(A.I) Deep Learning Specialization

Weeks ( 1 - 9 )

Module 01: Introduction to Data Science

Module 02: Introduction to Python

Module 03: Descriptive statistics and Probability Distributions

Module 04: Important Packages in Data Science (Numpy and Pandas)

Module 05: Charting Fundamentals with Matplolib and Seaborn

Module 06: Step into the world of Data Science

Module 07: Introduction to Machine Learning

Module 08: Linear and Logistic Regression

Module 9: Exploratory Data Analysis: Data Visualisation

Module 10: Exploratory Data Analysis: Case Study

Module 11: Boosting

Module 12: Support Vector Machine

Module 13: Stacking

Module 14: Unsupervised learning (Anomaly Detection)

Module 15: Research Methodology in Machine Learning

Weeks ( 10 - 12 )

Module 16: PCA and LDA

Module 17: KNN

Module 18: Kmeans Clustering

Module 19: Market Basket Analysis

Module 20: Recommender Systems

Weeks ( 13 - 15 )

Module 21: Introduction to Deep Learning

Module 22: Basics of tensorflow programming

Module 23: Introduction to Neural Networks

Weeks ( 16 - 21 )

Module 24: Introduction to Spacy Toolkit - it's Data Structures & Models

Module 25: Basic Text Classification Model

Module 26: Word Vectors & Representations

Module 27: RNN & CNN in Text Classification

Module 28: Standard Text Problems - Classification, Entity Prediction, Sentiment Analysis

Module 29: Transformer Based Text Models - Elmo, GPT2 & Bert

Module 30: Standard Text Problems - Classification, Entity Prediction, Sentiment Analysis

Module 31: NLP Production Grade Use case

All the courses are instructor-led and take place online. The online interface lets you and the faculty have a two-way interaction. It’s as good as sitting in a physical classroom.

All classes take place over the weekends in the mornings. There’ll be one class of 2 to 2.5 hours on Saturdays and Sundays each. This means that you can now acquire in-demand skills without compromising on your schedule.

Yes, the recordings of the trial classes are uploaded.

In physical classrooms, students generally feel hesitant to ask questions. If you miss any class or didn’t understand some concepts, you can’t go through the class again. However, in online courses, it’s possible to do that. We share the recordings of all our classes after each class with the student. Also, there’s no hassle of long distance commuting and disrupting your schedule.

We believe that unless you implement the concepts studied in the classes, you are unable to join the dots and hence can’t see the entire picture. Our capstone projects let you apply the learned concepts to real-world data sets. You’ll be working on real-time data-sets (which can run in 100s of MBs or possibly in GBs!) which you get to choose from a variety of domains such as retail, finance, social media, healthcare, etc. These datasets have been curated from top sources such as World Bank, US Health Department, Carnegie Mellon, Stanford and many more.

You’ll get access to our virtual computing lab through the login credentials provided to you. The virtual machines will enable you to work on “Big Data” sets for your projects and practice hands-on too.

We provide certification of completion to students who attend at least 70% of the classes. After the course ends, we conduct a certification exam that evaluates them on the skills they have learnt. Certification from Hatigen is provided to only those who clear the exam. The exam is purely case-based and not even a single theory question is asked.

We provide career workshops and industry immersion sessions to help you become ready for roles you are aspiring for. We also help you in resume review and interview preparation. If you diligently follow our advice, you should start getting interview calls as soon as you finish the course.