Computers deal with words very differently from people. A sentence that feels obvious to us first has to be broken into smaller pieces, represented in a form a machine can work with, and then compared with patterns learned from data.
That basic idea sits behind tools such as sentiment analysis, text classification, chatbots, and language models.
Natural language processing, or NLP, focuses directly on text and language. Deep learning provides many of the neural-network techniques now used to handle more complicated language and data problems.
Learning the two together can make topics such as Transformers and large language models much easier to follow later.
The courses below give beginners several ways to approach the subject. Some stay close to language processing, while others spend more time on neural networks, TensorFlow, or the mechanics behind modern language models.
Overview: 5 Free NLP and Deep Learning Courses
| Program | Provider | Duration | Fee | Best Aligned With | |
|---|---|---|---|---|---|
| 1 | Introduction to Natural Language Processing | Great Learning Academy | 6.75 hours | Free | NLP concepts and text analysis |
| 2 | Introduction to Natural Language Processing Concepts | Microsoft Learn | 30 min | Free | Basic text and language processing |
| 3 | Introduction to Deep Learning | Great Learning Academy | 2.25 hours | Free course content | Neural-network fundamentals |
| 4 | Intro to Deep Learning | Kaggle | Approx. 4 hours | Free | TensorFlow and neural networks |
| 5 | Introduction to Large Language Models | Google for Developers | 45 min | Free | Transformers and language models |
1. Introduction to Natural Language Processing - Great Learning Academy
This free NLP course by Great Learning gives beginners a broad first look at the way computers handle language. Instead of jumping straight into advanced models, it introduces the terminology used in NLP and then moves toward language models, sentiment analysis, and practical examples.
Delivery & Duration: The course is online, self-paced, and includes 6.75 hours of learning material.
Credentials: Learners can complete the required modules and assessment to become eligible for the course completion certificate.
Program Highlights: NLP concepts, NLP libraries, language models, NLP applications, deep learning in NLP, TextBlob, sentiment analysis, semantic segmentation, and practical demonstrations.
Outcomes: By the end, learners should be able to explain several common NLP tasks and recognize how text data can be prepared and analyzed for language-based applications.
Why should you choose this course?
- It gives you time to understand the subject properly. At 6.75 hours, the course covers more ground than a very short NLP overview.
- The syllabus mixes concepts with examples. Sentiment analysis and library-based exercises help connect terminology with actual use cases.
2. Introduction to Natural Language Processing Concepts - Microsoft Learn
Microsoft Learn offers a much shorter introduction for someone who wants the basic ideas first. The module looks at how text can be split into tokens and how software can use statistical and semantic techniques to interpret language.
Delivery & Duration: Online, beginner level, approximately 30 minutes.
Credentials: Completion and assessment progress can be recorded on a Microsoft Learn profile.
Program Highlights: Tokenization, text processing, statistical analysis, semantic language models, language concepts, and a guided exercise.
Outcomes: Learners get a quick explanation of what happens to text before it can be analyzed by an NLP system.
Why should you choose this course?
- It works well as a short first lesson. You can understand the vocabulary without committing several hours.
- The practical exercise adds context. It gives you something concrete to connect with the theory.
3. Introduction to Deep Learning - Great Learning Academy
The deep learning basics in this course start with artificial neurons and gradually move toward larger neural-network structures. It also introduces several network types that appear frequently in AI discussions, including CNNs, RNNs, and LSTMs.
Delivery & Duration: Online, self-paced, beginner level, with 2.25 learning hours.
Credentials: A completion certificate is available after meeting the course requirements; a certificate fee may apply.
Program Highlights: Perceptrons, artificial neurons, feed-forward networks, activation functions, backpropagation, ANN, CNN, RNN, LSTM, TensorFlow Playground, Python, and Jupyter demonstrations.
Outcomes: The course gives beginners a working vocabulary for neural networks and helps them see how layers, activation functions, and training methods fit together.
Why should you choose this course?
- It explains the building blocks before the larger architectures. That makes CNNs, RNNs, and LSTMs less difficult to understand.
- There is some practical exposure as well. Python and visualization examples make the material less abstract.
4. Intro to Deep Learning - Kaggle
Kaggle is a better fit for learners who want to write some code while studying the topic. Its course uses TensorFlow and Keras, so the emphasis quickly shifts from definitions to building and adjusting neural networks.
Delivery & Duration: Online and self-paced, approximately 4 hours.
Credentials: Learners can earn a Kaggle course completion certificate.
Program Highlights: TensorFlow, Keras, neurons, deep networks, stochastic gradient descent, dropout, batch normalization, binary classification, overfitting, and underfitting.
Outcomes: Learners practice creating neural networks and then see how training choices can affect model performance.
Why should you choose this course?
- It gives you direct coding practice. That is useful if you already know a little Python or want to learn by doing.
- The course also covers common training problems. Overfitting and underfitting are introduced as part of actual model development.
5. Introduction to Large Language Models - Google for Developers
Google’s course moves closer to the systems behind tools such as modern text generators and AI assistants. It explains how language models work with tokens and context, then introduces self-attention and Transformers.
Delivery & Duration: Online, self-paced, approximately 45 minutes.
Credentials: Learners can complete the associated assessment and earn the available Google Developer Program badge.
Program Highlights: Tokens, context, model parameters, language modeling, self-attention, Transformers, fine-tuning, distillation, and LLM limitations.
Outcomes: Learners come away with a clearer sense of how a language model processes text and why attention and context matter so much.
Why should you choose this course?
- It connects earlier NLP ideas with current AI systems. Tokens and language prediction lead naturally into Transformers.
- The course stays focused. It introduces several important LLM concepts without turning into a long technical program.
Conclusion
NLP and deep learning make more sense when they are learned as connected subjects. One explains how text is represented and analyzed, while the other provides many of the neural-network methods used to recognize patterns in that text.
Starting with a free online course lets you find out which side interests you more. You may prefer working with text and language tasks, or you may be more interested in building and training neural networks.
Either foundation can make later topics such as Transformers, generative AI, and large language models easier to understand.
