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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.
Explore ChatGPT, a cutting-edge world of AI content creation, with our comprehensive course. This meticulously curated program unravels the technology behind ChatGPT to practical applications in machine learning, social media, data analysis, and image generation. This course unveils new facets of AI to navigate this evolving landscape with prowess.
Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.
Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited
This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.
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Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client