# Need Help Getting Started with Applied Machine Learning?

## How Do I Get Started?

The most common question I’m asked is: “how do I get started?

My best advice for getting started in machine learning is broken down into a 5-step process:

• Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
• Step 2: Pick a Process. Use a systemic process to work through problems.
• Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
• Step 4: Practice on Datasets. Select datasets to work on and practice the process.
• Step 5: Build a Portfolio. Gather results and demonstrate your skills.

For more on this top-down approach, see:

Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists.

## Applied Machine Learning Process

The benefit of machine learning are the predictions and the models that make predictions.

To have skill at applied machine learning means knowing how to consistently and reliably deliver high-quality predictions on problem after problem. You need to follow a systematic process.

Below is a 5-step process that you can follow to consistently achieve above average results on predictive modeling problems:

For a good summary of this process, see the posts:

## Probability for Machine Learning

Probability is the mathematics of quantifying and harnessing uncertainty. It is the bedrock of many fields of mathematics (like statistics) and is critical for applied machine learning.

Below is the 3 step process that you can use to get up-to-speed with probability for machine learning, fast.

• Step 1: Discover what Probability is.
• Step 2: Discover why Probability is so important for machine learning.
• Step 3: Dive into Probability topics.

You can see all of the tutorials on probability here. Below is a selection of some of the most popular tutorials.

## Statistics for Machine Learning

Statistical Methods an important foundation area of mathematics required for achieving a deeper understanding of the behavior of machine learning algorithms.

Below is the 3 step process that you can use to get up-to-speed with statistical methods for machine learning, fast.

• Step 1: Discover what Statistical Methods are.
• Step 2: Discover why Statistical Methods are important for machine learning.
• Step 3: Dive into the topics of Statistical Methods.

You can see all of the statistical methods posts here. Below is a selection of some of the most popular tutorials.

## Linear Algebra for Machine Learning

Linear algebra is an important foundation area of mathematics required for achieving a deeper understanding of machine learning algorithms.

Below is the 3 step process that you can use to get up-to-speed with linear algebra for machine learning, fast.

• Step 1: Discover what Linear Algebra is.
• Step 2: Discover why Linear Algebra is important for machine learning.
• Step 3: Dive into Linear Algebra topics.

You can see all linear algebra posts here. Below is a selection of some of the most popular tutorials.

## Optimization for Machine Learning

Optimization is the core of all machine learning algorithms. When we train a machine learning model, it is doing optimization with the given dataset.

You can get familiar with optimization for machine learning in 3 steps, fast.

• Step 1: Discover what Optimization is.
• Step 2: Discover the Optimization Algorithms.
• Step 3: Dive into Optimization Topics.

You can see all optimization posts here. Below is a selection of some of the most popular tutorials.

## Calculus for Machine Learning

Calculus is the hidden driver for the success of many machine learning algorithms. When we talk about the gradient descent optimization part of a machine learning algorithm, the gradient is found using calculus.

You can get familiar with calculus for machine learning in 3 steps.

• Step 1: Discover what Calculus is about.
• Step 2: Discover the rules of differentiation.
• Step 3: Dive into Calculus Topics.

You can see all calculus posts here. Below is a selection of some of the most popular tutorials.

## Python for Machine Learning

Python is the lingua franca of machine learning projects. Not only a lot of machine learning libraries are in Python, but also it is effective to help us finish our machine learning projects quick and neatly. Having good Python programming skills can let you get more done in shorter time!

You can get familiar with Python for machine learning in 3 steps.

• Step 1: Learn the language.
• Step 2: Learn how to work with the language.
• Step 3: Learn what you can do in Python ecosystem.

You can see all Python posts here. But don’t miss Python for Machine Learning (my book). Below is a selection of some of the most popular tutorials.

## 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.

You can see all machine learning algorithm posts here. Below is a selection of some of the most popular tutorials.

## 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:

• Step 1: Discover the features of the Weka platform.
• Step 2: Discover how to get around the Weka platform.
• Step 3: Discover how to deliver results with Weka.

You can see all Weka machine learning posts here. Below is a selection of some of the most popular tutorials.

## Python Machine Learning (scikit-learn)

Python is one of the fastest growing platforms for applied machine learning.

You can use the same tools like 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:

• Step 1: Discover Python for machine learning
• Step 2: Discover the ecosystem for Python machine learning.
• Step 3: Discover how to work through problems using machine learning in Python.

You can see all Python machine learning posts here. Below is a selection of some of the most popular tutorials.

## R Machine Learning (caret)

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.

You can see all R machine learning posts here. Below is a selection of some of the most popular tutorials.

## Code Algorithm from Scratch (Python)

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 is a learning tool only.
• Step 3: Discover how to code machine learning algorithms from scratch in Python.

You can see all of the Code Algorithms from Scratch posts here. Below is a selection of some of the most popular tutorials.

## Introduction to Time Series Forecasting (Python)

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.

You can see all Time Series Forecasting posts here. Below is a selection of some of the most popular tutorials.

## Data Preparation for Machine Learning (Python)

The performance of your predictive model is only as good as the data that you use to train it.

As such data preparation may the most important parts of your applied machine learning project.

Here’s how to get started with Data Preparation for machine learning:

• Step 1: Discover the importance of data preparation.
• Step 2: Discover data preparation techniques.
• Step 3: Discover how to get good at delivering results with data preparation.

You can see all Data Preparation tutorials here. Below is a selection of some of the most popular tutorials.

## XGBoost in Python (Stochastic Gradient Boosting)

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:

• Step 1: Discover the Gradient Boosting Algorithm.
• Step 2: Discover XGBoost.
• Step 3: Discover how to get good at delivering results with XGBoost.

You can see all XGBoosts posts here. Below is a selection of some of the most popular tutorials.

## Imbalanced Classification

Imbalanced classification refers to classification tasks where there are many more examples for one class than another class.

These types of problems often require the use of specialized performance metrics and learning algorithms as the standard metrics and methods are unreliable or fail completely.

Here’s how you can get started with Imbalanced Classification:

• Step 1: Discover the challenge of imbalanced classification
• Step 2: Discover the intuition for skewed class distributions.
• Step 3: Discover how to solve imbalanced classification problems.

You can see all Imbalanced Classification posts here. Below is a selection of some of the most popular tutorials.

## 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:

• Step 1: Discover what deep learning is all about.
• Step 2: Discover the best tools and libraries.
• Step 3: Discover how to work through problems and deliver results.

You can see all deep learning posts here. Below is a selection of some of the most popular tutorials.

## Deep Learning (PyTorch)

Besides Keras, PyTorch is another library for deep learning with a huge market-share. It is important to know about PyTorch and become familiar with its syntax.

Here’s how to get started with deep learning in PyTorch:

• Step 1: Discover what deep learning is all about.
• Step 2: Discover PyTorch
• Step 3: Discover how to work through problems and deliver results.

You can see all PyTorch deep learning posts here. Below is a selection of some of the most popular tutorials.

## 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:

• Step 1: Discover the challenge of deep learning.
• Step 2: Discover frameworks for diagnosing and improving model performance.
• Step 3: Discover techniques that you can use to improve performance.

You can see all better deep learning posts here. Below is a selection of some of the most popular tutorials.

## Ensemble Learning

Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member.

Here’s how to get started with getting better ensemble learning performance:

• Step 1: Discover ensemble learning.
• Step 2: Discover ensemble learning algorithms.
• Step 3: Discover techniques that you can use to improve performance.

You can see all ensemble learning posts here. Below is a selection of some of the most popular tutorials.

## Long Short-Term Memory Networks (LSTMs)

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

Here’s how to get started with LSTMs in Python:

• Step 1: Discover the promise of LSTMs.
• Step 2: Discover where LSTMs are useful.
• Step 3: Discover how to use LSTMs on your project.

You can see all LSTM posts here. Below is a selection of some of the most popular tutorials using LSTMs in Python with the Keras deep learning library.

## Deep Learning for Natural Language Processing (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:

• Step 1: Discover what deep learning for NLP is all about.
• Step 2: Discover standard datasets for NLP.
• Step 3: Discover how to work through problems and deliver results.

You can see all deep learning for NLP posts here. Below is a selection of some of the most popular tutorials.

## 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:

• Step 1: Discover what deep learning for Computer Vision is all about.
• Step 2: Discover standard tasks and datasets for Computer Vision.
• Step 3: Discover how to work through problems and deliver results.

You can see all deep learning for Computer Vision posts here. Below is a selection of some of the most popular tutorials.

## 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:

• Step 1: Discover the promise (and limitations) of deep learning for time series.
• Step 2: Discover how to develop robust baseline and defensible forecasting models.
• Step 3: Discover how to build deep learning models for time series forecasting.

You can see all deep learning for time series forecasting posts here. Below is a selection of some of the most popular tutorials.

#### Forecast Air Pollution (multivariate, multi-step)

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.

You can see all Generative Adversarial Network tutorials listed here. Below is a selection of some of the most popular tutorials.

## Attention and Transformers

Attention mechanisms are the techniques invented to mitigate the issue where recurrent neural networks failed to work well with long sequences of input. We learned that the attention mechanism itself can be used as a building block of neural networks and therefore we now have the transformer architecture.

Attention mechanisms and transformer models are shown to deliver amazing results, especially in natural language processing. There are examples of using transformer models in one way or another that make computers understand human language and perform tasks such as translation or summarizing a paragraph, in human-like quality.

Here’s how to get started to understand attention mechanisms and transformers:

• Step 1: Learn about what attention is and what it can do.
• Step 2: Discover how to use attention in a neural network model.
• Step 3: Learn how the transformer model is built from the attention mechanism.

You can see all Attention and Transformer tutorials listed here. Below is a selection of some of the most popular tutorials.