Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more. A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

First, you’ll see if you can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

You’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, you’ll improve on both of those by using a fully Bayesian approach. Why is the Bayesian method interesting to us in machine learning? It’s an entirely different way of thinking about probability. It’s a paradigm shift. You’ll probably need to come back to this course several times before it fully sinks in. It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum — it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, you’re using A/B testing as a concrete example of how Bayesian techniques can be applied. You’ll learn these fundamental tools of the Bayesian method — through the example of A/B testing — and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn.

In this course, you are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail. It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next you’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability. It’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next you’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One you’ve studied these algorithms, you’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification. You’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More.

You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify hand writings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more.

Inside the course, you’ll learn how to:

Set up a Python development environment correctly

Gain complete machine learning tool sets to tackle most real world problems

Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.

Combine multiple models with by bagging, boosting or stacking

Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data

Develop in Jupyter (IPython) notebook, Spyder and various IDE

Communicate visually and effectively with Matplotlib and Seaborn

Engineer new features to improve algorithm predictions

Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data

Use SVM for handwriting recognition, and classification problems in general

Use decision trees to predict staff attrition

Apply the association rule to retail shopping datasets

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python.

In this course you’ll study ways to combine models like decision trees and logistic regression to build models that can reach much higher ac-curacies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, you will learn about an important topic in statistical learning, the bias-variance trade-off. You will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

You’ll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, you will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

Leverage Machine Learning and TensorFlow in Python to improve your business! Build deep learning algorithms from scratch.

It starts with the very basics and covers everything you need to know. One hour into the course, you will have created your first machine learning algorithm! Isn’t that exciting! And it only gets better from there. We are not simply scratching the surface. The course digs deep into machine learning theory and practice, focusing on deep neural networks and Google’s state-of-the-art TensorFlow framework.

All sophisticated concepts are explained intuitively, with beautifully animated videos and our step-by-step approach, which makes this course an engaging and fun experience.

Here are some steps of this journey:

Cover the minimum to create your first algorithm

Get acquainted with Google’s TensorFlow with Python

Apply all you see with the appropriate TensorFlow structure

Explore layers, their building blocks, their activations (sigmoid, tanh, ReLu, softmax, …)

Understand the backpropagation process, intuitively and mathematically

Spot and prevent overfitting

Get to know the state-of-the-art initialization methods

Implement cutting-edge optimizations, such as SGD, batching, learning rate schedules

Tackle the ‘Hello, world’ of machine learning

All these steps will lead us to the practical example, which will require you to build your first machine learning algorithm based on a real-life business problem. You will tackle it on your own, completely from scratch.

Using 10 different projects, the course focuses on breaking down the important concepts, algorithms, and functions of Machine Learning. The course starts at the very beginning with the building blocks of Machine Learning and then progresses onto more complicated concepts. Each project adds to the complexity of the concepts covered in the project before it.

Project 1 — Stock Market Clustering Project

— Stock Market Clustering Project Project 2 — Breast Cancer Detection

— Breast Cancer Detection Project 3 — Board Game Review

— Board Game Review Project 4 — Credit Card Fraud Detection

— Credit Card Fraud Detection Project 5 — Diabetes Onset Detection

— Diabetes Onset Detection Project 6 — Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences

— Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences Project 7 — Getting Started with Natural Language Processing In Python

— Getting Started with Natural Language Processing In Python Project 8 — Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning

— Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning Project 9 — Image Super Resolution with the SRCNN

— Image Super Resolution with the SRCNN Project 10 — Natural Language Processing: Text Classification

— Natural Language Processing: Text Classification Project 11 — K-Means Clustering For Image Analysis

— K-Means Clustering For Image Analysis Project 12 — Data Compression & Visualization Using Principle Component Analysis

The course covers a variety of different machine learning concepts such as supervised learning, unsupervised learning, reinforced learning and even neural networks. But that’s not all. In addition to understanding the theory behind machine learning, you will then actually use these concepts and implement them into actual projects to see how they work in action!

The course also comes with quizzes at the end of each section to help solidify your understanding for the subject. It will also help you valuate your learning of the subject.

At the end of this interactive and hands-on course, you will have everything you need to actually get started with understanding machine learning algorithms and even start writing your own algorithms that you can use for your own projects.