Learn Advanced Deep Learning and NLP Online: Free Course with AI Tutor & Certificate
You'll learn to build text classification models and language generators in this free deep learning course. Across 12 modules and about 1 hour of content, you will explore Recurrent Neural Networks, Transformers, and multimodal NLP. It's a quick, focused way to master natural language processing online.
Advanced Deep Learning and NLP course syllabus
01 Introduction to Deep Learning and NLP
- Basics of Deep Learning
- Introduction to Natural Language Processing
- Text Preprocessing Techniques
- Sentiment Analysis Fundamentals
02 Recurrent Neural Networks (RNNs)
- Understanding RNN Architecture
- Long Short-Term Memory (LSTM) Networks
- Gated Recurrent Unit (GRU) Networks
- Training and Tuning RNNs
03 Advanced NLP Techniques
- Word Embeddings and Word2Vec
- Sequence-to-Sequence Models
- Attention Mechanism in NLP
- Named Entity Recognition (NER)
04 Text Classification with RNNs
- Text Classification Basics
- Building RNN-based Text Classifiers
- Handling Imbalanced Datasets
- Evaluating Text Classification Models
05 Introduction to Transformers
- Transformer Architecture
- Self-Attention Mechanism
- Transformer Encoder and Decoder
- Applications of Transformer Models in NLP
06 Advanced Sentiment Analysis
- Aspect-Based Sentiment Analysis
- Fine-Grained Sentiment Analysis
- Emotion Detection in Text
- Multimodal Sentiment Analysis
07 Language Generation with RNNs
- Text Generation Techniques
- Conditional Language Generation
- Handling Divergent Text Generation
- Evaluating Language Generation Models
08 Transfer Learning in NLP
- Transfer Learning Basics
- Pre-trained Language Models
- Fine-tuning Language Models
- Leveraging Transfer Learning for NLP Tasks
09 Ethical Considerations in NLP
- Bias and Fairness in NLP
- Privacy and Security in NLP Applications
- Ethical Use of NLP Models
- Mitigating Ethical Risks in NLP
10 Advanced Text Preprocessing
- 1 Tokenization and Lemmatization
- 1.1 Tokenization Techniques|Exploring the methods for breaking text into tokens, including word, subword, and character tokenization.
- 1.2 Lemmatization vs. Stemming|Understanding the differences between lemmatization and stemming and when to use each.
- 1.3 Practical Applications of Tokenization and Lemmatization|Discussing how these preprocessing steps are applied in various NLP tasks.
- 2 Handling Out-of-Vocabulary Words
- 2.1 Understanding Out-of-Vocabulary Challenges|Identifying the issues caused by words not present in the model's vocabulary.
- 2.2 Strategies for OOV Words|Exploring techniques such as subword tokenization and character-level models to handle OOV words.
- 2.3 Impact of OOV Words on NLP Performance|Discussing how OOV handling affects the accuracy and robustness of NLP models.
- 3 Text Normalization Techniques
- 3.1 The Need for Text Normalization|Explaining why normalizing text is crucial for consistent NLP model performance.
- 3.2 Common Text Normalization Methods|Reviewing methods like lowercasing, punctuation removal, and unicode normalization.
- 3.3 Normalization in Multilingual NLP|Discussing the challenges and strategies for text normalization in multilingual contexts.
- 4 Handling Noisy Text Data
- 4.1 Challenges Posed by Noisy Data|Understanding the impact of noise such as typos, slang, and abbreviations on NLP tasks.
- 4.2 Noise Reduction Techniques|Exploring methods for cleaning and standardizing noisy text data.
- 4.3 Case Studies of Noisy Text Handling|Analyzing examples where noise reduction significantly improved NLP model performance.
11 Multimodal NLP
- 1 Introduction to Multimodal NLP
- 1.1 Multimodal NLP Concepts|Defining multimodal NLP and its significance in integrating multiple data types.
- 1.2 Applications of Multimodal NLP|Exploring how multimodal NLP is used in areas like sentiment analysis and human-computer interaction.
- 1.3 Challenges in Multimodal NLP|Discussing the complexities and considerations when working with multimodal data.
- 2 Fusion Techniques for Multimodal Data
- 2.1 Fusion Strategies in Multimodal NLP|Understanding different approaches to combine text, audio, and visual data.
- 2.2 Early vs. Late Fusion|Comparing the advantages and limitations of early and late fusion techniques.
- 2.3 Fusion Techniques in Action|Examining case studies where fusion methods have been successfully applied.
- 4 Multimodal Language Generation
- 4.1 Fundamentals of Multimodal Generation|Introducing the concept of generating text that aligns with audiovisual content.
- 4.2 Techniques for Multimodal Generation|Exploring methods for creating coherent multimodal narratives.
- 4.3 Evaluating Multimodal Generation Systems|Discussing metrics and approaches to assess the quality of multimodal language generation.
12 Advanced NLP Applications
- 1 Question Answering Systems
- 1.1 QA System Overview|Defining question answering systems and their role in NLP.
- 1.2 Building Blocks of QA Systems|Exploring the components and architecture of effective QA systems.
- 1.3 Evaluating QA Systems|Discussing methods for assessing the performance of question answering models.
- 2 Text Summarization Techniques
- 2.1 The Purpose of Text Summarization|Understanding the need for summarizing text in various applications.
- 2.2 Extractive vs. Abstractive Summarization|Comparing the two main approaches to text summarization.
- 2.3 Advances in Summarization Algorithms|Reviewing state-of-the-art techniques and models for text summarization.
- 3 Dialogue Systems and Chatbots
- 3.1 Dialogue System Fundamentals|Introducing the principles and types of dialogue systems.
- 3.2 Designing Conversational Agents|Discussing the process of creating engaging and effective chatbots.
- 3.3 Challenges in Dialogue System Development|Exploring common obstacles and how to overcome them in chatbot design.
- 4 NLP for Low-Resource Languages
- 4.1 Importance of Low-Resource Language NLP|Understanding the challenges and significance of NLP for languages with limited data.
- 4.2 Techniques for Low-Resource NLP|Reviewing
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Advanced Deep Learning and NLP course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Data scientists looking to specialize in natural language processing and text analytics.
- Software engineers in India who want to transition into AI and machine learning roles.
- Students seeking a free deep learning course to build advanced AI projects for their portfolios.
- Working professionals who need a quick, focused way to understand transformers and RNNs.
Prerequisites
- Learners should have a basic understanding of Python programming and introductory machine learning.
- Familiarity with foundational neural network concepts is helpful but not strictly required.
- A computer with internet access to run cloud-based notebooks or local Python scripts.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Advanced Deep Learning and NLP course: free, structured and job-focused
Start by writing basic neural networks, then advance to complex text analysis tasks. This nlp training online covers exactly how machines understand and generate human language. You'll begin with the Basics of Deep Learning before moving into Recurrent Neural Networks (RNNs). Once you understand the foundation, the deep learning syllabus expands into Text Classification with RNNs, Introduction to Transformers, and Advanced Sentiment Analysis. Every step focuses on practical concepts rather than just abstract theory, ensuring you know how to handle real text data.
Later sections of this natural language processing tutorial cover Transfer Learning in NLP, Advanced Text Preprocessing, and Multimodal NLP. You will also study Ethical Considerations in NLP to ensure your models are fair, unbiased, and safe for public use. Finally, the Advanced NLP Applications module ties everything together. The content is dense but highly focused, spread across 79 video lessons that take about 1 hour in total to complete.
LearnTube makes this deep learning full course highly interactive and easy to fit into a busy schedule. You watch top curated YouTube video lessons, and an AI tutor generates a quick quiz after every single video to test your knowledge. This immediate feedback loop helps you remember what you just watched. The platform is completely free to learn, giving you full access to all 12 modules. If you want official proof of your new skills for your resume, you can upgrade to an optional paid certificate at the end.
Jobs you can target after learning Advanced Deep Learning and NLP
Advanced Deep Learning and NLP is a gateway skill for some of the fastest-growing roles in tech.
Designs and trains models that understand and generate human language.
Builds and deploys AI systems for various business applications.
Analyzes large datasets and uses deep learning to extract patterns.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a Advanced Deep Learning and NLP certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Advanced Deep Learning and NLP course.
Do I need any specific software installed?
No special software is required to start. You can watch the video lessons and take the AI quizzes directly in your browser. For coding practice, you can use free cloud environments like Google Colab.
Is prior experience needed for this nlp training?
Yes, you should know basic Python and introductory machine learning concepts. Since this is an advanced module, having some familiarity with standard neural networks will make the 79 video lessons much easier to follow.
What job roles can this deep learning certification help me get?
This training prepares you for roles like NLP Engineer, Machine Learning Engineer, and Data Scientist. In India, entry-level professionals in these positions typically earn between ₹5 LPA and ₹10 LPA, depending on the company.
Can I take this free nlp course on my mobile phone?
Absolutely. LearnTube works perfectly on mobile devices. You can watch the curated YouTube lessons and answer the AI-generated quizzes on your phone, making it easy to study during your daily commute.
Is this natural language processing tutorial self-paced?
Yes, the entire program is completely self-paced. You can access all 12 modules whenever you want. The content takes about 1 hour to complete, so you can easily finish it in a single sitting or spread it out.
How much are the nlp course fees?
All the learning content is completely free. You can watch every video and take all the quizzes without paying anything. The only cost is if you choose to buy the optional paid certificate upon completion.
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