Name: Demand Side Management - Integration of New Technologies, Regulatory Changes & Renewable Energy Resources
Price: 1112 GBP
Availability: InStock

<p>Are you looking for a career in Data Science &amp; Python? Do you want to learn the skills you need to succeed in this exciting Data Science &amp; Python field? If so, then the Data Science &amp; Python - Career Mentoring &amp; Support with Job Opportunity program is perfect for you! Enrol Data Science &amp; Python today and earn upto •90,000 with the help of our guidance.</p><p>We will help you until you find a job, so you won't have to worry about a thing. Just gain knowledge and learn Data Science &amp; Python skills, getting you a job is our duty. You will receive career mentoring and support from our experienced team of professionals. We will help you develop your resume, network with potential employers, and land your dream job!</p><p>We are committed to being with you every step of the way, from your job search to your ultimate success.</p><p><strong>Why Choose Us</strong></p><p>Our mission is simple: to be your trusted partner and assist you every step of the way until you land the job of your dreams.</p><p>Here's what makes our Data Science &amp; Python Program stand out from the crowd:</p><ul><li><strong>Personalised Job Search Assistance:</strong> We're in this together! Our dedicated team will work tirelessly to support your entire job searching process. From crafting a standout resume to submitting it to top companies, we'll ensure you have a compelling application that gets noticed.</li></ul><ul><li><strong>Expertly Crafted CV:</strong> Your resume is your ticket to the interview room. Our professional resume writers will create a customised CV highlighting your unique skills and experiences. This will maximise your chances of standing out among the competition.</li></ul><ul><li><strong>Strategic Placement:</strong> 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.</li></ul><ul><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science &amp; Python field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and finesse.</li></ul><ul><li><strong>Comprehensive Skill Development:</strong> Our program is designed to equip you with the most sought-after skills in Data Science &amp; Python. From mastering cutting-edge tools to honing your skills, we'll ensure you have the knowledge and expertise to excel in any Data Science &amp; Python environment.</li></ul><ul><li><strong>Ongoing Support:</strong> We understand that the journey to landing your dream job doesn't end with placement. That's why our commitment to your success extends well beyond your initial training. Our support team will be available to answer your questions, provide guidance, and assist you as you progress in your career.</li></ul><p><strong>Here are the 25 courses we will provide once you enrol in the program:</strong></p><ul><li>Python Basic Programming for Absolute Beginners</li><li>Intermediate Python Coding</li><li>Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>SQL for Data Science, Data Analytics and Data Visualization</li><li>Learn MySQL from Scratch for Data Science and Analytics</li><li>Computer Science with Python Course</li><li>Machine Learning Course with Python</li><li>Python 3 Programming</li><li>Diploma in Python Programming</li><li>Python Data Science with Numpy, Pandas and Matplotlib</li><li>Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3</li><li>Advanced Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning at QLS Level 7</li><li>Learn Python, JavaScript, and Microsoft SQL for Data science</li><li>Quick Data Science Approach from Scratch</li><li>Data Science &amp; Machine Learning with R from A-Z</li><li>R Programming for Data Science</li><li>Clinical Data Management with SAS Programming</li><li>Information Governance and Data Management Training</li><li>Electronic Document Management: Efficient Data Handling</li><li>Cyber Security Awareness Training</li><li>AWS Certified Solutions Architect Associate Preparation</li><li>CompTIA Cloud+ (CV0-002)</li><li>IT Administration and Networking</li><li>CompTIA Network+ Certification (N10-007)</li><li>CompTIA PenTest+ (Ethical Hacking) course</li></ul><p>These courses will help you to develop your knowledge and skills to become a successful Data Science &amp; Python Expert.</p> <p>The Program is completed in 5 easy steps:</p><p><strong>Step 1 - Enrol in the program and start learning from the courses we provide</strong></p><p>After you enrol in the Data Science &amp; Python Program, you will get lifetime access to 25 premium courses related to Data Science &amp; Python. These courses will teach you the knowledge and skills required to become a successful Data Science &amp; Python Expert. Our customer service team will help you and keep in contact with you every step of the way.</p><p><strong>Step 2 - Complete your courses and get certifications</strong></p><p>After learning from the Data Science &amp; Python 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 for the first course and 80% for the rest relevant courses. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 3 - Get a consultation session from a professional</strong></p><p>Take your Data Science &amp; Python skills to new heights with a one-on-one consultation session led by a seasoned professional. Gain invaluable insights, expert tips, and tailored advice that will propel your career forward.</p><p><strong>Step 4 - Complete the CV and attend the interview</strong></p><p>Once you've successfully obtained the certifications, our team of professionals will build you a compelling CV and LinkedIn profile. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. Kickstart your Data Science &amp; Python career with a starting salary ranging from •25,000 to •40,000 annually. Step into the industry with the assurance of a promising future.</p><p><strong>Step 5 - We will not leave you until you find a job</strong></p><p>Our commitment to your success goes above and beyond. We won't stop until you land that dream job. With personalised support, expert guidance, and unwavering dedication, we'll be by your side until you secure the perfect opportunity. Your job search becomes our mission, ensuring you have the best chance at a successful career in Data Science &amp; Python</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Science &amp; Python program is ideal for:</p><ul><li>Recent Data Science &amp; Python graduates seeking practical guidance and support in their career journey.</li><li>Professionals looking to transition into the Data Science &amp; Python field and in need of targeted mentoring and job placement assistance.</li><li>Individuals who want to enhance their Data Science &amp; Python skills and improve their job prospects in the industry.</li><li>Career changers who aspire to become Data Science &amp; Python and require comprehensive training and guidance.</li><li>Anyone who wants personalised support in building a strong CV and navigating the competitive job market in the Data Science &amp; Python sector.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After successfully completing this Data Science &amp; Python program, you can explore these career pathways:</p><ul><li>Data Analyst: •25,000 - •55,000 per year</li><li>Data Scientist: •40,000 - •85,000 per year</li><li>Machine Learning Engineer: •50,000 - •100,000 per year</li><li>Data Engineer: •45,000 - •80,000 per year</li><li>Business Intelligence Analyst: •35,000 - •65,000 per year</li><li>Data Science Manager: •70,000 - •120,000 per year.</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter </h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>The Quality Licence Scheme Endorsed Certificate of Achievement</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Studyhub UK

Data Engineering

Big Data Analytics

Data Science

Data Skills

Data Analyst

Data Scientist with Python - IT Job Ready Program + Career Support & Money Back Guarantee

General Admission

<p>The world runs on zeros and ones, and Python is the key to unlocking their power. Whether you're a seasoned pro or just starting out, our Python Programming Bundle has everything you need to take your tech skills to the next level. With three QLS-endorsed courses and five CPD-QS accredited courses, this bundle is the ultimate resource for anyone looking to master Python programming and machine learning.</p><p>In the world of Python, there are no limits to what you can achieve. Our QLS-endorsed courses in Python Programming for Non Programmers Level 5, and Python Programming Bible | Networking, GUI, Email, XML, CGI cover everything from the basics of Python syntax to advanced topics like networking and web development. And upon completion of each course, you'll receive a hardcopy certificate endorsed by QLS, recognised globally as a mark of excellence in education and training.</p><p>But that's not all - our bundle also includes five CPD-QS accredited courses covering topics like Azure Machine Learning, Complete Python Machine Learning &amp; Data Science Fundamentals, and 2021 Data Science &amp; Machine Learning with R from A-Z. These courses are designed to give you the competitive edge you need to succeed in the fast-paced world of tech.</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Advanced Diploma in Python Programming: Beginner To Expert at QLS Level 7</li><li><strong>Course 02:</strong> Diploma in Python Programming at QLS Level 5</li><li><strong>Course 03:</strong> Diploma in Python Programming Bible at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Diploma in Python Programming</li><li><strong>Course 05:</strong> Azure Machine Learning</li><li><strong>Course 06:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 08:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Understanding of Python syntax and programming concepts</li><li>Proficiency in Python data structures and algorithms</li><li>Knowledge of Python libraries and frameworks</li><li>Familiarity with networking and web development using Python</li><li>Understanding of machine learning and data science concepts</li><li>Ability to build Python-based applications and solutions</li><li>Effective problem-solving and debugging skills</li></ul><p>Our Python Programming Bundle is the ultimate resource for anyone looking to master the Python programming language and take their tech career to the next level. In the Python Programming course, you'll learn the basics of Python syntax and programming concepts, including data types, variables, and functions. The Python Programming for Non Programmers Level 5 course covers advanced topics like object-oriented programming and database integration. And in the Python Programming Bible | Networking, GUI, Email, XML, CGI course, you'll learn how to build networking applications, create GUIs, and work with email, XML, and CGI.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>IT professionals looking to enhance their Python programming skills</li><li>Students interested in pursuing a career in data science or machine learning</li><li>Entrepreneurs looking to build tech products and solutions</li><li>Anyone interested in learning more about Python and its applications in the tech industry</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Python Developer: £30,000 - £70,000 per year</li><li>Data Analyst: £22,000 - £50,000 per year</li><li>Machine Learning Engineer: £35,000 - £100,000 per year</li><li>Full-Stack Developer: £25,000 - £75,000 per year</li><li>Software Engineer: £30,000 - £80,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

One Education

Python Programming Bundle (QLS) Course

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p><span>This 5 half-day Virtual Instructor Led Training (VILT) course will assist energy professionals in the planning and operation of a power system from renewable energy sources. The VILT course will discuss key operating requirements for an integrated, reliable and stable power system.</span></p>
<p><span>The unique characteristics of renewable energy are discussed from a local, consumer centric and system perspective, bringing to life the ever-changing paradigm in delivering energy to customers. The course will explore the technical challenges associated with interconnecting and integrating hundreds of gigawatts of solar power onto the electricity grid in a safe and reliable way.</span></p>
<p><span>With references to international case studies, the VILT course will also demonstrate the state of the art methodologies used in forecasting solar power. The flexibility of the invertor-based resources will facilitate higher penetrations of photovoltaic, battery electricity storage systems and demand response while co-optimizing customer resources. The contribution of inverter-based generators that provides voltage support, frequency response and regulation (droop response), reactive power and power quality with a high level of accuracy and fast response will be addressed.</span></p>
<p><span>Furthermore, this VILT course will also describe how microgrids' controllers can allow for a fully automated energy management. Distributed energy resources are analyzed in detail from a technical and financial aspect and will address the best known cost based methodologies such as project financing and cost recovery.</span></p>
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</section>
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Training Objectives 

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<p><span>Upon completion of this VILT course, participants will be able to:</span></p>
<ul>
<li><span>Learn about renewable energy resources, their applications and methods of analysis of renewable energy issues.</span></li>
<li><span>Review the operational flexibility of renewable energy at grid level, distribution network and grid edge devices.</span></li>
<li><span>Understand and analyze energy performance from main renewable energy systems.</span></li>
<li><span>Get equipped on the insights into forecasting models for solar energy.</span></li>
<li><span>Predict solar generation from weather forecasts using machine learning.</span></li>
<li><span>Explore operational aspects of a complex power system with variability from both the supply &amp; demand sides.</span></li>
<li><span>Manage the impact of the design of a Power Purchase Agreement (PPA) on the power system operation.</span></li>
</ul>
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Target Audience 

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<p><span>Engineers, planners and operations professionals from the following organizations:</span></p>
<ul>
<li><span>Energy aggregators who would like to understand the system operations of renewable energy power plants</span></li>
<li><span>Renewable energy power system operator</span></li>
<li><span>Energy regulatory agencies who aim to derive strategies and plans based on the feedback obtained from the power system operations</span></li>
</ul>
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Course Level 

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<ul>
<li>Basic or Foundation</li>
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Training Methods 

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<p><span>The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, including time for lectures, discussion, quizzes and short classroom exercises.</span></p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
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Trainer

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<p><strong>Your first expert course leader</strong> is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5.</p>
<p>With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana.</p>
<p><strong>Your second expert course leader </strong>is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch.</p>
<p>He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto.</p>
<p>Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin.</p>
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POST TRAINING COACHING SUPPORT (OPTIONAL)

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<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
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Accreditions And Affliations

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</section>

EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Large Scale Solar & Energy Storage - System Operations

Team of 10, pp

Normal Fee, pp

<p><strong>***24 Hour Limited Time Flash Sale*** <br /><br /> Data Analysis, Statistics &amp; Economics - QLS Endorsed Admission Gifts  <br /><br /> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Immerse yourself in the realm of Data Analysis, Statistics &amp; Economics with our eight-course bundle, meticulously curated to offer a robust foundation in these critical fields. Our comprehensive package includes three prestigious QLS-endorsed courses in Statistics, Economics, and Basic Data Analysis, ensuring a strong foundational understanding. Plus, we supplement this knowledge with five relevant CPD QS accredited courses encompassing cutting-edge domains like Google Data Studio, Azure Machine Learning, AWS SageMaker Canvas, and more.</p><p>No matter your current knowledge level, this course bundle will deepen your understanding of Data Analysis, Statistics &amp; Economics and elevate your theoretical knowledge. Your achievement will be acknowledged with a hardcopy certificate for each QLS-endorsed course, a tangible testament to your dedication and hard work.</p><p>In this digital age, skills in Data Analysis, Statistics &amp; Economics are more than just desirable; they're indispensable. Equip yourself with the theoretical knowledge needed to navigate the complexities of these domains confidently. Whether you're looking to enhance your current skillset, seeking a career change, or just curious, this bundle is the perfect choice. Dive into the world of Data Analysis, Statistics &amp; Economics today, and prepare to discover a wealth of knowledge.</p><p><strong>Key Features of the Data Analysis, Statistics &amp; Economics Bundle:</strong></p><ul><li><strong>3 QLS-Endorsed Courses:</strong> We proudly offer 3 QLS-endorsed courses within our [Course_Title] bundle, providing you with industry-recognized qualifications. Plus, you'll receive a <strong>free hardcopy certificate</strong> for each of these courses.</li><ul><li><strong>QLS Course 01:</strong> Statistics</li><li><strong> QLS Course 02: </strong>Economics</li><li><strong> QLS Course 03:</strong> Basic Data Analysis</li></ul></ul><ul><li><strong>5 CPD QS Accredited Courses</strong>: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices.</li><ul><li><strong>Course 01: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 02: </strong>Azure Machine Learning</li><li><strong>Course 03:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 04: </strong>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 05: </strong>2021 Data Science &amp; Machine Learning with R from A-Z</li></ul></ul><ul><li>In Addition, you'll get <strong>Five Career Boosting Courses</strong> absolutely FREE with this Bundle.</li><ul><li><strong>Course 01: </strong>Professional CV Writing</li><li><strong>Course 02: </strong>Job Search Skills</li><li><strong>Course 03:</strong> Self Esteem &amp; Confidence Building</li><li><strong>Course 04: </strong>Professional Diploma in Stress Management</li><li><strong>Course 05: </strong>Complete Communication Skills Master Class</li></ul></ul><ul><li>Convenient Online Learning: Our Data Analysis, Statistics &amp; Economics courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home.</li></ul><p><strong>Learning Outcomes</strong></p><ul><li>Gain a robust theoretical understanding of Statistics, Economics &amp; Basic Data Analysis.</li><li>Develop a strong foundation in Google Data Studio for data analytics.</li><li>Acquire theoretical knowledge of Azure Machine Learning and its applications.</li><li>Familiarise with Machine Learning model creation using AWS SageMaker Canvas.</li><li>Understand the role of Statistics &amp; Probability in Data Science and Machine Learning.</li><li>Learn the principles and applications of Data Science &amp; Machine Learning with R.</li><li>Receive QLS-endorsed hardcopy certificates for three foundational courses.</li></ul> <p>Dive into the comprehensive world of Data Analysis, Statistics &amp; Economics with our tailored eight-course bundle. Our blend of three QLS-endorsed courses and five CPD QS accredited courses ensures a well-rounded understanding of these ever-relevant fields. Enhance your theoretical knowledge base and embrace the opportunity to explore different facets of these domains, from basic principles to the utilisation of cutting-edge tools.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        270 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in gaining a theoretical understanding of Data Analysis, Statistics &amp; Economics.</li><li>Professionals looking to supplement their existing knowledge with a broadened skillset.</li><li>Students seeking to complement their academic studies with additional courses.</li><li>Career changers looking to delve into the fields of Data Analysis, Statistics &amp; Economics.</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £26,000 - £50,000</li><li>Economic Consultant: £30,000 - £60,000</li><li>Statistical Analyst: £30,000 - £55,000</li><li>Machine Learning Engineer: £40,000 - £80,000</li><li>Business Intelligence Analyst: £35,000 - £65,000</li><li>Operations Research Analyst: £28,000 - £55,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Academy for Health and Fitness

Online Course Marketing

Finance Cert & Exam Prep

ELS (Effective Learning Service)

A Level Economics

Data Analysis, Statistics & Economics - QLS Endorsed

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analysis &amp; Data Science Diploma </strong>bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Big Data Analysis &amp; Data Science Diploma is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Data Analysis &amp; Data Science Diploma </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Big Data Analysis &amp; Data Science Diploma package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analysis &amp; Data Science Diploma bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This bundle comprises the following courses:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Quality Standards</strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Courses:</strong></p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analytics with PySpark Power BI and MongoDB</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analytics with PySpark Tableau Desktop and MongoDB</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Complete Python Machine Learning &amp; Data Science Fundamentals</strong></li></ol><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcome:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Gain comprehensive insights into multiple fields.</li><li fr-original-style="" style="box-sizing: border-box;">Foster critical thinking and problem-solving skills across various disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Understand industry trends and best practices through the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Data Analysis &amp; Data Science Diploma</strong> Bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Develop practical skills applicable to real-world situations.</li><li fr-original-style="" style="box-sizing: border-box;">Enhance personal and professional growth with the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Data Analysis &amp; Data Science Diploma.</strong></li><li fr-original-style="" style="box-sizing: border-box;">Build a strong knowledge base in your chosen course via the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analysis &amp; Data Science Diploma</strong>.</li><li fr-original-style="" style="box-sizing: border-box;">Benefit from the flexibility and convenience of online learning.</li><li fr-original-style="" style="box-sizing: border-box;">With the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analysis &amp; Data Science Diploma</strong> package, validate your learning with a CPD certificate.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Big Data Analysis &amp; Data Science Diploma, a rich anthology of 15 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analysis &amp; Data Science Diploma</strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisBig Data Analysis &amp; Data Science Diploma bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Big Data Analysis &amp; Data Science Diploma bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis &amp; Data Science Diploma package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
                                    <div fr-original-style="" style="box-sizing: border-box;">
                                        25 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analysis &amp; Data Science Diploma</strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analysis &amp; Data Science Diploma </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analysis &amp; Data Science Diploma </strong>bundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">You will get a complimentary Hard Copy Certificate.</strong></p></div>

                        </div>

NextGen Learning

Diploma in Professional Studies (DPS)

Continuous Professional Development (CPD)

Big Data Analysis & Data Science Diploma - 8 Courses Bundle 

<p><strong>*** 10 QLS Endorsed Courses for Programmers | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support ***</strong></p><p>Imagine being able to code in a multitude of programming languages and expand your skill set, making you an invaluable asset in the tech industry. Imagine navigating complex software development projects confidently and being able to design and implement effective solutions. This dream can become a reality with our<strong> Computer Programmer QLS Endorsed Courses </strong>bundle<strong>.</strong></p><p>Endorsed by the prestigious Quality Licence Scheme, our bundle offers learners <strong>10 QLS Endorsed Certificate hard copies free of charge</strong> upon completion. Whether you're a beginner looking to dip your toes in the world of programming or a seasoned pro wanting to expand your knowledge, our courses cover a wide range of programming languages, from Assembly to C++, HTML to Python, and even machine learning.</p><p>As a tech industry professional in the UK, it's essential to stay up to date with the latest programming languages and tools. With statistics showing that the tech industry is continuing to grow, with a <strong>projected 7% increase in tech job openings</strong> by 2023 in the UK alone, there has never been a better time to invest in your career development. By taking our <strong>Computer Programmer QLS Endorsed Courses</strong>, you can position yourself as a highly skilled and knowledgeable candidate in this competitive field.</p><p>Invest in your future and enrol in our <strong>Computer Programmer QLS Endorsed Courses</strong> bundle today. With our free certificate hardcopies and Quality Licence Scheme endorsement, you'll be on your way to achieving your programming career goals in no time.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Learn assembly programming for various platforms, including Windows, Linux, and macOS.</li><li>Develop a solid foundation in C++ programming language and learn advanced concepts such as templates and exceptions.</li><li>Gain a thorough understanding of HTML 5 and its applications in web development.</li><li>Develop dynamic and secure web applications using PHP, MySQL, and Object-Oriented Programming (OOP) principles.</li><li>Master the basics of HTML and CSS and develop responsive websites with HTML Web development.</li><li>Learn to write secure code and prevent common security vulnerabilities in web applications.</li><li>Gain proficiency in Python programming language and apply it to data analysis and Machine learning.</li></ul><p><strong>Why Choose Imperial Academy?</strong></p><ul><li>Fully Online Training</li><li>QLS Endorsed &amp; CPD Accredited Certificate</li><li>No Time Limits. Study at Your Own Pace</li><li>Expert Instructors</li><li>Audio Visual Training</li><li>Up-to-date Curriculum and Valuable Guidelines</li></ul><p>The <strong>Computer Programmer QLS Endorsed Courses</strong> bundle offers a diverse range of courses covering various programming languages and concepts, career coaching, and self-improvement. The bundle starts with assembly programming, where learners will gain a fundamental understanding of assembly language and develop skills to write assembly programs for different platforms. The C++ development course is designed for learners who want to learn both basic and advanced C++ programming concepts. The Basic HTML 5 course provides a solid foundation in HTML 5, including its new features and applications in web development.</p><p>The PHP &amp; MySQL web development course teaches learners how to develop dynamic web applications using PHP, MySQL, and OOP principles. The HTML Web Development Crash Course covers the basics of HTML and CSS, enabling learners to create responsive websites with a mobile-first approach. The Secure Programming of Web Applications course is designed to help learners prevent common security vulnerabilities in web applications.</p><p>The Python Programming for Non-Programmers Level 5 course teaches learners Python programming from scratch. The Machine Learning with Python course covers machine learning concepts and tools and their applications in data analysis.</p><p><strong>This bundle comprises the following courses:</strong></p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Assembly Programming For All Platforms</li><li><strong>Course 02:</strong> C++ Development: The Complete Coding Guide</li><li><strong>Course 03:</strong> Basic HTML 5</li><li><strong>Course 04:</strong> Ultimate PHP &amp; MySQL Web Development Course &amp; OOP Coding</li><li><strong>Course 05:</strong> HTML Web Development Crash Course</li><li><strong>Course 06:</strong> Secure Programming of Web Applications - Developers and TPMs</li><li><strong>Course 07:</strong> Python Programming for Non-Programmers Level 5</li><li><strong>Course 08:</strong> Machine Learning with Python</li><li><strong>Course 09:</strong> Career Coaching</li><li><strong>Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p><strong>Certificate: </strong></p><p>The Quality Licence Scheme ensures excellence and quality. These courses' learning outcomes also provide the learners with high competence in their line of work. So, <strong>QLS Endorsed</strong> courses deliver not only deep knowledge but also add great value to the resume.</p><p>After completing the assessments, learners will be eligible for a prestigious <strong>QLS Certificate</strong> for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><p>Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in learning assembly programming, C++ programming, HTML, PHP, MySQL, and Python.</li><li>Anyone looking to improve their programming skills and gain knowledge in web development and machine learning.</li><li>People interested in improving their self-confidence and self-esteem through the Unstoppable</li><li>Self-Confidence &amp; High Self-Esteem Blueprint course.</li><li>Individuals seeking career coaching to advance their careers and improve their job prospects.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in this <strong>Computer Programmer QLS Endorsed Courses</strong>.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Web Developer: £20,000 - £50,000</li><li>Software Engineer: £25,000 - £65,000</li><li>Data Analyst: £24,000 - £50,000</li><li>Machine Learning Engineer: £30,000 - £80,000</li><li>Cybersecurity Analyst: £25,000 - £60,000</li><li>Career Coach: £20,000 - £60,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Hard Copy QLS Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Imperial Academy

Computer Programming: Where Inspiration Meets Logic And Dreams Become Lines Of Code

Computer Programmer QLS Endorsed Courses

<p>The world is changing at an unprecedented pace, and data is at the heart of it all. Are you ready to join the ranks of data analysts, statisticians, and economists who are driving the future? We have just the thing to get you started - our Data Analysis, Statistics &amp; Economics bundle, designed to give you the skills and expertise you need to thrive in these exciting fields.</p><p>At the heart of the bundle are three QLS-endorsed courses that cover the fundamentals of statistics, economics, and basic data analysis. Whether you're just starting out or looking to enhance your skills, these courses are the perfect place to begin. And upon completion of each course, you'll receive a hardcopy certificate endorsed by QLS, recognised globally as a mark of excellence in education and training.</p><p>But that's not all - our bundle also includes five CPD-QS accredited courses that cover the latest tools and techniques used by industry professionals. From Google Data Studio and Azure Machine Learning to Statistics &amp; Probability for Data Science &amp; Machine Learning and 2021 Data Science &amp; Machine Learning with R from A-Z, these courses are designed to give you the competitive edge you need to succeed in today's data-driven world.</p><p>So what are you waiting for? Join the ranks of the data elite and unlock your full potential with our Data Analysis, Statistics &amp; Economics bundle.</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Statistics at QLS Level 4</li><li><strong>Course 02:</strong> Advanced Diploma in Economics at QLS Level 6</li><li><strong>Course 03:</strong> Diploma in Data Analysis Fundamentals at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Google Data Studio: Data Analytics</li><li><strong>Course 05:</strong> Azure Machine Learning</li><li><strong>Course 06:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 07:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 08:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Ability to analyse and interpret data</li><li>Understanding of statistical methods and their application</li><li>Familiarity with economic concepts and their role in decision-making</li><li>Proficiency in basic data analysis techniques</li><li>Knowledge of machine learning and data analytics tools</li><li>Critical thinking and problem-solving skills</li><li>Effective communication and presentation skills</li></ul><p>Our QLS-endorsed courses in Statistics, Economics, and Basic Data Analysis cover a range of topics, from data visualisation and hypothesis testing to regression analysis and forecasting. In the Statistics course, you'll learn how to use statistical methods to make sense of data and draw conclusions. The Economics course covers fundamental concepts like supply and demand, market equilibrium, and trade. And in the Basic Data Analysis course, you'll gain proficiency in Excel and other data analysis tools.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals looking to enhance their data analysis skills</li><li>Students interested in pursuing a career in data science or economics</li><li>Business professionals looking to improve their decision-making skills</li><li>Anyone interested in learning the fundamentals of statistics and economics</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £22,000 - £50,000 per year</li><li>Economist: £25,000 - £60,000 per year</li><li>Data Scientist: £35,000 - £90,000 per year</li><li>Business Analyst: £25,000 - £60,000 per year</li><li>Financial Analyst: £25,000 - £60,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Data Analysis, Statistics & Economics Course

<div>
</div>
<section>
<div>
<h2>About this training course</h2>
<p>Artificial lift systems are an important part of production operations for the entire lifecycle of an asset. Often, oil and gas wells require artificial lift for most of the life cycle. This 5-day training course offers a thorough treatment of artificial lift techniques including design and operation for production optimization. With the increasing need to optimize dynamic production in highly constrained cost environments, opportunities and issues related to real-time measurements and optimization techniques needs to be discussed and understood. Artificial lift selection and life cycle analysis are covered. These concepts are discussed and reinforced using case studies, quizzing tools, and exercises with software. Participants solve examples and class problems throughout the course. Animations and videos reinforce the concepts under discussion.</p>
<p>Understanding of these important production concepts is a must have to exploit the existing assets profitably.</p>
<p><strong>Unique Features:</strong></p>
<ul>
<li>Hands-on usage of <strong>SNAP Software</strong> to solve gas-lift exercises</li>
<li>Discussion on <strong>digital oil field</strong></li>
<li><strong>Machine learning</strong> <strong>applications</strong> in gas-lift optimization</li>
</ul>
</div>
</section>
<section>
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Training Objectives 

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<div id="collapseOne">
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<p>After the completion of this training course, participants will be able to:</p>
<ul>
<li>Understand the basics and advanced concepts of each form of artificial lift systems including application envelope, relative strengths, and weaknesses</li>
<li>Easily recognize the different components from downhole to the surface and their basic structural and operational features</li>
<li>Design and analyze different components using appropriate software tools</li>
<li>Understand challenges facing artificial lift applications and the mitigation of these challenges during selection, design, and operation</li>
<li>Learn about the role of digital oilfield tools and techniques and their applications in artificial lift and production optimization</li>
<li>Learn about use cases of Machine learning and artificial intelligence in the artificial lift</li>
</ul>
</div>
</div>
</div>
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<div>

Target Audience 

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<div id="collapseTwo">
<div>
<p>This training course is suitable and will greatly benefit the following specific groups:</p>
<ul>
<li>Production, reservoir, completion, drilling and facilities engineers, analysts, and operators</li>
<li>Anyone interested in learning about selection, design, analysis and optimum operation of artificial lift and related production systems will benefit from this course.</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Intermediate</li>
<li>Advanced</li>
</ul>
</div>
</div>
</div>
<div>
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Training Methods 

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<div id="collapseThree">
<div>
<p><span>The training instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught in their own organization.</span></p>
<p><em><span>Course Duration: 5 days in total (35 hours).</span></em></p>
<p><strong><span>Training Schedule</span></strong></p>
<p><span>0830 - Registration </span><span>0900 - Start of training </span><span>1030 - Morning Break </span><span>1045 - Training recommences </span><span>1230 - Lunch Break </span><span>1330 - Training recommences </span><span>1515 - Evening break </span><span>1530 - Training recommences </span><span>1700 - End of Training</span></p>
<p><span>The maximum number of participants allowed for this training course is 20.</span></p>
<p>This course is also available through our Virtual Instructor Led Training (VILT) format.</p>
<p><strong>Prerequisites:</strong></p>
<ul>
<li>Understanding of petroleum production concepts.</li>
<li>Each participant needs a laptop/PC for solving class examples using software to be provided during class. Laptop/PC needs to have a current Windows operating system and at least 500 MB free disk space. Participants should have administrator rights to install software.</li>
</ul>
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Trainer

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<p>Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA.</p>
<p><strong>Highlighted Work Experience: </strong></p>
<ul>
<li>At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe.</li>
<li>Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements.</li>
<li>Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics.</li>
<li>Developed a workshop on digital oilfield approaches for production engineers.</li>
</ul>
<p><strong>Patents:</strong></p>
<ul>
<li>Principal inventor: 'Smarter Slug Flow Conditioning and Control'</li>
<li>Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells'</li>
<li>Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution'</li>
</ul>
<p><strong>Worldwide Experience in Training / Seminar / Workshop Deliveries:</strong></p>
<ul>
<li>Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&amp;T, Louisiana State, U of Southern California, and U of Houston.</li>
<li>He has conducted seminars, bespoke trainings / workshops globally for practicing professionals:
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<li><u>Companies:</u> Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others.</li>
<li><u>Countries:</u> USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela.</li>
<li>Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic.</li>
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POST TRAINING COACHING SUPPORT (OPTIONAL)

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<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information post training support and fees applicable</p>
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Artificial Lift and Real-Time Production Optimization in Digital Oilfield

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<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p>This Virtual Instructor Led Training (VILT) course presents advanced methodologies that implement demand response and energy conservation programs in light of the integration of new technologies, regulatory changes and the accelerated penetration of renewable energy resources.</p>
<p>This VILT course provides examples and case studies from North American and European jurisdictions covering the operational flexibilities on the demand side including requirements for new building codes to achieve zero net energy. The course describes a public agency's goals and objectives for conserving and otherwise reducing energy consumption and managing its demand for energy. This course presents the demand response implemented for economics and system security such as system balancing and relieving transmission congestion, or for system adequacy. The course also presents the principal attributes of conservation programs and the associated success criteria. In a system with increased penetration of renewable resources, demand response provides flexibility to system operators, helping them to maintain the reliability and the security of supply.</p>
<p>Demand response is presented as a competitive alternative to additional power sources, enhancing competition and liquidity in electricity markets. The unique characteristics are discussed from a local, consumer centric and also from a system perspective bringing to life the ever changing paradigm for delivery energy to customers. Interoperability aspects and standards are discussed, as well as the consumer centric paradigm of Transactive Energy with IOT enabled flexibilities at system level, distribution networks and microgrids.</p>
<p>The VILT course introduces the blockchain as a new line of defense against cyber threats and its increasing application in P2P transactions and renewable certificates. Our trainer's industry experience spans three decades with one of the largest Canadian utilities where she led or contributed to large operational studies and energy policies and decades of work with IEEE, NSERC and CIGRE. Our key expert also approaches to the cross sectional, interdisciplinary state of the art methodologies brings real life experience of recent industry developments.</p>
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<li>Innovative Digital Technologies</li>
<li>How systems Facilitate Operational Flexibility on the Demand Side</li>
<li>The Ecosystem of Demand Side Management Programs</li>
<li>Advanced Machine Learning techniques with examples from CAISO</li>
<li>Regulatory Policy Context and how to reduce regulatory barriers</li>
<li>Industry Examples from NERC and ENTSO</li>
<li>Relevant Industry standards: IEEE and IEC</li>
<li>Manage Congestion with Distributed Operational Flexibilities: Grid to Distribution Controls; examples from NERC (NA) and ENTSO (Europe)</li>
<li>Grid solutions with IEC 61850 communication protocols</li>
<li>Decentralized grid controls</li>
<li>The New Grid with accelerated V2G and Microgrids</li>
<li>How DSM is and will be applied in Your System: Examples and discussions</li>
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<li>All professionals interested in expanding their expertise, or advancing their career, or take on management and leadership roles in the rapidly evolving energy sector</li>
<li>Energy professionals implementing demand side management, particularly in power systems with increased renewable penetration, to allow the much needed operational flexibility paramount to maintaining the reliability and stability of the power system and in the same time offering all classes of customers flexible and economical choices</li>
<li>Any utility professional interested in understanding the new developments in the power industry</li>
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<p>The VILT course will be delivered online in <strong>5 half-day sessions comprising 4 hours per day, with 2 x 10 minutes break per day</strong>, including time for lectures, discussion, quizzes and short classroom exercises.</p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
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<p><strong>Your first expert course leader</strong> is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5.</p>
<p>With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana.</p>
<p><strong>Your second expert course leader </strong>is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch.</p>
<p>He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto.</p>
<p>Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin.</p>
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POST TRAINING COACHING SUPPORT (OPTIONAL)

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<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
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Demand Side Management - Integration of New Technologies, Regulatory Changes & Renewable Energy Resources

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Packt

Python

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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By EnergyEdge - Training for a Sustainable Energy Future