568 Machine Learning (ML) courses

Dive deep into the world of ChatGPT with our ChatGPT Masterclass. From basic functionalities to advanced applications across various domains, this course equips you with the knowledge to leverage ChatGPT effectively. Enhance your professional skills, academic pursuits, and personal projects by mastering ChatGPT today. Learning Outcomes Understand the mechanics behind ChatGPT’s responses. Create precise and effective prompts for ChatGPT. Utilize ChatGPT for creative and technical writing. Leverage ChatGPT for educational support and learning. Integrate ChatGPT with Microsoft Excel for data management. Employ ChatGPT to achieve professional excellence. Explore practical and innovative ChatGPT prompts. Implement ChatGPT strategies in social media marketing. Understand the capabilities of ChatGPT Plus and New Bing. Course Curriculum Module 01: Getting Started with ChatGPT Introduction to ChatGPT: Basics of how to interact with and utilize ChatGPT effectively. Module 02: Understanding ChatGPT How ChatGPT Works: Insights into the AI and machine learning principles powering ChatGPT. Module 03: Crafting Effective ChatGPT Prompts: A Guide Prompt Engineering: Techniques for developing precise prompts that generate desired outcomes. Module 04: Writing with ChatGPT Creative and Technical Writing: Using ChatGPT to assist with various writing tasks and projects. Module 05: ChatGPT for Students Educational Applications: How students can use ChatGPT for studying, research, and homework assistance. Module 06: ChatGPT for MS Excel Excel Integration: Harnessing ChatGPT for automating tasks and analyzing data in Microsoft Excel. Module 07: ChatGPT for Professional Excellence Career Development: Applying ChatGPT in professional settings for communication, problem-solving, and innovation. Module 08: Useful ChatGPT Prompts Practical Prompts: A collection of effective ChatGPT prompts for various uses. Module 09: Social Media Marketing with ChatGPT Marketing Strategies: Leveraging ChatGPT for content creation, customer engagement, and campaign management. Module 10: ChatGPT Plus and New Bing Advanced Features: Exploring the enhanced capabilities and applications of ChatGPT Plus and New Bing. Module 11: ChatGPT in Personal Life Everyday Uses: Practical ways to incorporate ChatGPT into daily personal tasks and activities. Module 12: The Future with ChatGPT Looking Ahead: Discussing potential future developments in ChatGPT technology and its implications for various sectors.

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Artificial Intelligence isn’t science fiction anymore — it’s shaping the way we search, shop, scroll, and sometimes even spill the tea. This course lays the groundwork for understanding how AI actually works — minus the jargon and dramatic movie scenes. You’ll explore essential concepts such as algorithms, data patterns, logic systems, and machine-based learning models, all presented in a format that makes sense even if your only prior experience with AI is arguing with your phone's voice assistant. Perfect for curious minds across industries, this foundational course covers the key principles that drive AI technologies, from basic neural networks to the role of big data in decision-making. Whether you're brushing up for academic reasons or looking to speak AI without sounding like a tech cliché, you’ll find this course insightful, neatly organised, and refreshingly down-to-earth. All content is delivered online, allowing you to study at your own pace — no awkward group projects or lab goggles required. Key Features CPD Accredited FREE PDF + Hardcopy certificate Fully online, interactive course Self-paced learning and laptop, tablet and smartphone-friendly 24/7 Learning Assistance Discounts on bulk purchases Course Curriculum Module 01 : Introduction to Artificial Intelligence Module 02 : Mathematics for AI Module 03 : Knowledge Representation in AI - Part 1 Module 04 : Knowledge Representation in AI - Part 2 Module 05 : Machine Learning - Part 1 Module 06 : Machine Learning - Part 2 Module 07 : Deep Learning Module 08 : Natural Language Processing Module 09 : Computer Vision Module 10 : Robotics Module 11 : Building AI Applications Learning Outcomes: Grasp the fundamentals of artificial intelligence and its applications. Develop a strong mathematical foundation for AI algorithms. Master knowledge representation techniques in AI. Explore the principles and applications of machine learning. Dive into the world of deep learning and its use in AI. Understand the core concepts of natural language processing and computer vision. Accreditation This course is CPD Quality Standards (CPD QS) accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Technology enthusiasts eager to delve into AI. Students pursuing a career in AI and machine learning. Professionals seeking to upskill in the AI domain. Engineers and programmers interested in AI development. Entrepreneurs exploring AI for business solutions. Anyone with a curiosity about the future of artificial intelligence. Graduates looking to enhance their tech-related knowledge. Innovators with an interest in robotics and AI applications. Career path AI Research Scientist Machine Learning Engineer Data Scientist Natural Language Processing Engineer Computer Vision Specialist Robotics Software Engineer Certificates Digital certificate Digital certificate - Included Once you've successfully completed your course, you will immediately be sent a FREE digital certificate. Hard copy certificate Hard copy certificate - Included Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK). For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, 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? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis 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 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

Building Data Science Products? Think Business First Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

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.

Dive into the transformative world of Artificial Intelligence through the course titled 'Foundations of Artificial Intelligence: Building Intelligent Systems.' This comprehensive curriculum sweeps across an array of subjects, from the rudimentary introduction to AI to the intricate nuances of building AI applications. Embrace a holistic understanding of core modules like Machine Learning, Natural Language Processing, and Robotics. The content, framed meticulously, beckons those inquisitive minds eager to craft, innovate, and change the world with AI's limitless possibilities. Deepen your conceptual clarity with two-part modules that delve into Knowledge Representation and Machine Learning, ensuring that learners grasp intricate details without feeling overwhelmed. With sections dedicated to Computer Vision and Deep Learning, individuals will find themselves proficiently navigating the vibrant ecosystems these technologies encompass. Finally, a spotlight on AI applications ensures that learners not only acquire theoretical wisdom but also grasp how AI integrates into real-world scenarios. By the culmination of this course, participants will stand at the forefront of AI innovations, armed with the acumen to shape a future where intelligent systems intertwine seamlessly with our daily lives. This foundation lays the groundwork for boundless exploration in the Artificial Intelligence realm Learning Outcomes Upon completion of this course, participants will be able to: Gain comprehensive insights into the fundamental principles of Artificial Intelligence. Understand the critical mathematical concepts underpinning AI technologies. Develop proficiency in various AI knowledge representation methods. Acquire a solid foundation in Machine Learning, Deep Learning, and Natural Language Processing techniques. Familiarise with the applications and integrations of AI in Robotics and Computer Vision. Why buy this Foundations of Artificial Intelligence: Building Intelligent Systems? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Foundations of Artificial Intelligence: Building Intelligent Systems there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Foundations of Artificial Intelligence: Building Intelligent Systems course for? This Foundations of Artificial Intelligence: Building Intelligent Systems does not require you to have any prior qualifications or experience. You can just enrol and start learning. Aspiring AI enthusiasts keen on building a robust foundation in the subject. Technologists aiming to pivot into AI-centric roles. Researchers eager to enhance their knowledge spectrum in intelligent systems. University students studying computer science or related disciplines, looking to supplement their academic pursuits. Entrepreneurs eyeing opportunities in AI-driven ventures. Prerequisites This Foundations of Artificial Intelligence: Building Intelligent Systems does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Foundations of Artificial Intelligence: Building Intelligent Systems was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path AI Research Scientist - Average Salary Range: £60,000 - £85,000 per annum Machine Learning Engineer - Average Salary Range: £55,000 - £80,000 per annum NLP Specialist - Average Salary Range: £50,000 - £75,000 per annum Computer Vision Engineer - Average Salary Range: £52,000 - £77,000 per annum Robotics Engineer - Average Salary Range: £48,000 - £73,000 per annum AI Application Developer - Average Salary Range: £54,000 - £79,000 per annum Course Curriculum Module 01: Introduction to Artificial Intelligence Introduction to Artificial Intelligence 00:21:00 Module 02: Mathematics for AI Mathematics for AI 00:17:00 Module 03: Knowledge Representation in AI - Part 1 Knowledge Representation in AI - Part 1 00:18:00 Module 04: Knowledge Representation in AI - Part 2 Knowledge Representation in AI - Part 2 00:16:00 Module 05: Machine Learning - Part 1 Machine Learning - Part 1 00:16:00 Module 06: Machine Learning - Part 2 Machine Learning - Part 2 00:15:00 Module 07: Deep Learning Deep Learning 00:16:00 Module 08: Natural Language Processing Natural Language Processing 00:22:00 Module 09: Computer Vision Computer Vision 00:14:00 Module 10: Robotics Robotics 00:18:00 Module 11: Building AI Applications Building AI Applications 00:24:00

Overview This comprehensive course on Building Big Data Pipelines with PySpark MongoDB and Bokeh will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Building Big Data Pipelines with PySpark MongoDB and Bokeh 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? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Building Big Data Pipelines with PySpark MongoDB and Bokeh. It is available to all students, of all academic backgrounds. Requirements Our Building Big Data Pipelines with PySpark MongoDB and Bokeh 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 7 sections • 25 lectures • 05:04:00 total length •Introduction: 00:10:00 •Python Installation: 00:03:00 •Installing Third Party Libraries: 00:03:00 •Installing Apache Spark: 00:12:00 •Installing Java (Optional): 00:05:00 •Testing Apache Spark Installation: 00:06:00 •Installing MongoDB: 00:04:00 •Installing NoSQL Booster for MongoDB: 00:07:00 •Integrating PySpark with Jupyter Notebook: 00:05:00 •Data Extraction: 00:19:00 •Data Transformation: 00:15:00 •Loading Data into MongoDB: 00:13:00 •Data Pre-processing: 00:19:00 •Building the Predictive Model: 00:12:00 •Creating the Prediction Dataset: 00:08:00 •Loading the Data Sources from MongoDB: 00:17:00 •Creating a Map Plot: 00:33:00 •Creating a Bar Chart: 00:09:00 •Creating a Magnitude Plot: 00:15:00 •Creating a Grid Plot: 00:09:00 •Installing Visual Studio Code: 00:05:00 •Creating the PySpark ETL Script: 00:24:00 •Creating the Machine Learning Script: 00:30:00 •Creating the Dashboard Server: 00:21:00 •Source Code and Notebook: 00:00:00

Overview This comprehensive course on Microsoft Azure Cloud Concepts will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Microsoft Azure Cloud Concepts 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? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Microsoft Azure Cloud Concepts. It is available to all students, of all academic backgrounds. Requirements Our Microsoft Azure Cloud Concepts 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 1 sections • 29 lectures • 03:33:00 total length •Unit 01: What will you learn and Cloud Concepts: 00:18:00 •Unit 02: Core Azure architectural components: 00:18:00 •Unit 03: LAB Create a Windows and Linux VM Computer: 00:10:00 •Unit 04: LAB Container creation: 00:04:00 •Unit 05: Storage with Azure: 00:04:00 •Unit 06: LAB Create a storage account: 00:07:00 •Unit 07: Network concepts: 00:03:00 •Unit 08: Lab Network Peering: 00:16:00 •Unit 09: Lab scale set: 00:11:00 •Unit 10: Marketspace and Serverless: 00:07:00 •Unit 11: Event HUB and Logic APPS: 00:07:00 •Unit 12: DevOps Overview: 00:04:00 •Unit 13: Azure Databases Overview: 00:04:00 •Unit 14: Lab SQL: 00:08:00 •Unit 15: What are AI and machine learning: 00:10:00 •Unit 16: Powershell and CLI: 00:09:00 •Unit 17: Azure Advisor: 00:05:00 •Unit 18: Review Core Azure: 00:04:00 •Unit 19: Azure security compliance and trust: 00:03:00 •Unit 20: Lab DDOS and NSGs: 00:07:00 •Unit 21: Authentication and Authorisation: 00:07:00 •Unit 22: Azure security centre: 00:06:00 •Unit 23: LAB Azure key vault and AIP overview: 00:06:00 •Unit 24: Azure Advanced Threat Protection (Azure ATP): 00:06:00 •Unit 25: Azure monitoring: 00:05:00 •Unit 26: Manage Azure Governance: 00:07:00 •Unit 27: Azure privacy and compliance: 00:04:00 •Unit 28: Summary: 00:03:00 •Unit 29: Azure Pricing and support: 00:10:00

Overview The demand for skilled cybersecurity professionals is soaring in today's digital landscape. The CompTIA CySA+ Cybersecurity Analyst (CS0-002) course is your gateway to a lucrative and rewarding career in this high-demand industry. This course delves deep into various aspects of cybersecurity, from threat analysis and vulnerability identification to incident response and digital forensics. It's designed to ensure you're ready to excel in the field. This course covers various topics, including threat intelligence, vulnerability identification, incident response, and forensics analysis.  With 60+ hours of engaging content, our expert instructors will equip you with the knowledge and skills required to excel in the CompTIA CySA+ certification exam and kickstart your career in cybersecurity. Enrol in the CompTIA CySA+ Cybersecurity Analyst (CS0-002) course today and secure your future in this high-demand industry! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this CompTIA CySA+ Cybersecurity Analyst (CS0-002). It is available to all students, of all academic backgrounds. Requirements Our CompTIA CySA+ Cybersecurity Analyst (CS0-002) 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 22 sections • 96 lectures • 11:35:00 total length •Introduction: 00:02:00 •All about the Exam: 00:08:00 •What's New on the CompTIA CySA+ Exam?: 00:05:00 •Meet the Instructors: 00:02:00 •Thinking like the Enemy: 00:09:00 •Tools of the Trade: 00:08:00 •Intelligence Sources and Confidence Levels: 00:08:00 •Threat Indicators and Actors: 00:08:00 •Threat Trends: 00:07:00 •Intelligence Cycle and ISACs: 00:06:00 •Attack Frameworks: 00:06:00 •Threat Research: 00:11:00 •Threat Modeling and Intelligence Sharing: 00:06:00 •Vulnerability Identification: 00:07:00 •Scanning Parameters and Criteria: 00:09:00 •Scanning Special Considerations: 00:06:00 •Validation: 00:03:00 •Remediation and Mitigation: 00:08:00 •Inhibitors to Remediation: 00:07:00 •Web Applications Scanners, Part 1: 00:10:00 •Web Applications Scanners, Part 2: 00:05:00 •Scanning: 00:06:00 •Configuring and Executing Scans: 00:08:00 •Vulnerability Scanning: 00:10:00 •Reverse Engineering: 00:08:00 •Enumeration: 00:06:00 •Wireless Assessment Tools: 00:08:00 •Cloud Assessment Tools: 00:04:00 •Mobile and IoT: 00:10:00 •Embedded and Firmware Systems (RTOS, SoC, and FPGA): 00:09:00 •Access and Vehicles Risk: 00:08:00 •Automation and Control Risk: 00:10:00 •Cloud Models: 00:07:00 •Remote Service Invocation (FaaS, IaC, API): 00:10:00 •Cloud Vulnerabilities: 00:06:00 •Injection and Overflow Attacks: 00:09:00 •Injection and Overflow Attacks: 00:09:00 •Exploits: 00:08:00 •Application Vulnerabilities, Part 1: 00:08:00 •Application Vulnerabilities, Part 2: 00:07:00 •Network Architecture and Asset Management: 00:09:00 •Protecting Your Territory: 00:05:00 •Identity and Access Management: 00:11:00 •Encryption and Active Defense: 00:08:00 •Platforms: 00:07:00 •SOA and DevSecOps: 00:09:00 •Secure Software Development: 00:08:00 •Best Coding Practices: 00:04:00 •Trusted Hardware: 00:10:00 •Hardware Encryption: 00:04:00 •Hardware Security: 00:08:00 •Data Analytics: 00:10:00 •Endpoint Security: 00:08:00 •Recon Results, Part 1: 00:13:00 •Recon Results, Part 2: 00:05:00 •Impact Analysis: 00:05:00 •Collective Tools: 00:09:00 •Query Writing: 00:07:00 •E-mail Analysis, Part 1: 00:10:00 •E-mail Analysis, Part 2: 00:08:00 •Permissions: 00:09:00 •Firewalls: 00:08:00 •Intrusion Prevention Rules: 00:05:00 •DLP and Endpoint Detection: 00:05:00 •Threat Hunting and the Hypothesis: 00:06:00 •Threat Hunting Process: 00:07:00 •Results and Benefits: 00:05:00 •Workflow and Scripting: 00:07:00 •API and Malware Signature Creation: 00:08:00 •Threat Feeds and Machine Learning: 00:06:00 •Protocols, Standards, and Software Engineering: 00:05:00 •IR Roles and Responsibilities: 00:08:00 •IR Active Preparation: 00:10:00 •Incident Response Process: 00:07:00 •Network Symptoms: 00:04:00 •Host Symptoms: 00:08:00 •Application Symptoms: 00:04:00 •Digital Forensics: 00:10:00 •Seizure and Acquisitions: 00:05:00 •Forensics Acquisition Tools: 00:09:00 •Mobile, Virtualization, and Cloud: 00:06:00 •Forensics Analysis, Part 1: 00:04:00 •Forensics Analysis, Part 2: 00:08:00 •Packet Capture: 00:12:00 •Data Privacy and Security: 00:06:00 •Nontechnical Controls: 00:09:00 •Technical Controls: 00:08:00 •Business Impact Analysis: 00:05:00 •Risk Identification: 00:05:00 •Risk Calculation and Communication: 00:06:00 •Training: 00:04:00 •Supply Chain Assessment: 00:04:00 •Frameworks: 00:13:00 •Policies and Procedures: 00:05:00 •Controls and Procedures: 00:08:00 •Verification: 00:06:00