Data Science - Deep Learning

Deep learning is a subfield of machine learning that focuses on neural networks with multiple layers, enabling the modeling of complex patterns and representations in data.

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Data Science - Deep Learning

Course description

This course is designed to provide students with a comprehensive understanding of deep learning techniques and their application in data science. I...

This course is designed to provide students with a comprehensive understanding of deep learning techniques and their application in data science. It covers both theoretical foundations and practical hands-on experience to equip students with the skills needed to work on real-world data science problems using deep learning approaches.


  • Basic understanding of machine learning concepts.
  • Proficiency in at least one programming language (e.g., Python).
  • Familiarity with linear algebra, calculus, and probability.

What you’ll learn

By the end of this course, students will have the knowledge and practical skills necessary to apply deep learning techniques to a wide range of data science tasks, making them well-prepared for careers in data science, machine learning, and artificial intelligence.


Deep Learning Algorithms (3 lectures)
1. CNN – Convolutional Neural Network

2. RNN – Recurrent Neural Network

3. ANN – Artificial Neural Network

Introduction to NLP (6 lectures)
1. Text Pre-processing

2. Noise Removal

3. Lexicon Normalization

4. Lemmatization

5. Stemming

6. Object Standardization

Text to Features(Feature Engineering) (8 lectures)
1. Syntactical Parsing

2. Dependency Grammar

3. Part of Speech Tagging

4. Entity Parsing

5. Named Entity Recognition

6. Topic Modelling

7. TF – IDF

8. Frequency / Density Features

Tasks of NLP (5 lectures)
1.Text Classification

2. Text Matching

3. Levenshtein Distance

4. Flexible String Matching

5. Phonetic Matching

Frequently Asked Questions

Frequently Asked Questions

1. What is deep learning, and how does it relate to data science??

Explanation of deep learning as a subset of machine learning and its relevance in data science.

2. What are the prerequisites for enrolling in this course??

Clarification of the required background knowledge in programming, mathematics, and machine learning concepts.

3. Which programming languages and tools will be used in the course??

Information on the programming languages (e.g., Python) and deep learning frameworks (e.g., TensorFlow, PyTorch) covered.

4. How will this course help me in my data science career??

Explanation of the practical skills and knowledge students will gain and how it can be applied in data science roles.

5. What topics will be covered in the course curriculum??

An overview of the course content, including neural networks, convolutional and recurrent networks, and applications in computer vision and natural language processing.

6. Will there be hands-on projects, and what kind of datasets will we work with??

Information about project-based learning and examples of datasets used for practical exercises.

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