ship them responsibly: CLIP-style retrieval, VLM prompting and hallucination control, document AI with OCR and validation, evaluation by slice, and cost-aware cascades.", "track": "CV & NLP", "track_slug": "cvnlp", "weeks": 6, "from_fee_inr_ex_gst": 18500, "skills": [ "Document understanding & OCR pipelines", "Image–text embeddings & retrieval", "Multimodal & vision-language systems" ], "roles": [ "Computer Vision / NLP Engineer" ], "tags": [ "Document AI", "Model evaluation", "Multimodal AI", "OCR", "Vision-language models" ], "prerequisites": [ "applied-computer-vision", "nlp-with-transformers" ] }, { "id": 24, "slug": "nlp-with-transformers", "title": "NLP with Transformers", "url": "https://capabilitynext.com/course-nlp-with-transformers", "apply_url": "https://capabilitynext.com/contact?course=nlp-with-transformers&title=NLP%20with%20Transformers", "summary": "Tokenisation, attention, pretraining and fine-tuning for classification, extraction and sequence tasks with Hugging Face.", "tagline": "Tokenisation, attention, pretraining and fine-tuning for classification, extraction and sequence tasks with Hugging Face.", "pitch": "Understand transformers by building them: BPE tokenisation, masked multi-head attention, NER with BIO decoding, beam search, honest NLP evaluation and efficient serving — then ship a classification and extraction service with a model card.", "track": "CV & NLP", "track_slug": "cvnlp", "weeks": 8, "from_fee_inr_ex_gst": 25000, "skills": [ "Evaluation and deployment of the model", "Language models — transformers, NER, seq2seq", "Tokenisation & text embeddings" ], "roles": [ "Computer Vision / NLP Engineer" ], "tags": [ "Model evaluation", "Named-entity recognition", "NLP", "Text classification", "Transformers" ], "prerequisites": [ "deep-learning-foundations" ] } ], "paths": [ { "title": "Applied CV/NLP certificate", "summary": "Perception and language, then both together. Deep-learning foundations, image models for detection and segmentation, transformer-based language systems, and vision-language models that read documents and answer questions about images.\n\n**You will be able to** build, evaluate and deploy a vision or language model, and combine them in a multimodal application.", "weeks": 20, "role": "Computer Vision / NLP Engineer", "courses": [ "deep-learning-foundations", "applied-computer-vision", "nlp-with-transformers", "multimodal-systems" ] }, { "title": "Introduction to RAG", "summary": "A focused introduction to retrieval-augmented generation: chunk, embed, retrieve, generate and evaluate — the pattern behind most \"chat with your documents\" products. A good first step before the LLM Engineer pathway.", "weeks": 8, "role": "LLM / Generative-AI Engineer", "courses": [ "intro-to-rag" ] }, { "title": "LLM Engineer pathway", "summary": "Become the engineer who ships LLM products. Build applications with prompts, tools and structured output; make them factual with retrieval and vector search; then make them measurably better with fine-tuning and evaluation suites.\n\n**You will be able to** design an LLM feature end to end, ground it in your own data with citations, and prove with an eval set that a change made it better.", "weeks": 26, "role": "LLM / Generative-AI Engineer", "courses": [ "python-maths-for-ml", "llm-application-engineering", "rag-and-vector-search", "fine-tuning-and-evaluation" ] }, { "title": "ML Engineer pathway", "summary": "From zero to a working ML engineer. Start with the Python and maths that ML rests on, learn the end-to-end machine-learning workflow, build neural networks in PyTorch, then learn the data plumbing that keeps real models fed. Every course ends in a portfolio artefact you can show an employer.\n\n**You will be able to** clean data and engineer features, train and evaluate classical and deep models, explain their behaviour, and hand a model to a data pipeline that runs without you.", "weeks": 30, "role": "AI / Machine Learning Engineer", "courses": [ "python-maths-for-ml", "intro-to-ml", "deep-learning-foundations", "data-engineering-for-ml" ] }, { "title": "MLOps bootcamp", "summary": "Run machine learning in production. Data pipelines and quality gates, then packaging, CI/CD, monitoring and drift on the cloud, then the specifics of serving large language models efficiently.\n\n**You will be able to** take a model from a notebook to a monitored, autoscaling service and keep it healthy after launch.", "weeks": 14, "role": "MLOps Engineer", "courses": [ "data-engineering-for-ml", "mlops-on-the-cloud", "llmops-and-serving" ] } ] }