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Experience the future of geographical analysis with our Machine Learning for Predictive Maps in Python and Leaflet course. Master the unique blend of programming, machine learning, and geographic information systems, all while honing your ability to predict and visualise spatial data in a powerful and effective way. This course offers you an unparalleled understanding of modern map creation, combined with the magic of prediction using machine learning models. Starting from the ground up, you'll be introduced to all the necessary setups and installations. After that, you will be diving into the depth of Django server-side code and front-end application code writing. The heart of the course lies in learning how to automate the machine learning pipeline, leading you to easily create predictive models.  Improve your maps with Leaflet programming, making your predictions accurate and also visually striking. By the end of this course, you will be armed with experience furnished by our comprehensive project source code and assignments, empowering you to drive data-driven decisions and insightful spatial analysis. Join us and map your way to success! Sign up today. Learning Outcomes:Upon completion of the Machine Learning course, you will be able to: Understand how to set up and install relevant software and libraries.Master Django server-side and application front-end code writing.Gain proficiency in the concepts and implementation of Machine Learning.Learn to automate Machine Learning pipelines for efficient workflows.Acquire skills in Leaflet programming for enhanced map visuals.Handle project source code effectively for real-world projects.Apply knowledge practically via assignments and gain experience. Who is this course for?This Machine Learning course is ideal for: Aspiring Data Scientists keen on harnessing geographical data.GIS professionals aiming to integrate Machine Learning into their skill set.Software Developers interested in creating geographically-focused applications.Analysts keen on enhancing their data visualisation skills with mapping. CertificationAfter studying the course materials of the Machine Learning for Predictive Maps in Python and Leaflet course, there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test, you have a range of certification options to choose from. You can claim a CPD Accredited PDF Certificate for £4.99, a CPD Accredited Hardcopy Certificate at £8.00, or you may choose to receive a PDF Transcript for £4.99 or a Hardcopy Transcript for £9.99. Select according to your needs, and we assure timely delivery of your chosen certificate. RequirementsThis professionally designed Machine Learning for Predictive Maps in Python and Leaflet course does not require you to have any prior qualifications or experience. It is open to everyone. You will be able to access the course from anywhere at any time. Just enrol and start learning! Career Path:Our Machine Learning course will help you to pursue a range of career paths, such as: Junior Data Analyst: £25,000 - £35,000 annually.Data Scientist: £40,000 - £60,000 annually.GIS Analyst: £30,000 - £45,000 annually.Geospatial Software Developer: £35,000 - £55,000 annually.Machine Learning Engineer: £50,000 - £80,000 annually.Lead Data Scientist (GIS speciality): £70,000 - £100,000+ annually. Course Curriculum Section 01: Introduction Introduction 00:10:00 Section 02: Setup and Installations Python Installation 00:04:00 Creating a Python Virtual Environment 00:07:00 Installing Django 00:09:00 Installing Visual Studio Code IDE 00:06:00 Installing PostgreSQL Database Server Part 1 00:03:00 Installing PostgreSQL Database Server Part 2 00:09:00 Section 03: Writing the Django Server-Side Code Adding the settings.py Code 00:07:00 Creating a Django Model 00:10:00 Adding the admin.py Code 00:21:00 Section 04: Writing the Application Front-end Code Creating Template Files 00:10:00 Creating Django Views 00:10:00 Creating URL Patterns for the REST API 00:09:00 Adding the index.html code 00:04:00 Adding the layout.html code 00:19:00 Creating our First Map 00:10:00 Adding Markers 00:16:00 Section 05: Machine Learning Installing Jupyter Notebook 00:07:00 Data Pre-processing 00:31:00 Model Selection 00:20:00 Model Evaluation and Building a Prediction Dataset 00:11:00 Section 06: Automating the Machine Learning Pipeline Creating a Django Model 00:04:00 Embedding the Machine Learning Pipeline in the Application 00:42:00 Creating a URL Endpoint for our Prediction Dataset 00:06:00 Section 07: Leaflet Programming Creating Multiple Basemaps 00:09:00 Creating the Marker Layer Group 00:10:00 Creating the Point Layer Group 00:12:00 Creating the Predicted Point Layer Group 00:07:00 Creating the Predicted High Risk Point Layer Group 00:12:00 Creating the Legend 00:09:00 Creating the Prediction Score Legend 00:15:00 Section 08: Project Source Code Resource 00:00:00 Assignment Assignment - Machine Learning for Predictive Maps in Python and Leaflet 00:00:00

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

Requirements: No previous Korean knowledge is required. This course is recommended to all ages, everyone who wants to learn Korean fast at home. This course is designed for absolute Korean beginners. A willingness to improve your Korean language ability through practice. Students will need good internet connection and gadgets like pc, tablet, or smartphone. A language notebook is required to do your homework in your own Korean language. Hello! Welcome to my basic Korean course number 1 series. Learn Korean Fast at Home is an online basic Korean course from a native Korean teacher Stormi Kim who was born in Busan Korea. You will learn the four basic Korean Language skills that are fundamentals to Korean communication at home. (Listening ,Reading, Speaking, and Writing) If your goal is to make Korean friends or to have jog opportunities in Korea, this lecture will be the cornerstone of learning Korean language. This course will be helpful for students who prepare for Korean exam Topik. Moreover, anyone who is interested in studying new languages can have a good command of Korean through this course. This course is recommendable for : Students who want to start learning Korean with a native Korean speaker. People who are K-drama and K-pop lovers. Anyone who wants to have confidence in learning Korean Students who want to master basic Korean expressions for academic purpose such as preparing for Korean Topik test. People who want to make Korean friends. This course includes: Printable PDF Korean language worksheets for beginners. Basic Korean lessons (Korean alphabet, Korean sentence structure, Korean grammar, simple Korean conversation) Practical words and real-life photos about Korea. Interactive Lessons online via Instagram posts. Immediate feedback and comments on students' task. Future updates. Through this course, you will be able to : read and write Korean alphabet confidently. learn how to make Korean sentences in a correct order speak Korean words with pictures that show Korean culture learn Korean fast and easily with a lot of Korean quizzes and exercises use basic Korean conversational expressions naturally develop your Korean language 4 skills to the next level This basic online Korean class is divided into four sections so that you can learn Korean step by step and improve your Korean Language skills at your own pace. You will listen to my voice and practice Korean through a variety of Korean language exercises. At the end of each lesson, I recommend that you write Koreanstudygram on your Instagram with pictures which show that you study Korean hard. If you want to get instant answers and likes from me, subscribe to my SNS account called Cracking Korean. At the end of this online basic Korean course, you can write Korean consonants and vowels naturally and master constructing simple Korean sentences in your own Korean language ability. Additionally, I attached some learning materials about basic Korean skills and Korean keyboard for you so that you can practice your Korean language ability day bay day. Enroll now! And start the journey of learning Korean together. ♬Music by (HYP MUSIC) 발랄한 귀여운 브금 | 저작권 없는 음악 | Free BGM | HYP - Monkey Absolute Korean beginners Students who want to start learning Korean with a native Korean speaker K-drama and K-pop lovers Students who want to make Korean friends Students who want to master basic Korean expressions for academic purpose such as preparing for Korean Topik test   Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. Course Curriculum Supplementary Resources Supplementary Resources - Learn Korean Fast at Home ∥ Online Basic Korean Class 00:00:00 Learn Korean Fast at Home for Beginners-Introduction Introduction 00:05:00 Korean introduction 00:07:00 Korean Syllables 00:08:00 Learn Korean Vowels: Korean Short Vowels/ Korean Double Vowels Korean Short Vowels - Unrounded Vowels l, ㅔ, ㅐ, ㅡ, ㅓ, ㅏ 00:07:00 Korean Short Vowels - Rounded Vowels ㅟ, ㅚ, ㅜ ㅗ 00:06:00 [y] Soundlike Korean Double Vowels-ㅑ, ㅕ, ㅛ, ㅠ, ㅖ, ㅒ 00:07:00 [w], [i] soundlike Korean Double Vowels-ㅘ, ㅝ, ㅞ, ㅙ, ㅢ 00:06:00 Learn Korean Consonants/ Korean Final Consonants/ Korean Double Final Consonants Korean Consonants - ㄱ, ㄲ, ㅋ 00:06:00 Korean Consonants - ㄷ, ㄸ, ㅌ 00:06:00 Korean Consonants - ㅂ, ㅃ, ㅍ 00:05:00 Korean Consonants - ㅅ, ㅆ, ㅎ 00:05:00 Korean Consonants - ㅈ, ㅉ, ㅊ 00:05:00 Korean Consonants - ㅁ, ㄴ, ㅇ, ㄹ 00:07:00 Korean Final Consonants - [k ̚ ] 00:05:00 Korean Final Consonants - [t ̚ ] sound 00:05:00 Korean Final Consonants - [p ̚ ] 00:04:00 Korean Final Consonants - [n], [l], [m], [ŋ] 00:05:00 The Names of Korean Consonants+Review 00:06:00 Korean Double Final Consonants - [k ̚ ], [t ̚ ] sound 00:06:00 Korean Double Final Consonants - [p ̚ ], [l] sound 00:06:00 Korean Double Final Consonants - [m], [n] sound 00:04:00 Korean Sentence Order Korean Sentence Structure1 00:09:00 Korean Sentence Structure2 00:07:00 Korean Connective adverbs 00:11:00 korean Conversation + Grammar Pointing to Something or Someone - Korean Demonstrative Pronouns 00:10:00 My Favorite Korean Food - Korean Particles 이 , 가 00:10:00 Introduce Yourself - Korean Particles 은, 는 1 00:09:00 Nationality+jobs - Korean Particles 은, 는 2 00:08:00 Expressing Possessions - Korean Possessive Particles 의 00:08:00 Talking about What You Did - Korean Particles 을, 를 00:07:00 Expressing Existence & Action - Korean Particle 에, 에서 00:10:00 Korean Verbs - Creating Action Verbs 00:09:00 Korean Verbs - Creating Descriptive Verbs 00:06:00 Korean Greetings - Formal & Informal expressions 00:07:00 Korean Farewells - Formal & Informal expressions 00:05:00 Korean Numbers - Sino, Native Numbers 00:11:00 The Present + The Past Ongoing Event - Korean Progressives 00:08:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

This ultimate course to kickstart your Python journey from scratch. This comprehensive course covers all the essential concepts of Python, providing explanations, examples, and practical implementations. Designed with beginners in mind, our goal is to help you learn and master Python by building a variety of projects.

Take this tremendous opportunity to transform yourself into highly skilled and recognised python experts by enrolling on our

Learning Outcomes Get an introduction to Python programming Know how to do conditional branching with Python Deepen your knowledge of importing external/internal libraries in Python Learn about project rock, paper and scissors as well as strings operation, time and date in Python Acquire more knowledge about data storage structures, tuples, lists and dictionary Enhance your understanding of import tricks, import operating systems and platforms and exception handling in Python Learn how to instal Packages and Scheduling in Python Description Python is a highly multi-purposeful still easy-to-understand programming language, which is why it is more adaptable all over the world. Whether to make a web application in data science, software engineering, mobile app development, or artificial intelligence - every industry uses Python to accomplish its work. Therefore, if you are planning to pursue a career in these sectors, develop your Python skills with the Diploma In Python Programming course. We made this course with an aim of enhancing your programming language skills in Python and making you job ready. Therefore, this course includes some easy-to-digest modules on topics such as - conditional branching with Python, writing user functions in Python, file handling, reading and writing using Python and many more. Moreover, we will introduce you to the procedure of data storage structures, tuples, lists and dictionaries through Python. Further topics will be discussed in the modules for which you need to enrol in our comprehensive course. So, join this course now to acquire the exclusive knowledge of Python and a CPD certificate of achievement after completing this course. 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 finishing this course you will get the expertise to aim for a career in the following positions: Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst Course Content Unit 01: Introduction to Python Programming Module 01: Course Introduction 00:02:00 Unit 02: Getting Started with Python Module 01: Software Installation 00:02:00 Module 02: Hello World Program 00:06:00 Module 03: Input and Output 00:07:00 Module 04: Calculating Average of 5 Numbers 00:03:00 Unit 03: Conditional Branching with Python Module 01: If Loop In Python 00:06:00 Module 02: Program Using If Else part 1 00:03:00 Module 03: Program Using If Else part 2 00:08:00 Module 04: Program for Calculator 00:02:00 Module 05: Program Using For Loop 00:08:00 Module 06: For Table 00:05:00 Module 07: For loop and Mathematical Operator in Python 00:04:00 Module 08: Factorial of Number Using Python 00:06:00 Module 09: Program Using While 00:05:00 Module 10: While Loop Example 00:07:00 Module 11: Tasks for Practice 00:02:00 Unit 04: Importing external/internal library in python Module 01: Importing Library in Python 00:07:00 Unit 05: Project Rock Paper and Scissors Module 01: Rock Paper and Scissor Game 00:06:00 Unit 06: Strings Operation in Python Module 01: Program Using String part 1 00:05:00 Module 02: Program using String 2 00:06:00 Module 03: Program Using String 3 00:06:00 Module 04: Program Using String part 4 00:03:00 Unit 07: Date and time in Python Module 01: Use of Date and Time part 1 00:05:00 Module 02: Use of Date and Time part 2 00:05:00 Unit 08: File Handling, read and write using Python Module 01: File Handling Part 1 00:08:00 Module 02: File Handling Part 2 00:07:00 Unit 09: Data Storage Structures, Tuple, List and Dictionary Module 01: Tuple in Python Part 1 00:10:00 Module 02: Tuple in Python Part 2 00:07:00 Module 03: Using Lists part 1 00:07:00 Module 04: Using List part 2 00:12:00 Module 05: Using Lists part 3 mm 00:06:00 Module 06: Using Lists part 4 00:08:00 Module 07: Using Lists part 5 00:02:00 Module 08: Use of Dictionary Part 1 00:04:00 Module 09: Use of Dictionary Part 2 00:05:00 Module 10: Use of Dictionary Part 3 00:08:00 Module 11: Use of Dictionary Part 4 00:07:00 Unit 10: Writing user functions in Python Module 01: Function in Python Part 1 00:06:00 Module 02: Function in Python Part 2 00:05:00 Module 03: Function in Python Part 3 00:04:00 Module 04: Function in Python Part 4 00:07:00 Module 05: Function in Python Part 5 00:08:00 Unit 11: Sending mail Module 01: Send Email 00:09:00 Unit 12: Import Tricks in Python Module 01: Import Study part 1 00:07:00 Module 02: Import Study part 2 00:03:00 Unit 13: Import Operating System and Platform Module 01: Importing OS 00:06:00 Module 02: Import Platform 00:05:00 Unit 14: Exceptions handling in python Module 01: Exception in Python part 1 00:11:00 Module 02: Exception in Python part 2 00:07:00 Module 03: Exception in Python part 3 00:05:00 Unit 15: Installing Packages and Scheduling In Python Module 01: Installing Packages using built in package manager 00:08:00 Module 02: Scheduler in Python 00:05:00 Unit 16: Data Base In Python using sqlite Module 01: Data Base 1 00:08:00 Module 02: Data Base 2 00:09:00 Module 03: Data Base 3 00:08:00 Module 04: Data base 4 00:07:00 Module 05: Data Base 5 00:06:00 Unit 17: Running Program from Command Prompt and jupyter Notebook Module 01: IDE_1 00:05:00 Module 02: IDE_2 00:07:00 Unit 18: Conclusion Module 01: Conclusion 00:02:00 Resources Resources - Diploma in Python Programming 00:00:00 Recommended Materials Workbook - Diploma in Python Programming 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00

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Master the art of project planning, scheduling, and resource management with Oracle Primavera P6 PPM Professional. The course offers a streamlined approach to efficiently manage projects and includes workshops for intermediate-level users to apply their knowledge in practice.