91 Natural Language Processing (NLP) courses delivered On Demand

The comprehensive Complete U&P AI - Natural Language Processing (NLP) with Python 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 Complete U&P AI - Natural Language Processing (NLP) with Python today, and learn from the very best the industry has to offer! This best selling Complete U&P AI - Natural Language Processing (NLP) with Python has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Complete U&P AI - Natural Language Processing (NLP) with Python is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Complete U&P AI - Natural Language Processing (NLP) with Python 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 Complete U&P AI - Natural Language Processing (NLP) with Python 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. After successfully completing the Complete U&P AI - Natural Language Processing (NLP) with Python, 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 Complete U&P AI - Natural Language Processing (NLP) with Python will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Complete U&P AI - Natural Language Processing (NLP) with Python 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.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

This course will help you prepare for the AI-900 Exam: Microsoft Azure AI Fundamentals. We will cover the complete exam syllabus as updated in April 2021 with sample questions.

Take your first step toward Natural Language Processing with this beginner-to-pro course. Gain an in-depth understanding of deep learning models for NLP with the help of examples. Learn the essential concepts from the absolute beginning with complete unraveling along with examples in Python.

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

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Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes. Benefits you'll get choosing Apex Learning for this Course: One payment, but lifetime access to 11 CPD courses Certificates, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills Gain valuable knowledge without leaving your home What other courses are included with this Course? Level 2 Microsoft Office Essentials Microsoft Teams Leadership & Management Diploma Working from Home Essentials Mental Health and Working from Home Online Meeting Management Effective Communication Skills Time Management Report Writing Emotional Intelligence and Human Behaviour Curriculum ***Data Science Course Bundle 2022*** Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview Set-up the Environment for the Course (lecture 1) Set-up the Environment for the Course (lecture 2) Two other options to setup environment Python Essentials Python data types Part 1 Python Data Types Part 2 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) Python Essentials Exercises Overview Python Essentials Exercises Solutions Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. NumPy arrays, built-in methods, array methods and attributes. Indexing, slicing, broadcasting & boolean masking Arithmetic Operations & Universal Functions Exercises Overview Exercises Solutions Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. Pandas Introduction Pandas Data Structures - Series Pandas Data Structures - DataFrame Handling Missing Data Data Wrangling - Combining, merging, joining Groupby Useful Methods and Operations Project 1 (Overview) Customer Purchases Data Project 1 (Solutions) Customer Purchases Data Project 2 (Overview) Chicago Payroll Data Project 2 (Solutions Part 1) Chicago Payroll Data Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach Matplotlib Essentials - Exercises Overview Matplotlib Essentials - Exercises Solutions Python for Data Visualization using Seaborn Introduction & Installation Distribution Plots Categorical Plots (Part 1) Categorical Plots (Part 2) Axis Grids Matrix Plots Regression Plots Controlling Figure Aesthetics Exercises Overview Exercise Solutions Python for Data Visualization using pandas Pandas Built-in Data Visualization Pandas Data Visualization Exercises Overview Panda Data Visualization Exercises Solutions Python for interactive & geographical plotting using Plotly and Cufflinks Interactive & Geographical Plotting (Part 1) Interactive & Geographical Plotting (Part 2) Interactive & Geographical Plotting Exercises (Overview) Interactive & Geographical Plotting Exercises (Solutions) Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types….. Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff Linear Regression Model - Hands-on (Part 1) Linear Regression Model Hands-on (Part 2) Good to know! How to save and load your trained Machine Learning Model! Linear Regression Model (Insurance Data Project Overview) Linear Regression Model (Insurance Data Project Solutions) Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. Logistic Regression Model - Hands-on (Part 1) Logistic Regression Model - Hands-on (Part 2) Logistic Regression Model - Hands-on (Part 3) Logistic Regression Model - Hands-on (Project Overview) Logistic Regression Model - Hands-on (Project Solutions) Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality …. K Nearest Neighbors - Hands-on K Nearest Neighbors (Project Overview) K Nearest Neighbors (Project Solutions) Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. Decision Tree and Random Forests - Hands-on (Part 1) Decision Tree and Random Forests (Project Overview) Decision Tree and Random Forests (Project Solutions) Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) Support Vector Machines - Hands-on (SVMs) Support Vector Machines (Project 1 Overview) Support Vector Machines (Project 1 Solutions) Support Vector Machines (Optional Project 2 - Overview) Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method ….. K Means Clustering - Hands-on K Means Clustering (Project Overview) K Means Clustering (Project Solutions) Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) Principal Component Analysis (PCA) - Hands-on Principal Component Analysis (PCA) - (Project Overview) Principal Component Analysis (PCA) - (Project Solutions) Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance Python for Recommender Systems - Hands-on (Part 1) Python for Recommender Systems - - Hands-on (Part 2) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) NLP-Challenges, Data Sources, Data Processing ….. Feature Engineering and Text Preprocessing in Natural Language Processing NLP - Tokenization, Text Normalization, Vectorization, BoW…. BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … Pipeline feature to assemble several steps for cross-validation… How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. CPD 20 CPD hours / points Accredited by CPD Quality Standards Who is this course for? There is no experience or previous qualifications required for enrolment on this Data Science Course Bundle 2022. It is available to all students, of all academic backgrounds. Requirements Our Data Science Course Bundle 2022 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Certificates Certificate of completion Digital certificate - Included

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

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