DL201

4-Weeks Advanced Deep Learning Bootcamp from Unpack AI

Deep dive into working with data, data processing and building predictive models.

🎉Congratulations on continuing your learning path to become a versatile AI Practitioner.
At unpackAI, we are strong believers in a top-down approach when it comes to learning technical multi-disciplinary AI skills in order to give a completed picture of the current state-of-the-art and how you can apply it to your personal needs or work. You might have recently completed DL101 and learned about machine learning use cases and built your first AI mini-projects. That was a really good start to build up "I can do it" confidence and excitement about the possibilities of AI. At the same time, you also encountered some limitations in exploring, buidling and validating good AI projects, such as :

• data collection, data preparation and data quality issues
• scarcity of computing power to train big models on Colab or Kaggle
• and above all, your own limited coding skills and lack of strong technical foundation.

🔥🔥🔥That's why we invite you to take another BIG leap forward by joining your next adventure in harnessing artificial intelligence: DL201 Bootcamp with deep dive into working with data, data processing and building predictive models. Let's learn what the course is about :👇

1. 🎳Goals

Be able to build AI proof of a concept to demonstrate to a software developer what should be done in a production environment. You will be able to connect the dots between the business requirements and technical feasibility of the AI project.

• Be able to work with the most common data types to load, analyze and prepare them for building predictive models. You will be able to parse images (jpg, png etc), text (pdf, word etc), web pages (html, json) and convert them into data frames, a required step before starting your ML experimentation.
  
  
• Be able to experiment with the most promising neural network architectures, traditional ML algorithms and pretrained models to find out which one fits data best. You will able to compare the results by understanding the mechanics of the model training and evaluation.
  
  
• Be able to work with many python packages and ML and AutoML tools, e.g pandas, numpy, pytorch, fast.ai, tai-chi, pycaret, unpackai and many more.
  
  
• Get familiar with the everyday techniques and skills of machine learning engineers and software developers, such as check github repos for code, read documentation, google as a pro etc
  
  
• Become a contributor to unpackai's AutoML packages: unpackai and tai-chi that simplifies the code and accelerates the development cycles of AI models. Unlock the opportunity to become the mentor for DL101s to enable more business professionals to learn and apply AIML.
  

2. Schedule

Week	Skills	Learning Content
0	Course deliverables and project setup	Meet your mentors, unpackers Understand the bootcamp objectives and logistics Receive the framework for building your AI project in this course. Learn and build as you go over the first three weeks, finalize the project by the end of 4th week.
1	Data Loading and Exploratory Analysis	Understand how to load datasets and metadata in the most common file formats Explore the datasets and discern if it is suitable for adaptation to our problem Gain tools to manipulate metadata and tabular data using Pandas Have an appreciation for how data can be represented as tensors Explore the data-centric approach in AI, and learn about its importance in Machine & Deep Learning. Utilize tools to label, improve and balance your datasets.
2	Data Preprocessing and Transformations	Explore common data wrangling tasks Learn how to apply feature engineering methods to spreadsheet data, such as grouping into categories, feature decomposition, tabular data transformation methods Master computer vision preprocessing techniques like label encoding, handling unbalanced classes; image data transformation like normalizing pixel values Dive into text preprocessing for NLP tasks like encoding and embeddings
3	Algorithms and Model Training	Discuss the most common cutting edge DL algorithms and architectures in computer vision, NLP Explore the best performing machine learning models used in supervised machine learning for structured data Learn how to apply pretrained models on new ML tasks with hyperparameters optimization and most common fine-tuning approaches Train Neural Network from Scratch using only Pytorch to undersand the mechanics of building ML model.
4	Project Finalization	Fully apply newly gained machine learning skills to your ongoing project to deliver the final results. Get on 1:1 calls with mentors to get personalized feedback and recommendations before presenting it as a proof-of-concept project on Demo Day.
5	Graduation and Demo Day	Present your project in the final session, receive the final feedback from mentors in order to get yourself ready for Demo Day Participate in Demo Day to showcase your achievement and feature your project in front of anyone. We will broadast this event on our social media to invite anyone interested in real AI use cases built by our graduates. Receive the certificate and get endorse for your skills on LinkedIn