1339 Scientist courses

Python Basics: Course Description Excellent for beginners, practical, in small groups of max 4 people, 1 Day Online Instructor-led. You could contact us for your prefereed date. Session 1: Python Data Types and Variables: Primitive types; Characters & Strings; Boolean; Working with variables and its scope; Conversion and casting types in Python. Operators and Expressions: Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.. Exercise: Calculate Movie Tickets for a Party, Are there enough seats in the cinema? Decision Making & Loops If statement; If - else statement; If- elif - else statement; Nested if - else; Exercise: Calculate the travel fee to deliver goods The while, For loop Jump statements: break, continue; Nesting loops. Exercise: Enter a password, if incorrect 3 times, you are blocked. Session 2: Data Structures Lists. Tuples. Exercise: Hangman Game Exercise: Get a word for the game from a Json File, store the high score in a Dictionary file Session 3: Files and exceptions Exception Handling, Exception types; Using try and Except. Files, streams: Open, Traverse, Read and Create Files: Csv, txt and Json Files. API: Connecting to API’s. Session 4: OOP Creating and using custom Functions. Using parameters and return values. Creating a Class; Creating an Object; Using an Object; Adding Instance variables; Class Constructors; Parameterized Constructors. Inheritance. Override. Session 5: Pandas Dataframe Basics Getting data into a dataframe: Dict to Dataframe, Dataframe to Dict. Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets. Getting information about the dataframe, Filter, sort and query a Dataframes, Slicing Dataframes, Duplicate values,Working with null-values, Sampling. Exercise: Query the top 1000 grossing movies of the last century Session 6: Built in Functions: String, Math, Random Python built-in functions: Strings functions. Maths functions. Random Functions. Exercise: Find information in prose, to get the sentiment of the prose. Exercise: Get a word for the game from a txt File Exercise: Win the lottery Included: PCWorkshops's Python Programming Basics Certification Course notes, exercises and code examples Revision session after the course Refund Policy No Refunds

Level 5 QLS Endorsed Diploma | 150 CPD Points | +Gifts: QLS Certificate + PDF Certificate | 24/7 Learner Support

Executive AI Coaching: Boost Your Leadership with Generative AI Unlock the power of AI for your business with personalised executive coaching. Paul Addicott-Evans, founder of AECS, offers bespoke 1-on-1 sessions to transform leaders into AI champions. Enhance productivity, drive innovation, and stay ahead in the AI revolution. 20+ years of tech and business expertise. Tailored for C-suite, senior leaders, and entrepreneurs. Elevate your leadership—master generative AI today!

This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.

Do you want to build a simple, reliable, and error-free chatbot for your business? If yes, then this is the course for you! Learn to build a chatbot with Amazon Lex, a fully-controlled AI service with sophisticated natural language models to create, develop, test, and deploy chatbots (conversational interfaces) in applications.

Are you interested in learning and deploying applications at scale using Google Cloud platform? Do you lack hands-on exposure when it comes to deploying applications and seeing them in action? Then this course is for you. You will also learn microservices and event-driven architectures with real-world use case implementations.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Level 5 QLS Endorsed Diploma | 150 CPD Points | +Gifts: QLS Certificate + PDF Certificate | 24/7 Learner Support

NATIONALLY RECOGNISED AND ACCREDITED FORENSIC SCIENCE COURSE Level Three (advanced), awarding 3 credits.   DUAL ACCREDITATION: Awarding Body: Open College Network (OCN Credit4Learning) Awarding Body: CPD (21 CPD Points)   A modular "hybrid" forensic science course - eLearning (online) theory and one full day classroom based practical training (Crime Scene Investigations). The practical day covers a wide range of CSI techniques with "hands-on" practical training. On completion awards an OCN Nationally Recognised and Accredited Certificate in Forensic Science. This course is additionally CPD Accredited and also awards 21 CPD points on completion.   PART 1 - THEORY Complete this part of your course online (eLearning Course) in the comfort of your own home or workplace. Please allow approx. 6-8 hours to complete the elearning modules. You do not need to complete Part 1 in one single "sitting” and can log on/off as many times as you wish and when convenient to you. You must complete both parts 1 and 2 to successfully pass this course.   PART 2 - CLASSROOM Attend your forensic science practical day in the classroom, covering a number of key CSI investigative processes and procedures. You will develop your crime scene investigator skills with "hands-on" training in a classroom environment at the training location you have selected. Please note that we will provide protective clothing (disposable aprons), goggles and gloves. As you will be participating in a range of forensic activities we would suggest you wear easy clothing, short-sleeved top and closed-toe shoes.   You can complete Part 1 before or after comleting Part 2.   DOWNLOAD A COURSE ITINERARY HERE Course Itinerary M01 - Overview and Historical Background: A look at definitions, historical perspective highlighting major forensic advancements covering since early times, the beginning of modern forensics including the advent of fingerprinting, toxicology and DNA, and how DNA solved the first case (a double murder). Understanding the services of Forensic Labs and the major disciplines.   M02 - Observational Skills Crime Scene Investigation & Recording Examination of the crime scene, photography, videography, sketch recording principles, using a CAD package.   M03 - Forensic Pathology Understanding the job role, working within hospitals, mortuaries, the pathological processes and manner of death.   M04 - Forensic Anthropology How does forensic anthropology help forensic scientists? Using physical markers present on a skeleton to determine age, sex, stature, and race. Bone anatomy and stages of development from foetal to elderly individual. Differentiating male and female: skull, pelvis, femur and humerus.   M05 - Forensic Entomology How entomologists determine time of death as well as advanced investigations involving abuse and neglect. The life cycle of the blowfly and environmental influences. Using insect gut DNA to help solve crimes.   CS01 - Case Study - Forensic Entomology - The Jigsaw Murders   M06 - Forensic Serology Understanding presumptive tests and confirmatory tests. Tests in detail - processes and methods with options for: Blood, Saliva, Semen, Urine.   M07 - Using the Microscope Correct procedures for using the light microscope. A look at the electron microscope and scanning probe microscope and their applications in forensic science. Detailed process guide including mounting slides.

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms. Course Outline: Supervised Machine Learning: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting Reinforcement learning Algorithms: Q-Learning Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: Python Machine Learning Certificate on completion Python Machine Learning notes Practical Python Machine Learning exercises and code examples After the course, 1 free, online session for questions or revision Python Machine Learning. Max group size on this Python Machine Learning is 4. Refund Policy No Refunds