Understand how Docker works and learn its core features with this hands-on course. This is 90% practice without any useless demos! Learn how to create different containers such as Node.js, Python, WordPress, MongoDB, MySQL, Redis, Elasticsearch, and much more.
Start your career in Data Science and earn up to £90,000 per month. Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course. Our mission is simple - to be your trusted partner every step of the way, from training to employment. In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Scientists, so we can help you get your foot in the door. If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist! If you have any questions, you can contact us. We will be happy to provide you with all the information you need. Why Choose Us? So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python: Personalised Guidance: We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python. One-On-One Consultation Sessions with Industry Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise. Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere. Our aim is to maximise your employment prospects and provide you with a range of exciting opportunities to choose from. Interview Preparation: No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress. Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee. If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us. Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance. Whether you need advice on career advancement, assistance with new projects, or simply a friendly ear to share your achievements, we'll be your trusted partner for long-term success. Here are the courses we will provide once you enrol in the program: Course 01: Business and Data Analytics for Beginners Course 02: Quick Data Science Approach from Scratch Course 03: Learn MySQL from Scratch for Data Science and Analytics Course 04: SQL for Data Science, Data Analytics and Data Visualization Course 05: Statistics & Probability for Data Science & Machine Learning Course 06: R Programming for Data Science Course 07: Python Data Science with Numpy, Pandas and Matplotlib Course 08: Complete Python Machine Learning & Data Science Fundamentals Course 09: 2021 Data Science & Machine Learning with R from A-Z Course 10: Python Programming from Scratch with My SQL Database Course 11: Level 2 Python Course Course 12: Machine Learning for Predictive Maps in Python and Leaflet Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI Course 14: Python for Spatial Analysis in ArcGIS Course 15: Ultimate Python Training for Beginners Course 16: PyScript Fundamentals Training These courses will help you to develop your knowledge and skills to become a successful Cyber Security Expert. The Data Scientist with Python Program is completed in 9 easy steps: Step 1: Enrol in the Programme Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied. After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing! Step 2: Initial One-On-One Counselling Session Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path. Step 3 - Certification upon Course Completion After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject. Step 4: CV Revamping Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. Step 5: Building Network and Submitting CV We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile. Step 6: Interview Preparation With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills. Step 7: Securing Job Interviews Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want. Step 8: Post-Interview Support Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job. Step 9: Celebrate Your New Job! Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role. We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us. CPD 100 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Beginners with an interest in Data Science and Python programming. Programmers and Software Engineers looking to transition into Data Science. Business Analysts and Project Managers aiming to leverage Data Science in their domains. Researchers and Academics who wish to utilise Python for complex data analysis. Requirements No experience required. Just enrol & start learning. Career path Data Scientist: £60,000 - £90,000 Machine Learning Engineer: £65,000 - £100,000 Data Analyst: £40,000 - £60,000 Business Intelligence Analyst: £45,000 - £70,000 Data Engineer: £50,000 - £80,000 Big Data Architect: £70,000 - £120,000 Certificates CPD Accredited e-Certificate Digital certificate - Included CPD Accredited Framed (Hardcopy) Certificate Hard copy certificate - Included Enrolment Letter Digital certificate - Included QLS Endorsed Hard Copy Certificate Hard copy certificate - Included Student ID Card Digital certificate - Included
This comprehensive course unlocks the boundless potential of LangChain, Pinecone, OpenAI, and LLAMA 2 LLM, guiding you from AI novice to expert. Dive into 15 different practical projects, from dynamic chatbots to data analysis tools, and cultivate a profound understanding of AI, empowering your journey into the future of language-based applications.
The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.
Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences, Arrays, Dictionaries and Sets About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 Python for Data Science: Hands-on Technical Overview (TTPS4873) 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.
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.
Overview This comprehensive course on Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? After successfully completing the course you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4. It is available to all students, of all academic backgrounds. Requirements Our Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 8 sections • 22 lectures • 04:20:00 total length •Introduction: 00:16:00 •Installing PostgreSQL and PostGIS: 00:08:00 •Creating the Application Database: 00:03:00 •Installing Django in a Python Virtual Environment: 00:08:00 •Installing the ATOM IDE: 00:09:00 •Creating the Django Base Project: 00:08:00 •Adding the Database Configuration to the settings.py File: 00:09:00 •Creating a Model in the models.py File: 00:07:00 •Extracting Data from the Web: 00:24:00 •Cleaning and Transforming the Data Part 1: 00:18:00 •Cleaning and Transforming the Data Part 2: 00:10:00 •Loading the Data into the Model: 00:12:00 •Adding the Web Map Tile Service Link in settings.py: 00:08:00 •Reading from the Model and Creating a GeoJSON Dataset: 00:12:00 •Adding Template Files (HTML): 00:10:00 •Adding a Layout and the Base Map: 00:25:00 •Plotting Circle Markers: 00:16:00 •Creating a Sliding Sidebar: 00:14:00 •Creating a Doughnut Chart: 00:19:00 •Creating a Multi-Bar Bar Chart: 00:12:00 •Creating a Multi-Bar Bar Chart: 00:12:00 •Resource: 00:00:00
Duration 5 Days 30 CPD hours This course is intended for This course is intended for new and experienced programmers that want to learn how to write and troubleshoot Python code. This is the Microsoft recommended course for preparing for the 98-381 test. Previous programming experience is not required but recommended. Overview By the end of this course, you will be able to: Create Operations using Data Types and Operators Create Control Flow Operations Create Input and Output Operations Write and Document code to solve a specified problem Troubleshoot Problems and Write Error Handling Operations Perform Operations Using Modules and Tools This five-day instructor-led course (three-day boot camp) is intended for students who want to learn how to write, debug and document Python code Module 1: Perform Operations Using Data Types and Operators Assign data types to variables Perform data and data type operations Perform Arithmetic, Comparison and Logical Operations Review Module 2: Control Flow with Decisions and Loops Construct and analyze code segments that use branching statements Construct and analyze code segments that perform iterations Review Module 3: Perform Input and Output Operations Create Python code segments that perform file input and output operations Create Python code segments that perform console input and output operations Review Module 4: Document and Structure Code Construct and analyze code segments Document code segments using comments and documentation strings Review Module 5: Perform Troubleshooting and Error Handling Analyze, Detect and Fix code segments that have errors Analyze and construct code segments that handle exceptions Review Module 6: Perform Operations Using Modules and Tools Use Built-In Modules to perform basic operations Use Built-In Modules to perform complex operations Review
Diploma in C++ and Python Programming is one of our best selling and most popular course. The Diploma in C++ and Python Programming is organized into 64 modules and includes everything you need to become successful in this profession. To make this course more accessible for you, we have designed it for both part-time and full-time students. You can study at your own pace or become an expert in just 17 hours! If you require support, our experienced tutors are always available to help you throughout the comprehensive syllabus of this course and answer all your queries through email. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Mock exams Multiple-choice assessment Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who is this course for? Diploma in C++ and Python Programming is suitable for anyone who want to gain extensive knowledge, potential experience, and professional skills in the related field.
Network automation training course description This course concentrates on the technical side of tools and languages for network DevOps rather than the soft skills. These tools include Python, Ansible, Git and NAPALM By the end of the course delegates should be able to recognise the tools that they can use to automate their networks and be able to use the knowledge gained to feel confident approaching network automation. What will you learn Describe network DevOps. Choose network automation tools. Explain the role of various network automation technologies including: Python Ansible Git NAPALM Network automation training course details Who will benefit: Those wishing to learn about the tools of network automation. Prerequisites: Introduction to data communications. Duration 1 day Network automation training course contents What is DevOps and network automation Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Unit testing. Hype vs reality. Benefits and features. Network monitoring and troubleshooting Traditional methods, SNMP. Netflow and xflow. Traditional automation. Streaming telemetry. Event driven automation. gRPC, Protocol buffers. Configuration management Catch 22 and initial configuration. ZTP, POAP. Traditional automation. TFTP. Ansible vs the rest (chef, salt, puppet). Jinja2 and templating. How ansible works. Network programmability Programming languages. Linux, shell scripting. Python vs the rest. Off box vs on box automation. Python network libraries Sockets pysnmp, ncclient, paramiko, netmiko, pyez, NAPALM. APIs Proprietary APIs, CLI, NETCONF, RETCONF. YANG, XML, YAML, JSON. Other tools Git, GitHub, Jenkins, JIRA and others.