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Machine Learning for Natural Language Processing

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Course Description

Welcome to Innovative Skills, your trusted partner in harnessing the potential of Natural Language Processing (NLP) through state-of-the-art Machine Learning solutions. Our mission is to empower your organization with innovative skills and technology that will revolutionize the way you interact with language data.

Topic-1 (How to do Automated Data Collection for NLP Project)

  • Locator Finding (Xpath. CSS. SELECTOR. ID. CLASS NAME....)
  • How to collect multiple data in single page
  • How to collect multiple page data
  • How to use regex in data collection
  • How to do brainstorm in a critical problems
  • How to handle cache issue
  • How to handle waiting Time
  • How to download file
  • How to handle google recaptcha (common pattern)
  • How to handle JS pagination to collect data
  • How to handle loader using scroll
  • How Does Scroll works mathematically
  • How to handle various click events
  • How to send data to a form
  • Comment Collection project
  • Lead generation Project from Google.
  • How to do freelancing in this sector

Topic-2 (Extraction, Transformation Loading Processing in NLP)

  • Extraction Techniques from various Sources
  • Transformation Techniques from Various Sources
  • SQL (How to Deal with SQL Source)
  • Feature Engineering
  • Loading techniques into various destination
  • Build a projects

Topic-3 (Text Preprocessing in NLP)

  • Lowercasing
  • Removing Punctuation
  • Tokenization
  • Stopwords Removal
  • Stemming
  • Lemmatization
  • Removing Numbers
  • Removing Extra Whitespace

Topic-4 (Vectorization Technique in NLP)

  • Bag of Words (BoW)
  • TF-IDF
  • CountVectorizer
  • Word Embeddings (After Deep Learning)
  • Sentence Embeddings (After Deep Learning)

Topic-5 (Traditional Machine Learning Algorithm - Theory + Implementation)

  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • Support Vector Machine
  • Decision Tree
  • Topic Modeling
  • Clustering
  • 2 Projects

Topic-6 (Deep Learning models - Theory + Implementation)

  • Artificial Neural Network
  • Deep Neural Network
  • Convolutional Neural Network
  • RNN
  • LSTM
  • GRU
  • 2 Projects

Topic-7 (Generative AI Foundation- Theory + Implementation)

  • Transformer
  • Attention with Self Attention
  • Positional Encoding & Layer Normalization

Topic-8 (Large Language Models (Theory + Implementation)

  • LLM use cases and tasks
  • Pre-training various large language models
  • Fine Tune Large Language Models
  • 2 Projects

Topic-9 (Knowledge Distillation)

  • Model Compression
  • Teacher vs. Student Model
  • Soft Targets vs. Hard Labels
  • Temperature Scaling in Softmax
  • KD Loss (KL Divergence between soft outputs)
  • Cross-Entropy with Ground Truth
  • Response-Based Distillation (Logits)
  • Feature-Based Distillation (Intermediate Representations)
  • Relation-Based Distillation (Structure or similarity between layers)
  • Self-Distillation (student learns from its own intermediate layers)

Topic-10 (Evaluation Metrics in Machine Learning - This will cover after Machine Learning Algorithms)

  • Classification Metrics (Accuracy
  • Precision
  • Recall (Sensitivity)
  • F1 Score
  • ROC-AUC Score
  • Confusion Matrix)
  • Regression Metrics (Mean Absolute Error
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R² Score (Coefficient of Determination))
  • Clustering Metrics

Topic-11 (Deep Learning Loss Function Techniques)

  • Understanding Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

Topic-12 (Fine Tuning Techniques in LLM)

  • Transfer Learning
  • Domain-Specific Fine-Tuning
  • Few-Shot and Zero-Shot Learning
  • Prompt Tuning
  • Adapter Tuning
  • Regularization
  • Learning Rate Scheduling

Projects

  • Spam Detection
  • Sentiment Analysis
  • Smart CV
  • AI Chatbot
  • PDF Summarizer Chatbot
  • PDF Formatter
  • Systematic Review Paper Generation
  • AI Agent using Langchain

Evaluation

Image

Reviews through our website

Reviews through Social Media

Tasfiq Kamran

Aminul Mahi

Shaiful Islam

MD Asadullah Shibli

Obaydullah Hasib

Nirban Mitra Joy

Md Maniruzzaman Manir

Md Anower Hossain

Mehedi Azad

Alomghir Hossain

Video Feedback

Student - 1
Student - 2
Student - 3
Student - 4
Student - 5

Key Topics:

  • 1 Automated data collection
  • 2 ETL
  • 3 System Design
  • 4 Text Processing
  • 5 Different Types of ML Algorithm
  • 6 Different Types of DL Algorithm
  • 7 Different Types of Generative AI Algorithm

Price

6000

Discount Price

4020

Duration

5 Months

Available Seats

81

Class Type

Live

Access

Lifetime

Remaining

1 day
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