Practical Nlp & Dl: From Text To Neural Networks (12+ Hours)

Learn text preprocessing, vectorization, neural networks, CNNs, RNNs, and deep learning with real-world NLP project

What you'll learn

Learn core NLP tasks like tokenization, stemming, lemmatization, POS tagging, and entity recognition for effective text preprocessing.

Convert text into vectors using One-Hot, TF-IDF, BOW, N-grams, and Word2Vec for ML and DL models.

Understand and implement neural networks, including perceptron, ANN, and backpropagation with math.

Master deep learning concepts like activation functions, loss functions, and optimization techniques like SGD and Adam

Build NLP and computer vision models using CNNs and RNNs with real-world datasets and end-to-end workflows

Requirements

Basic Python programming knowledge – including variables, functions, and loops, to follow along with NLP and DL implementations

Familiarity with high school math – especially linear algebra, probability, and functions, for understanding neural networks and backpropagation.

Interest in AI, ML, or data science – no prior experience in NLP or deep learning is required; concepts are taught from the ground up

Description

This course is designed for anyone eager to dive into the exciting world of Natural Language Processing (NLP) and Deep Learning, two of the most rapidly growing and in-demand domains in the artificial intelligence industry. Whether you're a student, a working professional looking to upskill, or an aspiring data scientist, this course equips you with the essential tools and knowledge to understand how machines read, interpret, and learn from human language.We begin with the foundations of NLP, starting from scratch with text preprocessing techniques such as tokenization, stemming, lemmatization, stopword removal, POS tagging, and named entity recognition. These techniques are critical for preparing unstructured text data and are used in real-world AI applications like chatbots, translators, and recommendation engines.Next, you will learn how to represent text in numerical form using Bag of Words, TF-IDF, One-Hot Encoding, N-Grams, and Word Embeddings like Word2Vec. These representations are a bridge between raw text and machine learning models.As the course progresses, you will gain hands-on experience with Neural Networks, understanding concepts such as perceptrons, activation functions, backpropagation, and multilayer networks. We’ll also explore CNNs (Convolutional Neural Networks) for spatial data and RNNs (Recurrent Neural Networks) for sequential data like text.The course uses Python as the primary programming language and is beginner-friendly, with no prior experience in NLP or deep learning required. By the end, you’ll have practical experience building end-to-end models and the confidence to apply your skills in real-world AI projects or pursue careers in machine learning, data science, AI engineering, and more.

Overview

Section 1: Basics Python Coding Exercise

Lecture 1 Introduction

Section 2: Introduction

Lecture 2 Introduction to Course Workflow

Lecture 3 Use Cases of NLP

Lecture 4 NLTK and SpaCy Comparision

Section 3: Text Preprocessing methods

Lecture 5 Tokenization

Lecture 6 Stemming Methods

Lecture 7 Snowball Stemmer

Lecture 8 Lemmatization

Lecture 9 Stopwords

Lecture 10 POS tagging

Lecture 11 NER (named entity recognition)

Lecture 12 Summary

Section 4: TextToVector Conversion Methods

Lecture 13 Introduction

Lecture 14 OHE theory+Implementation

Lecture 15 BOW theory+implementation

Lecture 16 N - grams

Lecture 17 TF-IDF

Lecture 18 Word2Vec

Lecture 19 Some Important Terms

Lecture 20 CBOW & Skipgram

Lecture 21 Avgword2Vec

Section 5: Deep Learning Fundamental for NLP

Lecture 22 Overview

Lecture 23 Why DL?

Lecture 24 Perceptron

Lecture 25 Advantages & Disadvantages of Perceptron

Lecture 26 understanding ANN with math intuition

Lecture 27 Backpropagation

Lecture 28 chain rule of derivatives

Lecture 29 Sigmoid Activation function with implementation

Lecture 30 Tanh Activation function with implementation

Lecture 31 ReLu Activation function with implementation

Lecture 32 Leaky ReLu and Parametric ReLu

Lecture 33 Elu

Lecture 34 SoftMax Activation function (multiclass classification)

Lecture 35 Summary & comparison of Activation Functions

Lecture 36 Error calculation for regression problems

Lecture 37 Entropy

Lecture 38 Recap and right combination

Lecture 39 Some Q&A

Section 6: Training Neural Networks

Lecture 40 Gradient Descent Optimizer

Lecture 41 SGD

Lecture 42 Adagrad

Lecture 43 Adadelta and RMSprop

Lecture 44 AdamOptimizer(Best)

Lecture 45 Exploding Gradient Problem and comparison with vanishing gradient

Lecture 46 Weight Initializing Techniques

Lecture 47 Dropout layer

Section 7: CNNs (Convolutional Neural Networks)

Lecture 48 RNN vs CNN vs ANN

Lecture 49 CNN Overview

Lecture 50 Images Overview

Lecture 51 Convolution Operation

Lecture 52 Padding

Lecture 53 Example

Lecture 54 Max,Mean,Min Pooling

Lecture 55 MNIST + RGB workflow

Lecture 56 End To End implementation of MNIST

Lecture 57 EarlyStopping Concept

Lecture 58 Summary

Section 8: NLP

Lecture 59 Basic of NLP

Lecture 60 Simple RNN

Lecture 61 Implementation

Lecture 62 Forward Propagation and Implementation

Lecture 63 Backward Propagation

Lecture 64 Problems with RNN

Lecture 65 LSTM Architecture

Lecture 66 Forget gate

Lecture 67 Input gate

Lecture 68 Output Gate

Lecture 69 Implementation of LSTM

Lecture 70 Variations of LSTM

Lecture 71 BiRNNs

Section 9: Sentiment Analysis Project

Lecture 72 Project implementation

Lecture 73 Optimized code Explanation

Computer Science and IT students looking to specialize in AI, ML, or NLP fields,Electronics and Communication (ECE) students interested in signal processing and AI applications,Data Science and Applied Mathematics learners aiming to implement ML models in real-world scenarios,Engineering or Science graduates planning to upskill or switch to careers in AI, data analytics, or software development