568 Machine Learning (ML) courses

In today's rapidly evolving digital era, the fusion of finance and technology has paved the way for unprecedented opportunities. Enter the world of FinTech, Cryptocurrency, and the power of Data Analysis. With this 'Data Analytics (Data Analysis), FinTech and Cryptocurrency' bundle, you're taking the first step into a realm where Data Analysis isn't just a tool-it's the core of decision-making. Dive deep into the nuances of modern finance, learn the intricacies of Cryptocurrency, and harness the might of Data Analysis to make informed strategies. In the UK, professionals in these fields can enjoy impressive salary ranges, with earnings starting from £35,000 per year and reaching up to £80,000 per year, making it an enticing career choice. This bundle includes three courses that will equip you with the essential knowledge and skills to excel in this domain. This comprehensive Data Analysis bundle provides a valuable opportunity to explore the world of finance, technology, and data. By enrolling in these Data Analysis bundles, you will gain a deep understanding of the innovations shaping the financial industry, such as blockchain and artificial intelligence, and how they intersect with technology. Each Data Analytics (Data Analysis) course in FinTech and Cryptocurrency bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This Data Analysis bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Immerse yourself in these diverse, enthralling subjects, each designed to fuel your curiosity and enhance your knowledge. Dive in now! The courses in this Data Analysis bundle include: Course 1: FinTech Course 2: Cryptocurrency Course 3: Data Analytics Learning Outcomes: By completing this Data Analysis bundle, you will achieve the following learning outcomes: Understand the principles and applications of FinTech in the financial industry. Leverage Data Analysis for informed decision-making in finance and digital currencies. Use Data Analysis to forecast market trends in FinTech and Cryptocurrency. Apply statistical analysis techniques to interpret data effectively. Elevate financial proficiency by integrating insights from Data Analysis. Develop a strategic mindset for leveraging data analytics in FinTech and Cryptocurrency. The first course, FinTech, delves into the fascinating intersection of finance and technology. Gain a deep understanding of the technological innovations that are revolutionising the financial industry, including blockchain, artificial intelligence, and mobile banking. Explore the impact of digital currencies, peer-to-peer lending, and robo-advisors on traditional financial systems. The second course, Cryptocurrency, uncovers the secrets of this decentralised digital currency phenomenon. Discover the fundamentals of cryptocurrencies, such as Bitcoin and Ethereum, and explore the underlying blockchain technology. Dive into topics like mining, digital wallets, smart contracts, and the future of cryptocurrencies. Develop a solid foundation to navigate the complex world of digital assets. The third course, Data Analytics, equips you with the essential skills to extract insights from vast amounts of data. Learn the techniques and tools used to collect, clean, and analyze data, allowing you to make informed decisions and predictions. Dive into statistical analysis, data visualisation, and machine learning algorithms. Harness the power of data to drive business growth and enhance decision-making processes. CPD 15 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Analytics (Data Analysis) in FinTech and Cryptocurrency course is suitable for: Professionals aspiring to work in the FinTech or Cryptocurrency sectors. Financial analysts seeking to enhance their data analytics skills. Entrepreneurs who are interested in leveraging technology to innovate in the financial industry. Graduates looking to enter the finance or technology sectors with a competitive edge. Business professionals aiming to stay ahead of industry trends. Requirements You can delightfully enrol in this Data Analytics (Data Analysis) in FinTech and Cryptocurrency course without any formal requirements. Career path You can pursue various exciting career paths in FinTech and Cryptocurrency, including: Financial Data Analyst: £35,000 - £50,000 per year. Blockchain Developer: £45,000 - £75,000 per year. Cryptocurrency Investment Analyst: £50,000 - £80,000 per year. FinTech Consultant: £40,000 - £65,000 per year. Data Scientist (Financial Sector): £55,000 - £90,000 per year. Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - £9.99

This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course. The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.

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

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.

Python is a powerful and versatile programming language that's widely used in the world of data science and machine learning. In this Python for Beginners course, you will learn the fundamentals of Python programming, including data types, data structures, control flow, and more. By the end of the course, you will have a solid foundation in Python that will enable you to tackle more complex projects in the future. Learning outcomes: Understand the basic concepts of programming using Python Know how to install and set up a Python development environment Learn about data types and operators in Python Understand the various data structures available in Python Learn how to use control flow constructs in Python to make decisions and repeat actions Gain the ability to write simple Python programs from scratch Python for Beginners Part 1 is a comprehensive course designed for anyone who wants to learn the basics of Python programming. The course is structured into five modules, each focusing on a specific area of Python programming. You will start by learning about the basics of programming and setting up a Python development environment. From there, you will move on to topics such as data types, data structures, and control flow. Throughout the course, you will have access to interactive exercises and quizzes that will help you reinforce your learning. By the end of the course, you will have a solid understanding of Python programming and the ability to write your own simple programs. If you're new to programming or just starting out with Python, this course is the perfect place to begin. With clear, concise explanations and plenty of examples, you'll be up and running with Python in no time. Certification Upon completion of the course, learners can obtain a certificate as proof of their achievement. You can receive a £4.99 PDF Certificate sent via email, a £9.99 Printed Hardcopy Certificate for delivery in the UK, or a £19.99 Printed Hardcopy Certificate for international delivery. Each option depends on individual preferences and locations. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals who are new to programming Professionals who want to learn Python for data science or machine learning Students who want to gain a fundamental understanding of Python programming Anyone who wants to add a valuable skill to their resume Career path Python Developer: £30,000 - £75,000 per year Data Analyst: £24,000 - £46,000 per year Machine Learning Engineer: £35,000 - £85,000 per year Software Engineer: £24,000 - £70,000 per year Full Stack Developer: £28,000 - £70,000 per year Artificial Intelligence Developer: £35,000 - £85,000 per year

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.

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