World's Best Machine Learning Resources For Beginners, Intermediate, And Advanced Level

World's Best Machine Learning Resources For Beginners, Intermediate, And Advanced Level



If you want to learn machine learning from Scratch (Beginners) to Advanced level, follow the below-given resources step by step.


FOR BEGINNERS LEVEL

1. Understand Machine Learning Algorithms

Machine learning is about machine learning algorithms. You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them.

Here’s how to get started with machine learning algorithms:
  • Step 1: Discover the different types of machine learning algorithms.
  • Step 2: Discover the foundations of machine learning algorithms.
  • Step 3: Discover how top machine learning algorithms work.
Below is a selection of some of the most popular tutorials.

Linear Algorithms
Nonlinear Algorithms
Ensemble Algorithms
How to Study/Learn ML Algorithms

2. Weka Machine Learning (no code)

Weka is a platform that you can use to get started in applied machine learning. It has a graphical user interface meaning that no programming is required and it offers a suite of state of the art algorithms.

Here’s how you can get started with Weka:
Below is a selection of some of the most popular tutorials.

Prepare Data in Weka
Weka Algorithm Tutorials

3. Machine Learning + Python (Scikit-Learn)

Python is one of the fastest-growing platforms for applied machine learning. You can use the same tools as pandas and scikit-learn in the development and operational deployment of your model.

Below are the steps that you can use to get started with Python machine learning:
Below is a selection of some of the most popular tutorials.

Prepare Data in Python
Machine Learning in Python

4. Machine Learning + R

R is a platform for statistical computing and is the most popular platform among professional data scientists. It’s popular because of the large number of techniques available, and because of excellent interfaces to these methods such as the powerful caret package.

Here’s how to get started with R machine learning:
  • Step 1: Discover the R platform and why it is so popular.
  • Step 2: Discover machine learning algorithms in R.
  • Step 3: Discover how to work through problems using machine learning in R.
Below is a selection of some of the most popular tutorials.

Data Preparation in R
Applied Machine Learning in R

5. Time Series Forecasting

Time series forecasting is an important topic in business applications. Many datasets contain a time component, but the topic of time series is rarely covered in much depth from a machine learning perspective.

Here’s how to get started with Time Series Forecasting:
  • Step 1: Discover Time Series Forecasting.
  • Step 2: Discover Time Series as Supervised Learning.
  • Step 3: Discover how to get good at delivering results with Time Series Forecasting.
Below is a selection of some of the most popular tutorials.

Data Preparation Tutorials
Forecasting Tutorials

FOR INTERMEDIATE LEVEL

1. Coding Machine Learning Algorithms

You can learn a lot about machine learning algorithms by coding them from scratch. Learning via coding is the preferred learning style for many developers and engineers.

Here’s how to get started with machine learning by coding everything from scratch.
  • Step 1: Discover the benefits of coding algorithms from scratch
  • Step 2: Discover that coding algorithms from scratch are a learning tool only.
  • Step 3: Discover how to code machine learning algorithms from scratch in Python.
Below is a selection of some of the most popular tutorials.

Prepare Data
Linear Algorithms
Algorithm Evaluation
Nonlinear Algorithms

2. XGBoost in Python

XGBoost is a highly optimized implementation of gradient boosted decision trees. It is popular because it is being used by some of the best data scientists in the world to win machine learning competitions.

Here’s how to get started with XGBoost:
Below is a selection of some of the most popular tutorials.

XGBoost Basics
XGBoost Tuning

3. Deep Learning (Keras)

Deep learning is a fascinating and powerful field. State-of-the-art results are coming from the field of deep learning and it is a sub-field of machine learning that cannot be ignored.

Here’s how to get started with deep learning:
Below is a selection of some of the most popular tutorials.

Background
Multilayer Perceptrons
Convolutional Neural Networks
Recurrent Neural Networks

4. Better Deep Learning Performance

Although it is easy to define and fit a deep learning neural network model, it can be challenging to get good performance on a specific predictive modeling problem. There are standard techniques that you can use to improve the learning, reduce overfitting, and make better predictions with your deep learning model.

Here’s how to get started with getting better deep learning performance:
Below is a selection of some of the most popular tutorials.

Better Learning (fix training)
Better Generalization (fix overfitting)
Better Predictions (ensembles)
Tips, Tricks, and Resources

FOR ADVANCED LEVEL

1. LONG-SHORT TERM MEMORY NETWORKS

Long Short-Term Memory (LSTM) Recurrent Neural Networks are designed for sequence prediction problems and are state-of-the-art deep learning techniques for challenging prediction problems.

Here’s how to get started with LSTMs in Python:
Below is a selection of some of the most popular tutorials using LSTMs in Python with the Keras deep learning library.

Data Preparation for LSTMs
LSTM Behaviour
Modeling with LSTMs
LSTM for Time Series

2. Deep Learning for NLP

Working with text data is hard because of the messy nature of natural language. Text is not “solved” but to get state-of-the-art results on challenging NLP problems, you need to adopt deep learning methods

Here’s how to get started with deep learning for natural language processing:
Below is a selection of some of the most popular tutorials.

Bag-of-Words Model
Language Modeling
Text Summarization
Photo Captioning
Text Translation

3. Deep Learning for Computer Vision

Working with image data is hard because of the gulf between raw pixels and the meaning in the images. Computer vision is not solved, but to get state-of-the-art results on challenging computer vision tasks like object detection and face recognition, you need deep learning methods.

Here’s how to get started with deep learning for computer vision:
Below is a selection of some of the most popular tutorials.

Image Data Handling
Image Data Augmentation
Image Classification
Image Data Preparation
Basics of Convolutional Neural Networks
Object Recognition

4. Deep Learning for Time Series Forecasting

Deep learning neural networks are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs. Methods such as MLPs, CNNs, and LSTMs offer a lot of promise for time series forecasting.

Here’s how to get started with deep learning for time series forecasting:
Below is a selection of some of the most popular tutorials.

Forecast Trends and Seasonality (univariate)0
Human Activity Recognition (multivariate classification)
Forecast Electricity Usage (multivariate, multi-step)
Models Types
Time Series Case Studies
Forecast Air Pollution (multivariate, multi-step)

5. Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks.

Here’s how to get started with deep learning for Generative Adversarial Networks:
  • Step 1: Discover the promise of GANs for generative modeling.
  • Step 2: Discover the GAN architecture and different GAN models.
  • Step 3: Discover how to develop GAN models in Python with Keras.
Below is a selection of some of the most popular tutorials.

GAN Fundamentals
GAN Loss Functions
Develop Simple GAN Models
GANs for Image Translation

Liked this article? Want to add anything? Tell us everything in the comment section.

If you like this article, please share it with your friends as well as enemies. And for more updates, stay tuned with us at LunaticAI

Image Credits: DG

Post a Comment

0 Comments