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Responsible Research In Practice

responsible research in practice

Horsham

Responsible Research in Practice was founded by Dr Nikki Osborne in 2015 to provide training & consultancy services to individuals & organisations working withing the Life Science research sector. The company is a UK Reproducibility Network external stakeholder affiliate member & a UK registered learning provider (UKRLP No. 10092337 [https://www.ukrlp.co.uk/ukrlp/ukrlp_provider.page_pls_searchProviders]). How can we help you? * FREE Responsible Research Webinars [https://www.responsibleresearchinpractice.co.uk/free-responsible-research-webinars/]. We run a monthly LIVE responsible research webinar series that is FREE to attend & fully open access on-demand. Topics vary but all relate to the practical 'how to' details of implementing best practice. All our professional & inspiring speakers are passionate about the topics they discuss & all attendees receive a certificate of attendance that can be used for CPD purposes.  * On-Demand Training [https://www.responsibleresearchinpractice.co.uk/on-demand-training/]. Our bitesize on-demand training sessions require just 1-2 hours of your time & are accessible 24 hours a day. Whether you are involved in the planning, design, conduct & dissemination of lab-based research, or training, mentoring, supervising & overseeing others - we have an on-demand training session for you. Our professional content is designed empower you to improve the rigour & reproducibility of your work. * Live training [https://www.responsibleresearchinpractice.co.uk/training-services/]. We provide training online and in-person (for client organisations) on a range of topics to support personal development. All our sessions focus on empowering individuals to be their best self by sharing experience of what works, developing critical thinking skills & facilitating self-reflection. To find out more visit our Mindset Awareness [https://www.responsibleresearchinpractice.co.uk/mindset-training/], PREPARE For Better Science [https://www.responsibleresearchinpractice.co.uk/prepare-for-better-science-training-course/], Publication School [https://www.responsibleresearchinpractice.co.uk/lab-animal-publication-school/], Responsible Animal Research [https://www.responsibleresearchinpractice.co.uk/responsible-animal-research-training/] & Statistical Analysis Training [https://www.responsibleresearchinpractice.co.uk/statistical-analysis-training/] pages. * Consultancy services. [https://www.responsibleresearchinpractice.co.uk/consultancy-services/] Training alone is not the solution to everything, so we offer a range of consultancy services including: coaching, independent advice, peer review, professional speaker & policy review/writing. Contact us anytime for a FREE 'no commitment' discussion & if we can help you we will, or we will recommend someone who can. Why work with us?  * We care. All our tutors are passionate about the topics they teach & have knowledge plus real life experience to share.  * We recognise that conducting responsible research is challenging. Our goal is to make doing the right thing simpler & easier to achieve.  * We empower individuals. Our training sessions are designed to inspire & support personal development so that participants feel more confident in their ability to do the right thing.

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Machine Learning Essentials with Python (TTML5506-P)

By Nexus Human

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Machine Learning Essentials with Python (TTML5506-P)
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