All Projects → Intro-Course-AI-ML → Lessonmaterials

Intro-Course-AI-ML / Lessonmaterials

Open Sourced Curriculum and Lessons for an Introductory AI/ML Course

Projects that are alternatives of or similar to Lessonmaterials

Deep Dream In Pytorch
Pytorch implementation of the DeepDream computer vision algorithm
Stars: ✭ 90 (-36.62%)
Mutual labels:  ai, jupyter-notebook
100daysofcode With Python Course
Course materials and handouts for #100DaysOfCode in Python course
Stars: ✭ 1,391 (+879.58%)
Mutual labels:  jupyter-notebook, course
Ai Dl Enthusiasts Meetup
AI & Deep Learning Enthusiasts Meetup Project & Study Sessions
Stars: ✭ 90 (-36.62%)
Mutual labels:  ai, jupyter-notebook
All4nlp
All For NLP, especially Chinese.
Stars: ✭ 141 (-0.7%)
Mutual labels:  ai, jupyter-notebook
Teach Me Quantum
⚛ 10 week Practical Course on Quantum Information Science and Quantum Computing - with Qiskit and IBMQX
Stars: ✭ 118 (-16.9%)
Mutual labels:  jupyter-notebook, course
Pragmaticai
[Book-2019] Pragmatic AI: An Introduction to Cloud-based Machine Learning
Stars: ✭ 79 (-44.37%)
Mutual labels:  ai, jupyter-notebook
Dopamine
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Stars: ✭ 9,681 (+6717.61%)
Mutual labels:  ai, jupyter-notebook
Ppd599
USC urban data science course series with Python and Jupyter
Stars: ✭ 1,062 (+647.89%)
Mutual labels:  jupyter-notebook, course
Dat8
General Assembly's 2015 Data Science course in Washington, DC
Stars: ✭ 1,516 (+967.61%)
Mutual labels:  jupyter-notebook, course
Sw machine learning
machine learning
Stars: ✭ 108 (-23.94%)
Mutual labels:  ai, jupyter-notebook
Course Computational Literary Analysis
Course materials for Introduction to Computational Literary Analysis, taught at UC Berkeley in Summer 2018, 2019, and 2020, and at Columbia University in Fall 2020.
Stars: ✭ 74 (-47.89%)
Mutual labels:  jupyter-notebook, course
Image classifier
CNN image classifier implemented in Keras Notebook 🖼️.
Stars: ✭ 139 (-2.11%)
Mutual labels:  ai, jupyter-notebook
Vitech
tuyển chọn các tài liệu về công nghệ bằng tiếng Việt
Stars: ✭ 63 (-55.63%)
Mutual labels:  ai, jupyter-notebook
Compling nlp hse course
Материалы курса по компьютерной лингвистике Школы Лингвистики НИУ ВШЭ
Stars: ✭ 85 (-40.14%)
Mutual labels:  jupyter-notebook, course
Aiopen
AIOpen是一个按人工智能三要素(数据、算法、算力)进行AI开源项目分类的汇集项目,项目致力于跟踪目前人工智能(AI)的深度学习(DL)开源项目,并尽可能地罗列目前的开源项目,同时加入了一些曾经研究过的代码。通过这些开源项目,使初次接触AI的人们对人工智能(深度学习)有更清晰和更全面的了解。
Stars: ✭ 62 (-56.34%)
Mutual labels:  ai, jupyter-notebook
Objectron
Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes
Stars: ✭ 1,352 (+852.11%)
Mutual labels:  ai, jupyter-notebook
Coursera Natural Language Processing Specialization
Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.
Stars: ✭ 39 (-72.54%)
Mutual labels:  jupyter-notebook, course
Practical dl
DL course co-developed by YSDA, HSE and Skoltech
Stars: ✭ 1,006 (+608.45%)
Mutual labels:  jupyter-notebook, course
Intro To Deep Learning
A collection of materials to help you learn about deep learning
Stars: ✭ 103 (-27.46%)
Mutual labels:  ai, jupyter-notebook
Curso Python Udemy
Curso Maestro Python 3 en Udemy (20 horas)
Stars: ✭ 134 (-5.63%)
Mutual labels:  jupyter-notebook, course

Introductory AI Course

Open Sourced Materials for the Intro to AI Course

Medium Article on the course: https://medium.com/better-programming/how-to-teach-ai-and-ml-to-middle-schoolers-34bf59262ea8

This is a detailed repository for an introductory AI and Machine Learning course for complete beginners. The course begins by covering the basics of AI and Machine Learning, and has lots of code and notebook examples. The course covers standard machine learning algorithms and progresses to building full-scale deep neural networks. By the end of the course, you will be able to build your own machine learning algorithms and deep neural networks and complete many different types of projects. At the end of the course, we have a variety of to explore, ranging from topics of image classification, time series analysis, and natural langauge processing.

To use this repository, follow the order of notebooks/presentations. This course can be completed over any period of time, but it is recommended that the notebooks are completed in order. The examples are coded primarly with Sci-kit (traditional machine learning algorithms) and Keras (Deep Learning/Neural Networks). A background in coding and python is highly recommended, but not entirely necessary.

Please star this repo if you found it helpful! If you want to use this repository, please fork it to get the materials. This will help us with exposure and growing our course. If you would like to join the organization to add your own repository with code, please email us! ([email protected], [email protected])

Lesson Videos

Here are pre-recorded lectures for each of the 6 lessons. Watch them in order, and whenever there are code examples, just open up the notebook and follow along.

The entire playlist is here: https://www.youtube.com/playlist?list=PLWj-3LXfs4r0pD_fzYXUa2PFJ5JHKwaaW

Overview of AI/ML (Lesson 1): https://youtu.be/pWXR7kh_65g

Machine Learning Theory (Lesson 2): https://youtu.be/RHcKxr0cbj4

Deep Learning Theory and Concepts (Lesson 3): https://youtu.be/8oROUOisDzI

CNN Theory (Lesson 4): https://youtu.be/0RFPyCBoa20

CNN and NN Examples in Keras (Lesson 5): https://youtu.be/pGWwDAiB3Ic

Natural Language Processing Theory and Examples (Lesson 6): https://youtu.be/y6swv4_Gh_U

Intro to AI Course Summer 2020

Summer 2020, June 23 - August 11, 8 classes once every week on Tuesdays 5 PM.

The Intro to AI course is a free course for middle school students to gain a basic understanding of Artificial Intelligence and Machine Learning. Throughout 8 weeks, Ayaan Haque and Viraaj Reddi covered various machine learning topics, from the mathematical theory to neural networks to NLP. By open sourcing these materials, we hope that others can begin guiding young students into the field of AI/ML.

To use the lessons, follow the presentations/notebooks in order, and watch the video associated with the lesson.

Note: Weeks 7 and 8 were for individual projects, so the videos aren't provided.

Week 1: https://www.youtube.com/watch?v=3O0umGVjwW8&feature=youtu.be (Introduction)

Week 2: Currently Unavailable (Linear Regression)

Week 3: https://www.youtube.com/watch?v=kwzVjyIwcwU&feature=youtu.be (Deep Learning Introduction)

Week 4: https://www.youtube.com/watch?v=3e3v1MXOTo4&feature=youtu.be (CNN Theory and Explanation)

Week 5: https://www.youtube.com/watch?v=UvsA06Bz7mo&feature=youtu.be (Building a Neural Network with Keras)

Week 6: Currently Unavailable (Introduction to Natural Language Processing)

Week 7-8: Worked on Projects(No Video)

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].