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 R, right? You've found the right Decision Trees and tree based advanced techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.

You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right? You've found the right Neural Networks course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.

You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right? You've found the right Linear Regression course! A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

We achieved an F1 score (harmonic mean of precision and recall) of 0.68, which is quite decent considering the limited size of our dataset. My role was related to hyperparameter optimization for the LRCN model, wherein I experimented with different values of the learning rate, dropout, and regularization techniques and how they impacted the results of our model. One important take from the entire experience was how teamwork is crucial to produce an efficient output. The internship was rigorous, with early morning lectures and late night team meetings, but I learned a lot and had fun in the process!

The rapid rise of digitalization and technological innovation is transforming almost every industry domain, including healthcare, banking, & finance. Regarding the revolutionary impact of AI integration upon industrial growth, education is no exception. The rapid incorporation of state-of-the-art technologies like artificial intelligence into education is changing how we learn and teach. AI for education is being used by educational platforms worldwide to create highly collaborative and interactive learning environments for students. AI has many key benefits for education, including personalized learning, digital lessons that are highly immersive, 24/7 access, instant feedback, and improved engagement for students with disabilities. Emergen Research estimates that the global market for artificial intelligence in the education sector accounted for USD 1.55 billion in 2020.

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Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains, and explores their connection to graphs and random walks. We utilize tools from linear algebra and graph theory to describe the transition matrices of different types of Markov chains, with a particular focus on exploring properties of the eigenvalues and eigenvectors corresponding to these matrices. The results presented are relevant to a number of methods in machine learning and data mining, which we describe at various stages. Rather than being a novel academic study in its own right, this text presents a collection of known results, together with some new concepts. Moreover, the tutorial focuses on offering intuition to readers rather than formal understanding, and only assumes basic exposure to concepts from linear algebra and probability theory. It is therefore accessible to students and researchers from a wide variety of disciplines.

A maximum of 50 participants are onsite at the Digital Futures hub. The deadline for registration is 10 September! If you cannot participate on-site, you are welcome to join us via Zoom. Zoom-link will be sent upon registration. The main objective of this PhD course, by Dr Pei Wang, Dr Patrick Hammer and Dr Robert Johansson, is to introduce the audience to Artificial General Intelligence.

One type of problem absolutely dominates machine learning and artificial intelligence: classification. Binary classification, the predominant method, sorts data into one of two categories: purchase or not, fraud or not, ill or not, etc. Machine learning and AI-based solutions need accurate, well-chosen algorithms in order to perform classification correctly. This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy (or strategies) for their projects. Instructor Keith McCormick draws on techniques from both traditional statistics and modern machine learning, revealing their strengths and weaknesses. Keith explains how to define your classification strategy, making it clear that the right choice is often a combination of approaches.

Certified AI & ML BlackBelt Plus Program is the best data science course online to become a globally recognized data scientist. BlackBelt Plus Program includes 105+ detailed (1:1) mentorship sessions, 36 + assignments, 50+ projects, learning 17 Data Science tools including Python, Pytorch, Tableau, Scikit Learn, Power BI, Numpy, Spark, Dask, Feature Tools, Keras,Matplotlib, Rasa, Pandas, ML Box, Scikits-Image, Amazon SageMaker, Streamlit, AWS, Flask, and other technologies such as Computer Vision, Natural Language Processing, Machine Learning, Artificial Intelligence and Deep Learning.