AI & Neural Networks
Learn AI fundamentals, biological and artificial neurons, ANN architecture, layers, and activation functions.
ARTIFICIAL INTELLIGENCE COURSE
Artificial Intelligence Fundamentals
From Neurons to NLP
Learn Artificial Intelligence step by step with Codes With Pankaj. Explore artificial neural networks, activation and optimization functions, CNN architectures, transfer learning, and Natural Language Processing through clear explanations and practical projects.
Neural networks CNN & transfer learning NLP & text projects
26 ModulesConcepts & projects
26 ModulesStructured curriculum
ANN & CNNNeural networks
NLPLanguage processing
2 ProjectsPractical learning
COURSE OVERVIEW
Explore AI Through Practical Learning
Build your understanding from the foundations of neurons to neural networks, computer vision, and natural language processing.
Training & Optimization
Explore cost functions, optimization methods, gradient descent, and regularization techniques.
CNN & Transfer Learning
Understand CNN architecture and work with pre-trained models such as VGG16, VGG19, ResNet50, and InceptionV3.
NLP & Word Embeddings
Explore NLTK, Bag of Words, TF-IDF, GloVe, Word2Vec, FastText, Keyed Vectors, and TextBlob.
Learning approach: The supplied course outline includes neural-network concepts, NLP techniques, and two practical projects: one focused on ANN/CNN and one focused on NLP.
WHO SHOULD ENROLL
Is This AI Course Right for You?
Designed for learners who want to understand the concepts behind modern AI and practice implementing them.
- Students and job seekers interested in Artificial Intelligence
- Python learners ready to explore neural networks
- Developers who want to expand into AI concepts
- Learners interested in computer vision and CNNs
- Learners interested in NLP and text analysis projects
GET READY TO LEARN
Prerequisites & Foundations
The supplied course description identifies basic Python knowledge as the starting point.
Basic Python
Comfort with Python fundamentals is recommended for following implementation examples.
Willingness to Practice
Be ready to experiment with code, train models, and review results.
Curiosity About AI
No previous AI course experience is stated as a requirement in the supplied outline.
26 MODULES · 2 PRACTICAL PROJECTS
Artificial Intelligence Course Content
Search the curriculum and open any module to explore the listed subtopics.
26 topic groups
01Module 1: Overview of AIUnderstand artificial intelligence, its need, and real-world applications
- Introduction to Artificial Intelligence
- Need for AI
- Applications of AI
02Module 2: Introduction to NeuronsExplore biological neurons and the artificial neuron model
- What is a Neuron?
- Biological vs. Artificial Neurons
- Understanding the basic concept
03Module 3: Architecture of ANNLearn the structure and major components of artificial neural networks
- Introduction to Artificial Neural Network (ANN)
- Structure and components of ANN
- Types of Neural Networks
04Module 4: Modules in ANNUnderstand network layers and how information moves through a network
- Layers in ANN
- Input, hidden, and output layers
- Forward and backward propagation
05Module 5: Activation FunctionsExplore activation functions and their role in neural networks
- Introduction to activation functions
- Types of activation functions: ReLU, Sigmoid, Tanh
- Usage and importance
06Module 6: Optimization FunctionsUnderstand optimization methods used while training neural networks
- Introduction to optimization functions
- Types of optimization functions
- Usage in training neural networks
07Module 7: Cost FunctionLearn how model error is measured and minimized
- Understanding cost function
- Types of cost functions
- Calculating and minimizing the cost
08Module 8: Dense NetworksStudy fully connected layers and their implementation
- Introduction to Dense Networks
- Structure and working
- Implementing dense layers in neural networks
09Module 9: Regularization TechniquesLearn approaches that help control overfitting
- Need for regularization
- Types of regularization: L1, L2, Dropout
- Applying regularization to neural networks
10Module 10: Gradient DescentUnderstand iterative optimization during model training
- Introduction to Gradient Descent
- Types: Batch, Stochastic, Mini-batch
- Convergence and optimization
11Module 11: ANN (Artificial Neural Network)Build, train, and evaluate a basic neural network
- Building a simple ANN model
- Training and evaluating the model
- Practical implementation with a dataset
12Module 12: Introduction to CNNLearn convolutional neural networks and where they are used
- Basic concepts of Convolutional Neural Network (CNN)
- Difference between ANN and CNN
- Applications of CNN
13Module 13: CNN Architecture BuildingExplore CNN layers and assemble a simple network
- Structure of CNN
- Convolutional, pooling, and fully connected layers
- Building a simple CNN model
14Module 14: Transfer LearningUse pre-trained networks as a starting point for a task
- Introduction to Transfer Learning
- Pre-trained models: VGG16, VGG19, ResNet50, InceptionV3
- Implementing transfer learning
15Module 15: Introduction to NLPExplore the foundations and applications of Natural Language Processing
- Basic concepts of Natural Language Processing (NLP)
- Applications of NLP
- Overview of NLP models
16Module 16: Simple NLP with NLTKUse NLTK for common text-processing tasks
- Introduction to NLTK library
- Stemming and lemmatization
- Regex, stop words, corpus, n-grams
17Module 17: Bag of WordsRepresent text as numerical features for NLP workflows
- Concept of Bag of Words
- Count vectorization
- Implementing Bag of Words in NLP
18Module 18: TF-IDFLearn a weighted representation of terms in text documents
- Introduction to Term Frequency-Inverse Document Frequency (TF-IDF)
- Calculating TF-IDF
- Usage in text analysis
19Module 19: Word EmbeddingUnderstand vector representations of words
- Understanding Word Embedding
- Importance of Word Embedding in NLP
- Techniques for Word Embedding
20Module 20: GloVeExplore Global Vectors for Word Representation
- Introduction to GloVe (Global Vectors for Word Representation)
- Implementing GloVe
- Applications of GloVe
21Module 21: Word2VecLearn CBOW and Skip-gram word representation approaches
- Introduction to Word2Vec
- Continuous Bag of Words (CBOW) and Skip-gram models
- Implementing Word2Vec
22Module 22: FastTextExplore subword-aware word representation
- Introduction to FastText
- Comparison with Word2Vec and GloVe
- Implementing FastText
23Module 23: Keyed VectorsWork with vector representations in NLP tools
- Introduction to Keyed Vectors
- Usage in NLP
- Implementing Keyed Vectors
24Module 24: TextBlobUse TextBlob for basic text analysis
- Introduction to TextBlob library
- Basic operations with TextBlob
- Sentiment analysis using TextBlob
25Module 25: Practical Project 1Apply neural-network concepts in a practical project
- Building a simple ANN or CNN model
- Training and evaluating the model
- Documenting the process and results
26Module 26: Practical Project 2Build an NLP workflow from text processing to model application
- Implementing an NLP model
- Processing text data and applying NLP techniques
- Documenting the process and results
Module topics are based on the curriculum provided. Specific lesson counts, course duration, certification, and delivery formats were not specified in that outline.
LEARN WITH ME
Meet Your Instructor, Pankaj Chouhan
I’m a software engineer and educator with 10+ years of experience, passionate about helping learners understand coding and problem-solving. My approach connects theory with practical examples and real-world application.
Our mission: learn, apply, and innovate through structured, engaging, and career-focused learning.
START YOUR AI JOURNEY
Explore Artificial Intelligence Step by Step
Study neural networks, CNNs, transfer learning, and NLP through the structured curriculum. Book a free demo to discuss your learning goals and course format.