Instructional Material

Link: The Complete Python 3 Course: Beginner to Advanced his course is designed to fully immerse you in the Python language, so it is great for both beginners and veteran programmers! This diploma in C and Python programming course is a great way to get started in programming. It covers the study of the C and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering.

Microsoft has launched a new 44-part series called Python for Beginners on YouTube, consisting of three- to four-minute lessons from two self-described geeks at Microsoft who love programming and teaching. The course isn't quite for total beginners as it assumes people have done a little programming in JavaScript or played around with the MIT-developed Scratch visual programming language aimed at kids. But it could help beginners kick-start ambitions to build machine-learning apps, web applications, or automate processes on a desktop. Microsoft has published a page on GitHub containing additional resources, including slides and code samples to help students become better at Python. The Python for Beginners series is presented by Christopher Harrison, a senior program manager at Microsoft, and Susan Ibach, a business development manager from Microsoft's AI Gaming unit.

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.

Do you want to add deep learning as your skill? We are with the best Deep Learning Tutorials for Beginners and Advanced, course, and certification. We are leaving in the era of machines. It is replacing the traditional ways of working. From a simple alarm clock to artificial intelligence, people are using machines in every sector of life. With the growth of using machines, the need to control and understand machines have grown. So, the skill of machine learning is in super demand. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. The internet can offer you an uncountable amount of courses on deep learning. We have searched and found the few best Deep Learning tutorial for beginners and advanced level. Here, are the best Deep Learning certification and training for you. Coursera is offering this special course for those who want to master Deep Learning and start a career in machine learning. This 100% online course will take 3 months to complete.

"Business is going to change more in the next five years than it has in the last twenty" Is this book for you? Are you looking to start up an automation Centre of Excellence (CoE) in your company to start building automation solutions, or perhaps you want your new CoE to mature and grow. Read industry best practices and insights, to get high-level steps on how to best implement Intelligent Automation. This will improve your awareness on what's been happening in the industry and what may be to come in the near future. This will help you understand the dos, don't, myths, challenges, and benefits of automating your business processes, and give you a picture of what your team is doing …or should be doing.

Below is a list of the topics I am planning to cover. Note that while these topics are numerated by lectures, note that some lectures are longer or shorter than others. Also, we may skip over certain topics in favor of others if time is a concern. While this section provides an overview of potential topics to be covered, the actual topics will be listed in the course calendar.

Make sure to collect chat transcripts after each chat so you can analyze them. This will help you identify any issues your customers are going through, and where your chatbot is falling behind. Identifying the bot's weak spots and optimizing it is the key to improving your bot. You can get a sense of customer satisfaction by seeing what percentage of overall chats are positive, neutral or negative. Depending on your chatbot's architecture, you should retrain the bot to learn how to overcome its weak spots.

Adaptive learning is an emerging technology that has been shown to increase student engagement and student learning. Adaptive learning systems are automated systems that use machine learning to provide questions to assess student knowledge, give immediate feedback on responses, and provide scaffolding to support learning. The Online Learning Consortium (OLC) is reaching out to our global community of thought leaders, faculty, innovators, and practitioners to bring you insights from the field of online, blended, and digital learning. This week, Dr. Deborah Taylor, OLC Institute SME and faculty for the Adaptive Learning Fundamentals and Courseware Exploration workshop, joins us to answer our questions about this new workshop. OLC: There are many opportunities to teach online.

Get a solid understanding of decision tree Understand the business scenarios where decision tree is applicable Tune a machine learning model's hyperparameters and evaluate its performance. Use Pandas DataFrames to manipulate data and make statistical computations. Use decision trees to make predictions Learn the advantage and disadvantages of the different algorithms Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right? You've found the right Decision Trees and tree based advanced techniques course! After completing this course you will be able to: Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.