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Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

An intermediate-level course that will help you improve your Power BI skills and become an expert data analyst or data scientist. The course is carefully structured to provide an in-depth understanding of Microsoft Power BI and its features, along with some important tips and tricks.

This course empowers you to create interactive web applications using Shiny for Python. From fundamental concepts to advanced techniques, you will master web development with Python as your toolkit. Develop dynamic projects, learn diverse deployment methods, and embark on a journey to become a skilled Python web developer.

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

If you are someone with a background in Python programming and is interested in presenting your analysis in interactive web-based dashboards, then you are in the right place. This course primarily focuses on Dash, along with other key data science libraries, including Pandas and Plotly. Learn to use Dash and Plotly in Python which can help you to visualize your critical insights and KPIs in web apps that are easily sharable.

This comprehensive training program covers many concepts in Microsoft Power BI. From beginner to advanced levels, learn data visualization, advanced DAX expression, Python integration, custom visuals, data preparation, and collaboration in Power BI service. Develop expertise in Power BI and position yourself for a successful career in data analytics.

This extensive course for beginners provides the basics of chatbots with machine learning, deep learning, AWS, and its applications, building it from scratch with hands-on practice for chatbot development. This course will help you learn basic to advanced mechanisms of developing chatbots using machine learning, deep learning, and AWS with Python.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

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.

The easy way to store your data and retrieve it when you need it! Database training is vital for success if you want to work in the technology business. Companies are looking for experts who can use databases effectively and have a solid understanding of them. Our course can assist you in developing the skills and information required to flourish in the computer business. This well-designed course can teach you the fundamentals of database design, including creating and managing databases, manipulating data, and developing data-driven applications. You'll gain knowledge of several database types, including relational, object-oriented, distributed, and NoSQL. Along with measures to protect data security, you will learn about database performance and optimisation. Our course is a fantastic method to get started in the profession or obtain expertise for a present position. Many businesses are looking for candidates with good database knowledge and effective usage skills. You may differentiate yourself from the competition by enrolling in a course and gaining the skills and knowledge you need. Along with this Database Course, you will get 11 premium courses, an original hardcopy, 11 PDF Certificates (Main Course + Additional Courses) Student ID card as gifts. So, don't wait up. Enrol now! This Database Bundle Consists of the following Premium courses: Courses are included in this Database Bundle? Course 01: Microsoft SQL Server Development for Everyone Course 02: SQL Programming Masterclass Course 03: SQL NoSQL Big Data and Hadoop Course 04: Python Programming for Everybody Course 05: Data Science with Python Course 06: JavaScript Foundations for Everyone Course 07: C# Programming - Beginner to Advanced Course 08: R Programming for Data Science Course 09: Three.js & WebGL 3D Programming Course for Beginners Course 10: Basic Google Data Studio Course 11: Data Analytics with Tableau Learning Outcomes Have a fundamental understanding of database design, implementation, and upkeep. Be able to query databases and learn the SQL language. To preserve data, use security procedures and backup procedures. Examine database performance and note any enhancements. Be familiar with approaches for database normalisation and optimisation. Create web services and database applications. Use DBMS software for managing and storing data. Identify technologies and trends in databases. These 11 courses cover programming and data science topics such as SQL, Python, JavaScript, C#, R, Three.js, WebGL, Google Data Studio, and Tableau. They teach skills in database management, data analysis, manipulation, visualization, and reporting for beginners to advanced learners. The bundle incorporates skills to shed some light on your way and boost your career. Hence, you can strengthen your Database Expertise and essential knowledge, which will assist you in reaching your goal. Certificate: PDF Certificate: Free (Previously it was £6*11 = £66) Hard Copy Certificate: Free (For The Title Course: Previously it was £10) CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals interested in learning programming and data science skills Professionals seeking to enhance their knowledge and skill set Entrepreneurs or business owners wanting to understand and manage data effectively Students or graduates in computer science, data science, or related fields Anyone looking to start a career in programming or data science Data analysts, data scientists, or data engineers looking to expand their skill set Requirements There is no time limit for completing this course; it can be studied in your own time at your own pace. Career path People tend to pursue their careers in the following professions in the database sector Database Administrator Database Developer Database Architect Database Analyst Database Designer The salary range for a Database Professional in the UK is typically between £25,000 and £50,000 per annum. Certificates Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Microsoft SQL Server Development for Everyone) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost. Certificate of completion Digital certificate - Included