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Programming with Python
By Xpert Learning
About Course Master the Fundamentals of Programming with Python Course Description Embark on an exciting journey into the world of programming with this comprehensive Python course, designed to equip you with the essential skills and knowledge to become a proficient Python programmer. Whether you're a complete beginner or seeking to enhance your existing Python skills, this course caters to all levels of expertise. What will be discussed in detail? Introduction to Python: Delve into the basics of Python programming, including variables, data types, operators, and control flow statements. Working with Data Types: Explore the fundamental data types in Python, including numbers, strings, booleans, and lists. Python Strings: Master the art of manipulating strings, including slicing, concatenation, and string formatting techniques. Python Lists: Discover the power of lists, one of Python's most versatile data structures, and learn how to create, access, modify, and iterate over lists. Python Casting and Input: Understand the concept of type casting and learn how to take user input from the console. Python Dictionary: Uncover the usefulness of dictionaries, another essential data structure in Python, and learn how to store and retrieve data using key-value pairs. Python Date and Time: Learn how to handle date and time operations in Python, including creating, formatting, and manipulating date and time objects. Python Loop Part 1: Master the 'for' loop, a fundamental looping construct in Python, to iterate over sequences and perform repetitive tasks. Python Loop Part 2: Expand your understanding of loops by exploring the 'while' loop, used to execute a block of code repeatedly while a condition remains true. Creating a Function: Discover the power of functions, reusable blocks of code that perform specific tasks, and learn how to define, call, and pass arguments to functions. Python OOP Part 1: Delve into the world of Object-Oriented Programming (OOP) with Python, and learn the concepts of classes, objects, inheritance, and polymorphism. Python OOP Part 2: Enhance your OOP skills by exploring advanced concepts such as abstract classes, multiple inheritance, and operator overloading. Python Advanced OOP Part 1: Discover more advanced OOP techniques, including class methods, static methods, and decorators. Python Advanced OOP Part 2: Master the concept of exception handling, a crucial aspect of robust programming, and learn how to handle errors and exceptions effectively. Error Handling: Understand the importance of error handling in Python programming and learn how to identify, handle, and prevent errors. Python File Handling: Learn how to read, write, and manipulate files in Python, enabling you to store and retrieve data from external sources. Python Modules: Explore the concept of modules, reusable code libraries, and discover how to import, use, and create your own modules. Why should you enroll into it? Gain a comprehensive understanding of Python programming: Master the fundamentals of Python programming, from basic syntax to advanced OOP concepts. Develop practical coding skills: Apply your theoretical knowledge to hands-on coding exercises, solidifying your understanding and building your confidence. Prepare for a career in programming: Equip yourself with the essential skills required for entry-level programming positions. Enhance your problem-solving abilities: Learn to think algorithmically and develop effective problem-solving techniques using Python programming. Expand your skillset and knowledge: Whether you're a beginner or an experienced programmer, this course will broaden your understanding of Python and its capabilities. What will be taught? (Learning Outcomes/Learning Objectives) Understand the fundamental concepts of Python programming Work with different data types, including numbers, strings, lists, dictionaries, and Booleans Master control flow statements such as 'if', 'elif', and 'else' Create and manipulate Python functions Implement Object-Oriented Programming (OOP) concepts using classes, objects, inheritance, and polymorphism Handle errors and exceptions effectively Read, write, and manipulate files in Python Import, use, and create Python modules What Will You Learn? Understand the fundamental concepts of Python programming Work with different data types, including numbers, strings, lists, dictionaries, and Booleans Master control flow statements such as 'if', 'elif', and 'else' Create and manipulate Python functions Implement Object-Oriented Programming (OOP) concepts using classes, objects, inheritance, and polymorphism Handle errors and exceptions effectively Read, write, and manipulate files in Python Import, use, and create Python modules Course Content Introduction to Python Introduction to Python Working with Data Types Working with Data Types Python Strings Python Strings Python List Python List Python Casting and Input Python Casting and Input Python Dictionary Python Dictionary Python Date and Time Python Date and Time Python Loop (Part - 1) Python Loop (Part - 1) Python Loop (Part - 2) Python Loop (Part - 2) Python While Loop Python While Loop Creating a Function Creating a Function Python OOP (Part - 1) Python OOP (Part - 1) Python OOP (Part - 2) Python OOP (Part - 2) Python Advanced OOP (Part - 1) Python Advanced OOP (Part - 1) Python Advanced OOP (Part - 2) Python Advanced OOP (Part - 2) Error Handling Error Handling Python File Handling Python File Handling Python Modules Python Modules A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsA basic understanding of computers and operating systemsA willingness to learn and practice codingA computer with internet access and the ability to install Python Audience Beginners with no prior programming experience Programmers seeking to transition to Python Individuals looking to enhance their programming skills and knowledge Anyone interested in pursuing a career in programming Audience Beginners with no prior programming experience Programmers seeking to transition to Python Individuals looking to enhance their programming skills and knowledge Anyone interested in pursuing a career in programming
AS Level Computer Science
By Spark Generation
Embark on a journey into the world of technology with Spark Generation! Learn the fundamentals of computer science, coding languages, and algorithmic thinking. Discover the logic behind programs and explore the creative potential of digital innovation.
A2 Level Computer Science
By Spark Generation
Embark on a journey into the world of technology with Spark Generation and our Cambridge self-paced courses! Learn the fundamentals of computer science, coding languages, and algorithmic thinking. Discover the logic behind programs and explore the creative potential of digital innovation.
Data Science Projects with Python
By Nexus Human
Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client
Machine Learning Essentials with Python (TTML5506-P)
By Nexus Human
Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.
Hands-on Predicitive Analytics with Python (TTPS4879)
By Nexus Human
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.