Get started with Machine Learning with Python - An introduction with Python programming examples

Overview

Machine Learning With Python

Get started with Machine Learning with Python

An engaging introduction to Machine Learning with Python

TL;DR

  • Download all Jupyter Notebooks from repo (zip-file-download).
  • Unzip download (main.zip) appropriate place.
  • Launch Ananconda and start JuPyter Notebook (Install it from here if needed)
  • Open the first Notebook from download.
  • Start watching the first video lesson (YouTube).

Machine Learning (ML)

Goal of Course

  • Learn the advantages of ML
  • Master a broad variety of ML techniques
  • Solve problems with ML
  • 15 projects with ML covering:
    • k-Nearest-Neighbors Classifier
    • Linear Classifier
    • Support Vector Classification
    • Linear Regression
    • Reinforcement Learning
    • Unsupervised Learning
    • Neural Networks
    • Deep Neural Networks (DNN)
    • Convolutional Neural Networks (CNN)
    • PyTorch classifier
    • Recurrent Neural Networks (RNN)
    • Natural Language Processing
    • Text Categorization
    • Information Retrieval
    • Information Extraction

Course Structure

  • The course puts you on an exciting journey with Machine Learning (ML) using Python.
    • It will start you off with simple ML concepts to understand and build on top of that
    • Taking you from simple classifier problems towards Deep Neural Networks and complex information extractions
  • The course is structured in 15 sessions, where each session is composed of the following elements
    • Lesson introducing new concepts and building on concepts from previous Lessons
    • Project to try out the new concepts
    • YouTube video explaining and demonstrating the concepts
      • A walkthrough of concepts in Lesson with demonstrating coding examples
      • An introduction of the Project
      • A solution of the project

Are You Good Enough?

Worried about whether you have what it takes to complete this course?

  • Do you have the necessary programming skills?
  • Mathematics and statistics?
  • Are you smart enough?

What level of Python is needed?

What about mathematics and statistics?

  • Fortunately, when it comes to the complex math and statistics behind the Machine Learning models, you do not need to understand that part.
  • All you need is to know how they work and can be used.
    • It's like driving a car. You do not have to be a car mechanic to drive it - yes, it helps you understand the basic knowledge of an engine and what the engine does.
    • Using Machine Learning models is like driving a car - you can get from A to B without being a car mechanic.

Still worried?

  • A lot of people consider me a smart guy - well, the truth is, I'm not
    • I just spend the hours learning it - I have no special talent
  • In the end, it all depends on whether you are willing to spend the hours
  • Yes, you can focus your efforts and succeed faster
    • How?
    • Well, structure it with focus and work on it consistently.
    • Structure your learning - many people try to do it all at once and fail - stay focused on one thing and learn well.
    • Yes, structure is the key to your success.

Any questions?

  • I try to answer most questions. Feel free to contact me.
Owner
Learn Python with Rune
Learn Python with Rune
TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

TargetAllDomainObjects A python wrapper to run a command on against all users/co

Podalirius 19 Dec 13, 2022
Official implementation of VaxNeRF (Voxel-Accelearated NeRF).

VaxNeRF Paper | Google Colab This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF). VaxNeRF provides very fast training and slightl

naruya 132 Nov 21, 2022
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences This repository is an official PyTorch implementation of Neighbor

DIVE Lab, Texas A&M University 8 Jun 12, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers ๐Ÿ”ฅ

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
EsViT: Efficient self-supervised Vision Transformers

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Microsoft 352 Dec 25, 2022
This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Bin Xiao 175 Jan 08, 2023
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

The first comprehensive Robustness investigation benchmark on large-scale dataset ImageNet regarding ARchitecture design and Training techniques towards diverse noises.

132 Dec 23, 2022
Distributing reference energies for SMIRNOFF implementations

Warning: This code is currently experimental and under active development. Is it not yet suitable for distribution or use as reference implementation.

Open Force Field Initiative 1 Dec 07, 2021
Autoencoder - Reducing the Dimensionality of Data with Neural Network

autoencoder Implementation of the Reducing the Dimensionality of Data with Neural Network โ€“ G. E. Hinton and R. R. Salakhutdinov paper. Notes Aim to m

Jordan Burgess 13 Nov 17, 2022
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

TradingGym TradingGym is a toolkit for training and backtesting the reinforcement learning algorithms. This was inspired by OpenAI Gym and imitated th

Yvictor 1.1k Jan 02, 2023
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

HKBU High Performance Machine Learning Lab 6 Nov 18, 2022
FeTaQA: Free-form Table Question Answering

FeTaQA: Free-form Table Question Answering FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form

Language, Information, and Learning at Yale 40 Dec 13, 2022
To prepare an image processing model to classify the type of disaster based on the image dataset

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas

Bunny Saini 1 Jan 24, 2022
GAN Image Generator and Characterwise Image Recognizer with python

MODEL SUMMARY ๋ชจ๋ธ์˜ ๊ตฌ์กฐ๋Š” ํฌ๊ฒŒ 6๋‹จ๊ณ„๋กœ ๋‚˜๋‰ฉ๋‹ˆ๋‹ค. STEP 0: Input Image Predict ํ•  ์ด๋ฏธ์ง€๋ฅผ ๋ชจ๋ธ์— ์ž…๋ ฅํ•ฉ๋‹ˆ๋‹ค. STEP 1: Make Black and White Image STEP 1 ์€ ์ž…๋ ฅ๋ฐ›์€ ์ด๋ฏธ์ง€์˜ ๊ธ€์ž๋ฅผ ํ‘์ƒ‰์œผ๋กœ, ๋ฐฐ๊ฒฝ์„

Juwan HAN 1 Feb 09, 2022
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning [CVPR'21, Oral] By Zhicheng Huang*, Zhaoyang Zeng*, Yupan H

Multimedia Research 196 Dec 13, 2022
Simulate genealogical trees and genomic sequence data using population genetic models

msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis

Tskit developers 150 Dec 14, 2022
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Wei-Ting Chen 49 Dec 27, 2022
Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Data Augmentation for Scene Text Recognition (ICCV 2021 Workshop) (Pronounced as "strog") Paper Arxiv Why it matters? Scene Text Recognition (STR) req

Rowel Atienza 152 Dec 28, 2022
Learning What and Where to Draw

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee This is the code for our NIPS 201

Scott Ellison Reed 337 Nov 18, 2022
Explainable Medical ImageSegmentation via GenerativeAdversarial Networks andLayer-wise Relevance Propagation

MedAI: Transparency in Medical Image Segmentation What is this repo This repo contains the code and experiments that are implemented to contribute in

Awadelrahman M. A. Ahmed 1 Nov 22, 2021