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Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

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Project Accounting and Finance Skills: In-House Training Do you manage both project schedules and budgets, but do not have insight into how actual results relate to the approved budget? Do you desire to have more clarity about the relationship between your project's performance with the accounting and financial systems in your organization? Do you need to understand financial and accounting terminology to bridge the gap between the 'world of finance' and the 'world of project management? Organizations have a need to manage-by-projects, because projects are the means to deliver on strategic goals and objectives. Therefore, the project manager must have an understanding of the financial world of investments to ensure the organization will realize expected business value. This requires a foundation in the principles of accounting and finance to comprehend how the project's contribution provides an organization with a competitive advantage. Learn what you must do to give your organization the assurance it needs that its investment in your project will realize business value. Learn what you must do to give your organization the assurance it needs to know that its investment in your project will realize business value. What You Will Learn At the end of this program, you will be able to: Explain the aspects of classical corporate accounting and finance effects on managing projects Determine how your project fits into the corporate income statement, balance sheet, and cash flow statement Analyze the financial aspects of managing projects Use earned value management as the basis for decision making throughout the project life Recognize the importance of the project manager's financial responsibilities Focus on what PMs do and should be doing, in support of accounting and finance Use financial information within a project environment to meet financial results Track and analyze the project's financial status and forecast with the goal of realizing benefits Generate work performance data to ensure a project's outcome aligns with financial metrics Foundation Concepts Accounting and Finance Terms and Concepts Accounting and Finance Essentials Financial Terms and Concepts Projects as Financial Investments Overview of 'Two Worlds' Project as Investments Accounting and Finance World: Standards, Principles and Practices Accounting and Finance Standards and Principles Accounting and Finance Practices Capital Budgeting Corporate Budgeting Accounting and Finance World: Economic Project Selection Methods Economic Project Selection Methods Economic Project Selection and the Business Case Project Management World Project Management and Financial Controls Project Management and Work Performance Data Project Management and Earned Value Management Project Management and Work Performance Reporting

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