NLP COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is a leading training institute dedicated to providing comprehensive NLP courses for individuals at all skill levels. Our NLP courses are thoughtfully designed to cater to beginners, intermediates, and experts in the field. Beginners can expect a solid foundation in NLP concepts, while intermediate learners benefit from in-depth training and practical applications. For experts, we offer advanced NLP techniques and hands-on projects, ensuring a holistic learning experience. With a focus on real-world applications and expert instructors, LearnAI.co.in is your destination for NLP training, no matter your current expertise level.

Upcoming Batches

Course	Branch	Training Type	Week	Day
NLP	Dilsukhnagar	Hybrid	1st	Wednesday
NLP	Dilsukhnagar	Hybrid	2nd	Monday
NLP	Dilsukhnagar	Hybrid	3rd	Wednesday
NLP	Dilsukhnagar	Hybrid	4th	Monday

What is Nlp?

NLP, or Natural Language Processing, is a technology that helps computers understand and interact with human language. It's what powers chatbots, voice assistants, and language translation apps, making it easier for us to communicate with machines in a way that feels natural.

Why to learn Nlp?

Learning NLP is essential for career growth because it opens doors to a wide range of lucrative opportunities in fields like data science, machine learning, and artificial intelligence. As businesses increasingly rely on NLP to analyze and understand customer sentiment and behavior, mastering this skill can lead to well-paid and in-demand job prospects.

Who can Pursue Nlp?

Any Degree pursuing or Graduated,
 Bachelor’s, Master’s, PhD, and
 Anyone interested in course

Enroll to Course Now

COURSE CURRICULUM

Python

Introduction
 Installation
 Fundamental of Python
 Variables
 Comments
 Print Statement
 Operators
 Mutable Data Types
Data Types
Special Data Types
Conditions
Loops
Functions
Break, Continue and Pass Statements
String Object and working
List
Tuple
Set
Dictionaries
Map
Reduce
Filter
Classes
Objects
Inheritance
Multiple Inheritance
Modules
Error Handling

Data Visualization

Matplotlib
 Seaborn
 Plotly
 Cuflinks
 Bokesh

Libraries

Numpy
Pandas
Random
Math
Scipy
sklearn
Keras
Tensorflow
OpenCV
NLTK
Spacy
Lot more…

Tableau

Introduction to Data Visualization
Introduction to Tableau
Basics charts and dashboards
Special Char Types
Dashboard design and principles
Connections with servers
Local file access
Hands on experience with worksheet

Web Scraping

 Url
 Beautiful Soup

Data base

 SQL
 MongoDB

Statistics

Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative Analysis
Quantitative Analysis
Hypothesis Testing
Data Distribution
Probability Distribution
Normal Distribution
Poison Distribution
Outlier Detection
Other Statistical Fundamentals

Machine Learning

Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

Supervised Learning

 Regression Models
Classification Models

Regression Models

Introduction to Regression Models
Linear Models
Linear Regression
Multiple Regression
Ordinary Least Square Method
Non-Linear Models
Support Vector Regressor
Random Forest Regressor
Decision Tree

Evaluation Metrics for Regression Models

R-Square
 Adjusted R Square
 Mean Square Error
Root Mean Square Error
Mean Absolute Error

Classification Models

Introduction to Classification Models
 Logistics Regression
 Naïve Bayes
 Support Vector Classifier
 K-NN Classifier
 Decision Tree Classifier
 Random Forest Classifier

Evaluation Metrics for Classification Models

Accuracy
Precision
Recall
F1 Score
ROC Curve

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering Models

 Introduction to Clustering Models
 K- Mean Clustering
 Hierarchical Clustering
High Dimensional Clustering

Dimension Reduction

Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

Feature Extraction
Model Defects & Evaluation Metrics
Model Selection and Tuning
Comparison of machine learning models

Neural Networks

Introduction to Deep Learning
Fundamental of Neural Networks
TensorFlow and Keras
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Evaluation of Deep Learning
Neural Networks Basics
Gradient Descent
Introduction to Perceptron & Neural Networks
Batch Normalization
Activation and Loss Functions
Hyper parameter tuning
SoftMax
Deep Neural Networks
Weights initialization

Image Pre-processing

Computer Vision
Open CV
Noise Detection
Noise Reduce
Low Pass Filters
Forward Propagation
Backward Propagation
Pooling & Padding

Natural Language Processing

Introduction to NLP
 Corpus
 Natural Language Understanding (NLU)
 Natural Language Generator (NLG)
Tokenization (Word, Sentence, Blank, RegEx)
Frequency Distribution
Filtering
Stemming
Lemmatization
Stop Words
Regular Expressions
 POS Tagging
 Syntax Tree
 Chunking
 Lemmas
 Hypernyms
 Hyponyms
 Synonyms
 Antonyms
 Distance Between words
Wordnet
 Name Entity Recognition
 Bag of words or Document Matrix
 Count Vectorization
 Term Frequency
 Inverse Document Frequency
 Sentimental Analysis

Job Roles

NLP Engineer
Data Scientist
Machine Learning Engineer
AI Research Scientist
Content Writer
Chatbot Developer
Customer Insights Analyst
Language Translator
SEO Specialist
Business Analyst

Components of Nlp

Text Corpora
Tokenization
Stop Words
Part-of-Speech (POS) Tagging
Named Entity Recognition (NER)
Syntax Parsing
Word Embeddings
Text Classification
Sentiment Analysis
Machine Translation
Question Answering
Text Generation
Speech Recognition
Language Generation
Information Retrieval
Text Summarization
Dialog Systems
Deep Learning Models
Evaluation Metrics
Ethical and Bias Considerations