With the paradigm shift in technology trending hard in the direction of machine learning and artificial intelligence, the skills of future-ready technologists, analysts, engineers and data managers also must shift, expand and advance. 

Machine Learning: Fundamentals and Algorithms, an online program offered by Limitless Learning, provides you with the technical knowledge and analytical methods that will prepare you for the next generation of innovation.

This 10-week online program is designed to provide software engineers, data analytics professionals and technical data managers with a skillset focused on fundamental machine learning methods. Participants who complete the program will be prepared to do the following:

• Synthesize components of machine learning to create functional tools for prediction of unseen data.

• Implement and analyze learning algorithms for classification, regression and clustering.

• Use concepts from probability, statistics, linear algebra, calculus and optimization to describe and refine the inner workings of machine learning algorithms.

Organized around 10 modules, this program helps participants broaden and deepen their Python programming skills for machine learning applications. This technical knowledge can be applied to any industry integrating machine learning and artificial intelligence into their digital drivers.

Module 1: Decision Trees

As you begin, you will learn to use a decision tree to make predictions and, given labeled training examples, you will learn a decision tree.

Module 2: K-Nearest Neighbor

In machine learning, there are fundamental algorithms. In this module, you will learn to use the k-NN algorithm to classify points given a simple dataset and implement a full decision tree for learning and prediction.

Module 3: Model Selection

Building your skills in Python, you will employ model selection techniques to select k for the k-NN algorithm and implement a grid search to select multiple hyperparameters for a model.

Module 4: Linear Regression

Creating machine learning solutions can require refinement of the inner workings of algorithms, including adapting the k-NN algorithm for classification to regression, adapting decision trees for classification to regression, as well as implementing learning for linear regression using gradient descent.

Module 5: Optimization

In this module, you will determine how convexity affects optimization and implement linear regression with optimization by stochastic gradient descent.

Module 6: Binary Logistic Regression

Given i.i.d. data and parameters of a logistic regression distribution, you will learn to compute conditional likelihood and learn to implement stochastic gradient descent for binary logistic regression.

Module 7: Regularization

As you discover ways to combat overfitting, you will convert a nonlinear dataset to a linear dataset in higher dimensions, manipulate the hyperparameters of L1 and L2 regularization implementations and identify the effects on magnitude and sparsity of parameters.

Module 8: Neural Networks

Combine simpler models as components to build up feed-forward neural network architectures and write mathematical expressions in scalar form defining a feed-forward neural network.

Module 9: Backward Propagation

Adding to your deep knowledge of algorithmic applications, you will learn to carry out the backpropagation algorithm on a simple computation graph over scalars and instantiate the backpropagation algorithm for a neural network.

Module 10: K-Means and Others Learning Paradigms

In addition to exploring solutions to practical challenges in this final module, you will learn to implement the k-means algorithm and recognize and explain challenges in selecting the number of clusters.

This program is designed for participants seeking a more sophisticated understanding of neural network architectures and the confidence to deploy their skills to solve real-world artificial intelligence problems. This program is most suitable for:

• Software Engineers

• Software Developers

• Data Scientists & Teams

• AI & ML Professionals

• Technology Professionals

Prerequisites: The subject matter in this program is rigorous. To ensure success, participants must have a strong working knowledge of linear algebra, calculus, statistics, probability, and object-oriented programming including Python.

“Limitless Learning Professional Education’s programs tackle all the technologies that are at the heart of the digital transformation, enabling dedicated professionals to meet new challenges in the Fourth Industrial Revolution and lead change within their organizations.

Dr. Mourad Bouache, AI Academy @Intel, Silicon Valley 

1. Your place will be reserved once payment is confirmed.

2. Program content will be available on the program’s start date.

3. Complete the form on the registration page.

4. You will then be able to access the virtual campus so you can begin to familiarize yourself with it.

5. You will begin your journey towards Limitless Learning