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I Asked A Python Programmer For A Joke. He Said, 'Import Antigravity' | 10 QLS Endorsed Courses for Python Programmer | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. THE PREDICTIVE ANALYTICS PROCESS * Technical requirements * What is predictive analytics? * Reviewing important concepts of predictive analytics * The predictive analytics process * A quick tour of Python's data science stack PROBLEM UNDERSTANDING AND DATA PREPARATION * Technical requirements * Understanding the business problem and proposing a solution * Practical project ? diamond prices * Practical project ? credit card default DATASET UNDERSTANDING ? EXPLORATORY DATA ANALYSIS * Technical requirements * What is EDA? * Univariate EDA * Bivariate EDA * Introduction to graphical multivariate EDA PREDICTING NUMERICAL VALUES WITH MACHINE LEARNING * Technical requirements * Introduction to ML * Practical considerations before modeling * MLR * Lasso regression * KNN * Training versus testing error PREDICTING CATEGORIES WITH MACHINE LEARNING * Technical requirements * Classification tasks * Credit card default dataset * Logistic regression * Classification trees * Random forests * Training versus testing error * Multiclass classification * Naive Bayes classifiers INTRODUCING NEURAL NETS FOR PREDICTIVE ANALYTICS * Technical requirements * Introducing neural network models * Introducing TensorFlow and Keras * Regressing with neural networks * Classification with neural networks * The dark art of training neural networks MODEL EVALUATION * Technical requirements * Evaluation of regression models * Evaluation for classification models * The k-fold cross-validation MODEL TUNING AND IMPROVING PERFORMANCE * Technical requirements * Hyperparameter tuning * Improving performance IMPLEMENTING A MODEL WITH DASH * Technical requirements * Model communication and/or deployment phase * Introducing Dash * Implementing a predictive model as a web application ADDITIONAL COURSE DETAILS: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Hands-on Predicitive Analytics with Python (TTPS4879) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is appropriate for advanced users, system administrators and web site administrators who want to use Python to support their server installations, as well as anyone else who wants to automate or simplify common tasks with the use of Python scripts. Students can apply the course skills to use Python in basic web development projects or automate or simplify common tasks with the use of Python scripts. Overview This skills-focused course is about 50% hands-on lab to lecture ratio, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert instructor, you'll learn how to: Create working Python scripts following best practices Use python data types appropriately Read and write files with both text and binary data Search and replace text with regular expressions Work with with the standard library and its work-saving modules Create 'real-world', professional Python applications Know when to use collections such as lists, dictionaries, and sets Work with Pythonic features such as comprehensions and iterators Write robust code using exception handling Introduction to Python Programming Basics is a hands-on Python programming course that teaches you the key skills you?ll need to get started with programming in Python to a solid foundational level. The start of the course will lead you through writing and running basic Python scripts, and then guide you through how to use more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting. This course provides you with an excellent kick start for users new to Python and scripting, enabling you to quickly use basic Python skills on the job in a variety of ways. You?ll be able use Python in basic web development projects, or use it to automate or simplify common tasks with the use of Python scripts. The course also serves as a solid primer course / foundation for continued Python study in support for next level web development with Python, using Python in DevOps, Python for data science / machine learning or Python for systems admin or networking support. PYTHON QUICK VIEW * What is Python? * Python timeline * Advantages/disadvantages * Installing Python * Getting help THE PYTHON ENVIRONMENT * Starting Python * Using the interpreter * Running a Python script * Editors and IDEs GETTING STARTED WITH PYTHON * Using variables * Builtin functions * String data * Numberic data * Converting types * Console input/output * Command line parameters FLOW CONTROL * About flow control * The if statement * Relational and Boolean operators * while loops * Exiting from loops ARRAY TYPES * About array types * Lists and list methods * Tuples * Indexing and slicing * Iterating through a sequence * Sequence functions, keywords, and operators * List comprehensions and generators WORKING WITH FILES * File overview * Opening a text file * Reading a text file * Writing to a text file DICTIONARIES AND SETS * About dictionaries * Creating dictionaries * Iterating through a dictionary * About sets * Creating sets * Working with sets FUNCTIONS * Defining functions * Returning values * Parameters and arguments * Variable scope SORTING * The sorted() function * Custom sort keys * Lambda functions * Sorting in reverse * Using min() and max() ERRORS AND EXCEPTION HANDLING * Exceptions * Using try/catch/else/finally * Handling multiple exceptions * Ignoring exceptions MODULES AND PACKAGES * Creating Modules * The import statement * Module search path * Using packages * Function and module aliases GETTING STARTED WITH OBJECT ORIENTED PROGRAMMING AND CLASSES * About object-oriented programming * Defining classes * Constructors * Understanding self * Properties * Instance Methods and data * Class methods and data * Inheritance ADDITIONAL COURSE DETAILS: Nexus Humans Introduction to Python Programming Basics (TTPS4800) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Introduction to Python Programming Basics (TTPS4800) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. PYTHON FOR DATA SCIENCE * ? Using Modules * ? Listing Methods in a Module * ? Creating Your Own Modules * ? List Comprehension * ? Dictionary Comprehension * ? String Comprehension * ? Python 2 vs Python 3 * ? Sets (Python 3+) * ? Python Idioms * ? Python Data Science ?Ecosystem? * ? NumPy * ? NumPy Arrays * ? NumPy Idioms * ? pandas * ? Data Wrangling with pandas' DataFrame * ? SciPy * ? Scikit-learn * ? SciPy or scikit-learn? * ? Matplotlib * ? Python vs R * ? Python on Apache Spark * ? Python Dev Tools and REPLs * ? Anaconda * ? IPython * ? Visual Studio Code * ? Jupyter * ? Jupyter Basic Commands * ? Summary APPLIED DATA SCIENCE * ? What is Data Science? * ? Data Science Ecosystem * ? Data Mining vs. Data Science * ? Business Analytics vs. Data Science * ? Data Science, Machine Learning, AI? * ? Who is a Data Scientist? * ? Data Science Skill Sets Venn Diagram * ? Data Scientists at Work * ? Examples of Data Science Projects * ? An Example of a Data Product * ? Applied Data Science at Google * ? Data Science Gotchas * ? Summary DATA ANALYTICS LIFE-CYCLE PHASES * ? Big Data Analytics Pipeline * ? Data Discovery Phase * ? Data Harvesting Phase * ? Data Priming Phase * ? Data Logistics and Data Governance * ? Exploratory Data Analysis * ? Model Planning Phase * ? Model Building Phase * ? Communicating the Results * ? Production Roll-out * ? Summary REPAIRING AND NORMALIZING DATA * ? Repairing and Normalizing Data * ? Dealing with the Missing Data * ? Sample Data Set * ? Getting Info on Null Data * ? Dropping a Column * ? Interpolating Missing Data in pandas * ? Replacing the Missing Values with the Mean Value * ? Scaling (Normalizing) the Data * ? Data Preprocessing with scikit-learn * ? Scaling with the scale() Function * ? The MinMaxScaler Object * ? Summary DESCRIPTIVE STATISTICS COMPUTING FEATURES IN PYTHON * ? Descriptive Statistics * ? Non-uniformity of a Probability Distribution * ? Using NumPy for Calculating Descriptive Statistics Measures * ? Finding Min and Max in NumPy * ? Using pandas for Calculating Descriptive Statistics Measures * ? Correlation * ? Regression and Correlation * ? Covariance * ? Getting Pairwise Correlation and Covariance Measures * ? Finding Min and Max in pandas DataFrame * ? Summary DATA AGGREGATION AND GROUPING * ? Data Aggregation and Grouping * ? Sample Data Set * ? The pandas.core.groupby.SeriesGroupBy Object * ? Grouping by Two or More Columns * ? Emulating the SQL's WHERE Clause * ? The Pivot Tables * ? Cross-Tabulation * ? Summary DATA VISUALIZATION WITH MATPLOTLIB * ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary DATA SCIENCE AND ML ALGORITHMS IN SCIKIT-LEARN * ? Data Science, Machine Learning, AI? * ? Types of Machine Learning * ? Terminology: Features and Observations * ? Continuous and Categorical Features (Variables) * ? Terminology: Axis * ? The scikit-learn Package * ? scikit-learn Estimators * ? Models, Estimators, and Predictors * ? Common Distance Metrics * ? The Euclidean Metric * ? The LIBSVM format * ? Scaling of the Features * ? The Curse of Dimensionality * ? Supervised vs Unsupervised Machine Learning * ? Supervised Machine Learning Algorithms * ? Unsupervised Machine Learning Algorithms * ? Choose the Right Algorithm * ? Life-cycles of Machine Learning Development * ? Data Split for Training and Test Data Sets * ? Data Splitting in scikit-learn * ? Hands-on Exercise * ? Classification Examples * ? Classifying with k-Nearest Neighbors (SL) * ? k-Nearest Neighbors Algorithm * ? k-Nearest Neighbors Algorithm * ? The Error Rate * ? Hands-on Exercise * ? Dimensionality Reduction * ? The Advantages of Dimensionality Reduction * ? Principal component analysis (PCA) * ? Hands-on Exercise * ? Data Blending * ? Decision Trees (SL) * ? Decision Tree Terminology * ? Decision Tree Classification in Context of Information Theory * ? Information Entropy Defined * ? The Shannon Entropy Formula * ? The Simplified Decision Tree Algorithm * ? Using Decision Trees * ? Random Forests * ? SVM * ? Naive Bayes Classifier (SL) * ? Naive Bayesian Probabilistic Model in a Nutshell * ? Bayes Formula * ? Classification of Documents with Naive Bayes * ? Unsupervised Learning Type: Clustering * ? Clustering Examples * ? k-Means Clustering (UL) * ? k-Means Clustering in a Nutshell * ? k-Means Characteristics * ? Regression Analysis * ? Simple Linear Regression Model * ? Linear vs Non-Linear Regression * ? Linear Regression Illustration * ? Major Underlying Assumptions for Regression Analysis * ? Least-Squares Method (LSM) * ? Locally Weighted Linear Regression * ? Regression Models in Excel * ? Multiple Regression Analysis * ? Logistic Regression * ? Regression vs Classification * ? Time-Series Analysis * ? Decomposing Time-Series * ? Summary LAB EXERCISES * Lab 1 - Learning the Lab Environment * Lab 2 - Using Jupyter Notebook * Lab 3 - Repairing and Normalizing Data * Lab 4 - Computing Descriptive Statistics * Lab 5 - Data Grouping and Aggregation * Lab 6 - Data Visualization with matplotlib * Lab 7 - Data Splitting * Lab 8 - k-Nearest Neighbors Algorithm * Lab 9 - The k-means Algorithm * Lab 10 - The Random Forest Algorithm

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.

This course focuses on understanding all the basic theory and programming skills required as a data scientist, featuring 35+ practical case studies covering common business problems faced by them. This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of data science and deep learning to real-world business problems.

This beginner-friendly course takes us on a journey into data visualization. You will learn to transform raw data into stunning visuals using Matplotlib, Seaborn, and Plotly. From charts to dynamic heatmaps, we will master the essentials. Fuel your curiosity, enhance your skills, and communicate insights effectively to become a Python data visualization pro!

Get ready for an exceptional online learning experience with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst 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. The Big Data Analysis, Data Science, Fintech & Python for Data Analyst is a dynamic package, that blends the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analysis package has something for everyone. As part of the Big Data Analysis, Data Science, Fintech & Python for Data Analyst package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analysis bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This bundle comprises the following courses: CPD Quality Standards Courses: 1. Big Data Analytics with PySpark Power BI and MongoDB 2. Big Data Analytics with PySpark Tableau Desktop and MongoDB 3. Building Big Data Pipelines with PySpark MongoDB and Bokeh 4. Develop Big Data Pipelines with R & Sparklyr & Tableau 5. Develop Big Data Pipelines with R, Sparklyr & Power BI 6. Learn Python, JavaScript, and Microsoft SQL for Data Science 7. SQL for Data Science, Data Analytics, and Data Visualization 8. Excel Data Analysis 9. Introduction to Data Analytics with Tableau 10. Business and Data Analytics for Beginners 11. Google Data Studio: Data Analytics 12. Basic Data Analysis 13. FinTech Learning Outcome: * Gain comprehensive insights into multiple fields. * Foster critical thinking and problem-solving skills across various disciplines. * Understand industry trends and best practices through the Data Analysis Bundle. * Develop practical skills applicable to real-world situations. * Enhance personal and professional growth with Data Analysis. * Build a strong knowledge base in your chosen course via Data Analysis. * Benefit from the flexibility and convenience of online learning. * With the Data Analysis package, validate your learning with a CPD certificate. 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. Embrace the future of learning with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst, a rich anthology of 15 diverse courses. Each course in the Data Analysis bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisBig Data Analysis, Data Science, Fintech & Python for Data Analyst bundle will take you on a unique and enriching educational journey. 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, Data Science, Fintech & Python for Data Analyst bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis package your trusted companion in your lifelong learning journey. CPD 35 CPD hours / points Accredited by CPD Quality Standards WHO IS THIS COURSE FOR? The Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle is perfect for: * Lifelong learners looking to expand their knowledge and skills. * Professionals seeking to enhance their career with CPD certification. * Individuals wanting to explore new fields and disciplines. * Anyone who values flexible, self-paced learning from the comfort of home. CAREER PATH Unleash your potential with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst 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 Data Analysis bundle package. CERTIFICATES CERTIFICATE OF COMPLETION Digital certificate - Included CERTIFICATE OF COMPLETION Hard copy certificate - Included You will get a complimentary Hard Copy Certificate.

In the fast-evolving landscape of UK DevOps engineering, the demand for skilled professionals has reached unprecedented levels. Recent challenges in the industry underscore the need for adept DevOps Engineers who seamlessly integrate web development, programming, and Linux proficiency. Our CPD Certified DevOps Engineer course is meticulously crafted to address these demands, offering a comprehensive journey through web development, Linux mastery, and specialised programming languages. Embrace a transformative learning experience that not only equips you with essential technical skills but also positions you at the forefront of the DevOps revolution in the UK. This DevOps Engineer - CPD Certified Bundle Consists of the following Premium courses: * Course 01: Complete Web Development * Course 02: Linux for Cloud and DevOps Engineers * Course 03: Computer Programming Specialist Certificate * Course 04: Basic C# Coding * Course 05: C# Basics * Course 06: JavaScript Functions * Course 07: PHP Web Development with MySQL; GitHub & Heroku * Course 08: ASP.Net MVC and Entity Framework Course * Course 09: Learn Spring & Angular Material with a Full Web Application * Course 10: Build Progressive Web Apps with Angular * Course 11: Computer Science with Python Course * Course 12: JavaScript Foundations for Everyone * Course 13: Learn to Code HTML, CSS & Javascript * Course 14: Asynchronous JavaScript Basics * Course 15: Node JS: API Development with Swagger Interface Description Language * Course 16: Mobile and Web Development with Ionic & Angular JS * Course 17: Coding Essentials - Javascript, ASP. Net, C# - Bonus HTML * Course 18: Basics of WordPress: Create Unlimited Websites * Course 19: Master JavaScript with Data Visualization * Course 20: Web Applications for Specialisation on Development 10 Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques- * Course 01: Effective Communication Skills Diploma * Course 02: Business Networking Skills * Course 03: Influencing and Negotiation Skills * Course 04: Delegation Skills Training * Course 05: Time Management * Course 06: Leadership Skills Training * Course 07: Decision Making and Critical Thinking Online Course * Course 08: Emotional Intelligence and Social Management Diploma * Course 09: Assertiveness Skills * Course 10: Touch Typing Complete Training Diploma Learning Outcomes: Upon completion of this DevOps Engineer - CPD Certified bundle, you should be able to: * Proficiency in complete web development and Linux for DevOps. * Mastery in C# coding, ASP.Net MVC, and Entity Framework. * Fluency in JavaScript, including advanced functions and asynchronous basics. * Competence in mobile and web development with Ionic & Angular JS. * Expertise in Python for computer science and data visualisation. * Ability to create unlimited websites using WordPress and JavaScript. As you embark on this CPD-certified journey, you'll unravel the intricacies of complete web development fortified by Linux expertise. From mastering C# and JavaScript to delving into the realms of ASP.Net MVC and Entity Framework, this course ensures a holistic understanding of the technologies shaping the industry. Elevate your capabilities by building progressive web apps, exploring Python in computer science, and conquering the nuances of mobile and web development with Ionic & Angular JS. Join us to unlock the keys to success in the world of DevOps engineering, making an impact that echoes throughout the UK tech industry. CPD 300 CPD hours / points Accredited by CPD Quality Standards WHO IS THIS COURSE FOR? * Individuals aspiring to become DevOps Engineers. * Web developers looking to enhance their Linux proficiency. * Programmers seeking expertise in C# and JavaScript. * Tech enthusiasts interested in ASP.Net MVC and Entity Framework. * Those wanting to excel in mobile and web development. * Individuals keen on mastering Python for data visualisation. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. REQUIREMENTS To participate in this DevOps Engineer course, all you need is - * A smart device * A secure internet connection * And a keen interest in DevOps Engineer * AWS Certified DevOps Engineer - Professional CAREER PATH * Web Developer: •40,000 - •60,000 * DevOps Engineer: •45,000 - •70,000 * Full-Stack Developer: •50,000 - •75,000 * Software Engineer: •45,000 - •65,000 * JavaScript Developer: •40,000 - •60,000 * Systems Administrator: •35,000 - •55,000 CERTIFICATES CPD ACCREDITED CERTIFICATE Digital certificate - Included * CPD Accredited e-Certificate - Free * CPD Accredited Hardcopy Certificate - Free * Enrolment Letter - Free * Student ID Card - Free

Transform Your Career with Our IT: Data Analysis, Data Science & Data Protection Program - an all-in-one Program Designed for Mastery! Do you know the demand for IT professionals with expertise in data science is skyrocketing? This Ultimate IT: Data Analysis, Data Science & Data Protection Program is your gateway to a thriving career in this dynamic industry. This program is meticulously designed to equip you with the knowledge and skills demanded by hiring managers across various sectors. By enrolling in this IT: Data Analysis, Data Science & Data Protection program, you'll embark on a journey that opens doors to exciting opportunities and empowers you to shape your future in the IT industry. Our IT: Data Analysis, Data Science & Data Protection program will give you a comprehensive understanding of data analysis, from data collection and preparation to data visualisation and communication. You will be equipped with the necessary skills and guidance to uncover insights from data, solve real-world problems, and make informed decisions. Also, you will discover the ethical and legal implications of data handling, how to protect sensitive information & develop a career in this sector. Moreover, we're your dedicated partners on this exciting journey. Our goal isn't just to teach you; it's to support you 24/7 so you can get closer to your dream job. We're so confident with our program that we offer a 100% money-back guarantee, ensuring your complete satisfaction. Learning Outcomes By completing this IT: Data Analysis, Data Science & Data Protection program, you will gain expertise in the following: * Data analysis techniques and methodologies. * Python programming for data analysis. * Business intelligence and data mining. * Advanced Excel techniques, including VBA and Power Query. * SQL programming and big data technologies. * Data Science & Data Protection, Machine Learning with Python and R. * Data visualisation with tools like Tableau and Power BI. * Statistics and probability for data science. * Effective career development and job-seeking skills. * Design an engaging resume and excel in the job search. * Succeed in interviews, including video interviews. * Build a strong LinkedIn profile to connect with professionals and enhance your online visibility in IT: Data Analysis- Data Science & Data Protection field. Courses Included in the Program You get 25 in-demand courses once you enrol in our IT: Data Analysis, Data Science & Data Protection program. * => Course 01: Introduction to Data Analysis * => Course 02: Data Analytics * => Course 03: Python for Data Analysis * => Course 04: Basic Google Data Studio * => Course 05: Business Intelligence and Data Mining Masterclass * => Course 06: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query * => Course 07: SQL Programming Masterclass * => Course 08: SQL NoSQL Big Data and Hadoop * => Course 09: Data Science & Machine Learning with Python * => Course 10: Machine Learning with Python * => Course 11: Data Science & Machine Learning with R * => Course 12: Data Analytics with Tableau * => Course 13: Develop Big Data Pipelines with R & Sparklyr & Tableau * => Course 14: Complete Introduction to Business Data Analysis Level 3 * => Course 15: Data Analysis in Microsoft Excel Complete Training * => Course 16: Excel Data Analysis for Beginner * => Course 17: GDPR Data Protection Level 5 * => Course 18: Master JavaScript with Data Visualization * => Course 19: Data Visualization and Reporting with Power BI * => Course 20: Statistics & Probability for Data Science & Machine Learning * => Course 21: Career Development Plan Fundamentals * => Course 22: CV Writing and Job Searching * => Course 23: Interview Skills: Ace the Interview * => Course 24: Video Job Interview for Job Seekers * => Course 25: How to Create a Professional LinkedIn Profile Enrol in our highly regarded IT: Data Analysis, Data Science & Data Protection program, featuring a job-relevant curriculum that ensures your skills align with employer expectations across various sectors. Don't miss this opportunity - your success story starts now! Our IT: Data Analysis, Data Science & Data Protection Program is a comprehensive and industry-relevant journey through data analysis, data science, and IT analytics. With a focus on providing theoretical knowledge and academic depth, this program is your gateway to a promising career in IT: Data Analysis, Data Science & Data Protection sector. Why Choose Us? We take great pride in offering you a great learning experience that stands out. When you consider enrolling in our IT: Data Analysis, Data Science & Data Protection program, you're making a decision that will positively impact your career and knowledge in various aspects related to IT: Data Analysis, Data Science & Data Protection. Here's why choosing us is a smart choice: Updated Materials: We're committed to providing the most up-to-date learning materials. Our dedicated team continuously reviews and updates our content, ensuring you're always learning from the latest sources. When you choose us, you select the most current and relevant information, giving you the edge in your IT career. Flexible Timing: We understand that life can get busy, and you may have existing commitments that can make pursuing further education challenging. That's why we offer flexibility in your study schedule. With our courses, you can learn at your own pace, on your terms. You're in control and can adjust your learning to fit your life. No Hidden Cost: When choosing our program, you won't incur additional expenses. The certification and course materials are all-inclusive within the program's price. You can focus on your studies without worrying about hidden fees. Money-Back Guarantee: Your satisfaction is our top priority. We're so confident in the quality of our courses that we back them up with a 14-day money-back guarantee. 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This IT: Data Analysis, Data Science & Data Protection program is suitable for: * Aspiring IT: Data Analysis, Data Science & Data Protection professionals. * Students and recent graduates looking to enter the field. * Career changers interested in data analytics. * Security professionals seeking to upskill in data security. * Anyone interested in learning about IT: Data Analysis, Data Science & Data Protection. REQUIREMENTS No prior experience is required in our IT: Data Analysis, Data Science & Data Protection program. CAREER PATH Upon completing the program, you'll get edges in various IT: Data Analysis, data science & data protection-related jobs including: * Data Analyst: £25,000 - £45,000 * Business Intelligence Analyst: £30,000 - £50,000 * Data Scientist: £35,000 - £60,000 * Machine Learning Engineer: £40,000 - £70,000 * SQL Developer: £30,000 - £55,000 * Tableau Developer: £35,000 - £60,000 * Power BI Developer: £35,000 - £60,000 CERTIFICATES CPD ACCREDITED (E-CERTIFICATE) Digital certificate - Included CPD ACCREDITED (HARD COPY CERTIFICATE) Hard copy certificate - Included E-TRANSCRIPT Digital certificate - Included HARD COPY TRANSCRIPT Hard copy certificate - Included STUDENT ID CARD Digital certificate - Included