Professional skills

Deep Learning Specialization

Name: Deep Learning Specialization
Price: 2800.00 USD
Availability: InStock

Eight-day, 60-hour deep learning course where you build and train neural networks, CNNs, RNNs and transformers in TensorFlow and PyTorch.

60 hours Advanced Data Science & AI

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THE BIG PICTURE

About this course

The Deep Learning Specialization is a multi-course certificate of completion awarded by DeepLearning.AI and delivered on Coursera. It recognises learners who finish the sequence of courses and graded assessments covering core deep learning topics. Across eight days you will design, train and evaluate models for vision and NLP, working with TensorFlow and PyTorch. You will implement feedforward nets, CNNs, RNNs, LSTMs and introductory transformer workflows, apply regularisation and optimisation techniques, and complete structured labs that mirror the specialization’s assignments. There is no single proctored exam for this credential. Instead, you earn the certificate by completing the specialization’s graded quizzes and programming assignments on Coursera. This course includes a mock assessment and guidance to plan, enrol, and submit the required online assessments. DeepLearning.AI owns the certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner.

What you’ll learn

Build feedforward neural networks with appropriate activations, loss functions and initialisation
Train deep models using mini-batch SGD, adaptive optimisers, learning-rate schedules and regularisation (L2, dropout, batch norm)
Diagnose bias/variance, design robust train/dev/test splits and run systematic error analysis
Implement and fine-tune CNNs for image classification, transfer learning and data augmentation
Develop sequence models (RNNs, LSTMs, GRUs) with embeddings, attention and introductory transformer-based NLP pipelines
Use TensorFlow and PyTorch effectively, including data pipelines, training loops, callbacks and experiment tracking
Prepare models for production considerations such as reproducibility, evaluation, and basic deployment options
Prepare for the specialization’s graded quizzes and programming assignments with practice tasks and a mock assessment

A CLEAR PATH FORWARD

8-day Deep Learning Specialization agenda

8 days · 60 contact hours · 09:00–17:30

8 instructor-led days (60 contact hours) mapped to the exam domains. Each day combines short concept sessions, hands-on practice on a running case study and exam-style questions.

Learning route: 8 recommended days
Practical work: 8 guided activities
Finish with: Mock exam + certification exam

Take a look at each day. Open a section to see the topics and practical work.

Day 1Foundations of neural networksNeural networks fundamentals; optimisation basics

What we’ll cover

09:00–09:30 · Course kickoff, goals, environment check and repo access
09:30–10:30 · Python/NumPy review and vectorisation for deep learning
10:45–12:15 · Logistic regression as a neural net: loss, gradients and SGD
13:15–14:45 · Activation functions, weight initialisation and normalisation
15:00–16:15 · Forward/backprop from scratch; automatic differentiation concepts
16:15–17:00 · TensorFlow and PyTorch primers: tensors, modules and training loops
17:00–17:30 · Exam-style practice questions and wrap-up

Put it into practice

Build a two-layer neural network from scratch in NumPy, then re-implement it in TensorFlow and PyTorch. Compare training curves and accuracy across implementations.

You’ll take away
Working notebooks for scratch, TensorFlow and PyTorch MLPs with notes on differences

Day 2Optimisation and regularisationOptimisation; regularisation; hyperparameter tuning

What we’ll cover

09:00–09:30 · Recap of Day 1 and Q&A
09:30–10:30 · Mini-batch SGD, momentum, RMSProp and Adam
10:45–12:15 · Learning-rate schedules, early stopping and gradient clipping
13:15–14:45 · Regularisation: L2, dropout and batch normalisation in practice
15:00–16:15 · Systematic hyperparameter tuning and experiment tracking
16:15–17:00 · Exam-style practice questions using optimisation and tuning scenarios
17:00–17:30 · Lab debrief and key takeaways

Put it into practice

Tune an MLP on a tabular dataset using different optimisers, L2, dropout and batch norm. Track experiments and select a configuration based on validation metrics.

You’ll take away
Hyperparameter tuning report with chosen configuration and learning-curve plots

Day 3ML project strategy and error analysisProject strategy; data management; error analysis

What we’ll cover

09:00–09:30 · Recap of Day 2 and Q&A
09:30–10:30 · Data splits, leakage avoidance and metric selection
10:45–12:15 · Bias/variance diagnosis and prioritising improvements
13:15–14:45 · Error analysis, ablation studies and robust evaluation
15:00–16:15 · Data augmentation, class imbalance and transfer learning decisions
16:15–17:00 · Exam-style practice questions on strategy and diagnosis
17:00–17:30 · Lab debrief and action plan

Put it into practice

Perform structured error analysis on a pre-trained model, propose targeted fixes, and validate the impact via controlled experiments.

You’ll take away
Error analysis worksheet and experiment log with recommendations

Day 4Convolutional neural networksCNNs; computer vision; transfer learning

What we’ll cover

09:00–09:30 · Recap of Day 3 and Q&A
09:30–10:30 · Convolutions, pooling and receptive fields
10:45–12:15 · Modern CNN architectures (VGG, ResNet) and design choices
13:15–14:45 · Data pipelines and augmentation for images
15:00–16:15 · Transfer learning and fine-tuning a pretrained CNN
16:15–17:00 · Exam-style practice questions on CNN concepts and troubleshooting
17:00–17:30 · Lab debrief and checkpoint

Put it into practice

Fine-tune a ResNet on a custom image dataset, comparing feature extractor vs full fine-tuning. Evaluate accuracy and confusion matrices.

You’ll take away
Fine-tuned CNN model checkpoint and evaluation notebook

Day 5Sequence models: RNNs and LSTMsSequence models; embeddings; attention (intro)

What we’ll cover

09:00–09:30 · Recap of Day 4 and Q&A
09:30–10:30 · RNNs, LSTMs, GRUs and vanishing/exploding gradients
10:45–12:15 · Word embeddings and sequence batching/padding
13:15–14:45 · Sequence-to-sequence modelling and teacher forcing
15:00–16:15 · Attention mechanisms and alignment basics
16:15–17:00 · Exam-style practice questions on sequence modelling
17:00–17:30 · Lab debrief and reading list

Put it into practice

Train an LSTM-based text classifier with pretrained embeddings and compare against a GRU baseline. Add a simple attention layer and assess impact.

You’ll take away
Sequence model notebooks with results table and commentary

Day 6Transformers and modern NLPTransformers; tokenisation; fine-tuning

What we’ll cover

09:00–09:30 · Recap of Day 5 and Q&A
09:30–10:30 · Self-attention, encoder–decoder and positional encodings
10:45–12:15 · Subword tokenisation and batching for transformers
13:15–14:45 · Fine-tuning a pretrained transformer for text classification
15:00–16:15 · Evaluation, error analysis and prompt-based baselines
16:15–17:00 · Exam-style practice questions on transformers and NLP
17:00–17:30 · Lab debrief and next steps

Put it into practice

Fine-tune a small transformer model on an NLP task, measuring accuracy and calibration. Compare with an LSTM baseline on the same data.

You’ll take away
Transformer fine-tuning notebook and comparison report

Day 7Practical workflows and deployment basicsPractical model development; project strategy

What we’ll cover

09:00–09:30 · Recap of Day 6 and Q&A
09:30–10:30 · Efficient data input pipelines and mixed-precision training
10:45–12:15 · Reproducibility, experiment tracking and model versioning
13:15–14:45 · Exporting models (SavedModel, TorchScript, ONNX) and inference
15:00–16:15 · Serving options, performance measurement and monitoring basics
16:15–17:00 · Exam-style practice questions integrating end-to-end workflows
17:00–17:30 · Lab debrief and readiness check

Put it into practice

Package a trained model for inference and serve it locally via a simple API, profiling latency and throughput.

You’ll take away
Exported model artefacts and a minimal serving endpoint

Day 8Capstone labs, mock assessment and next stepsIntegration across all domains; assessment readiness

What we’ll cover

09:00–09:30 · Recap of Day 7 and plan for capstone
09:30–10:30 · Capstone scoping: choose CV or NLP pathway and dataset
10:45–12:15 · Build: data pipeline, baseline model and metrics
13:15–14:45 · Improve: tuning, regularisation and error analysis
15:00–16:15 · Mock assessment mirroring graded quiz and coding tasks
16:15–17:00 · Capstone presentations and feedback
17:00–17:30 · Specialization enrolment and submission guidance (booking and timelines)

Put it into practice

Complete a mini end-to-end project in the chosen pathway and sit a timed mock assessment aligned to typical graded tasks. Receive targeted feedback and a personal completion plan.

You’ll take away
Capstone notebook, mock assessment score report and Coursera submission plan

Bring it all together

Certification exam: DeepLearning.AI Deep Learning Specialization (Coursera), awarded by DeepLearning.AI. Confirm the current exam format with the awarding body when you book. Every attendee also receives a MindClick certificate of completion for 60 contact hours.

Sample MindClick certificate of completion for Deep Learning Specialization, marked SAMPLE — What you receive
You earn two separate credentials. The sample shows the MindClick training certificate.
MindClick certificate of completion: 8 days, 60 contact hours, issued to every attendee who completes the course.
DeepLearning.AI Deep Learning Specialization (Coursera): issued by DeepLearning.AI when you meet its requirements and pass the exam.

Before you choose your learning route

Certification route: This credential is earned by completing the Coursera specialization’s graded quizzes and programming assignments, not by a single voucher-based exam.

Session timings are indicative; the trainer may adjust them for the group. Exam details reflect the awarding body’s published information at the time of writing; confirm the current version when you book.

DeepLearning.AI owns this certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner of DeepLearning.AI.

Deep Learning Specialization — DeepLearning.AI ↗

Fees

Fee per learner outside India. Exam fee included.
Delivery format	Fee per learner	Exam fee
Live online	$2,800 USD	Included
Classroom	$4,500 USD	Included

One awarding-body certification exam attempt is included in the training fee. Confirm the current fee, any applicable tax and what the fee covers in writing before you pay.

Prerequisites

Recommended: solid Python programming, basic linear algebra (vectors, matrices), probability and calculus; prior machine learning exposure is helpful. Bring a laptop with admin rights, a modern browser, and ability to run Python, TensorFlow and PyTorch (install guidance provided).

FIND YOUR START

Confirmed batch starts

Choose a published start, then review your offer in the same enrollment flow. A start date is not the full timetable or a seat reservation.

Choose your country & month

Nov82026