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A warm welcome to the Machine Learning using Python: A Comprehensive Course by Uplatz.The Machine Learning with Python course aims to teach students/course participants some of the core ideas in machine learning, data science, and AI that will help them go from a real-world business problem to a first-cut, working, and deployable AI solution to the problem. Our main goal is to enable participants use the skills they acquire in this course to create real-world AI solutions. We'll aim to strike a balance between theory and practice, with a focus on the practical and applied elements of ML.This Python-based Machine Learning training course is designed to help you grasp the fundamentals of machine learning. It will provide you a thorough knowledge of Machine Learning and how it works. As a Data Scientist or Machine Learning engineer, you'll learn about the relevance of Machine Learning and how to use it in the Python programming language. Machine Learning Algorithms will allow you to automate real-life events. We will explore different practical Machine Learning use cases and practical scenarios at the end of this Machine Learning online course and will build some of them.In this Machine Learning course, you'll master the fundamentals of machine learning using Python, a popular programming language. Learn about data exploration and machine learning techniques such as supervised and unsupervised learning, regression, and classifications, among others. Experiment with Python and built-in tools like Pandas, Matplotlib, and Scikit-Learn to explore and visualize data. Regression, classification, clustering, and sci-kit learn are all sought-after machine learning abilities to add to your skills and CV. To demonstrate your competence, add fresh projects to your portfolio and obtain a certificate in machine learning.Machine Learning Certification training in Python will teach you about regression, clustering, decision trees, random forests, Nave Bayes, and Q-Learning, among other machine learning methods. This Machine Learning course will also teach you about statistics, time series, and the many types of machine learning algorithms, such as supervised, unsupervised, and reinforcement algorithms. You'll be solving real-life case studies in media, healthcare, social media, aviation, and human resources throughout the Python Machine Learning Training.Course Outcomes: After completion of this course, student will be able to:Understand about the roles & responsibilities that a Machine Learning Engineer playsPython may be used to automate data analysisExplain what machine learning isWork with data that is updated in real timeLearn about predictive modelling tools and methodologiesDiscuss machine learning algorithms and how to put them into practiceValidate the algorithms of machine learningExplain what a time series is and how it is linked to other ideasLearn how to conduct business in the future while living in the nowApply machine learning techniques on real world problem or to develop AI based applicationAnalyze and Implement Regression techniquesSolve and Implement solution of Classification problemUnderstand and implement Unsupervised learning algorithmsObjective: Learning basic concepts of various machine learning methods is primary objective of this course. This course specifically make student able to learn mathematical concepts, and algorithms used in machine learning techniques for solving real world problems and developing new applications based on machine learning.TopicsPython for Machine LearningIntroduction of Python for ML, Python modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from dataset, Future Scope of ML.Introduction to Machine LearningWhat is Machine Learning, Basic Terminologies of Machine Learning, Applications of ML, different Machine learning techniques, Difference between Data Mining and Predictive Analysis, Tools and Techniques of Machine Learning.Types of Machine LearningSupervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle.Supervised Learning: Classification and RegressionClassification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression.Unsupervised and Reinforcement LearningClustering: K-Means Clustering, Hierarchical clustering, Density-Based Clustering.Machine Learning - Course Syllabus1. Linear AlgebraBasics of Linear AlgebraApplying Linear Algebra to solve problems2. Python ProgrammingIntroduction to PythonPython data typesPython operatorsAdvanced data typesWriting simple Python programPython conditional statementsPython looping statementsBreak and Continue keywords in PythonFunctions in PythonFunction arguments and Function required argumentsDefault argumentsVariable argumentsBuild-in functionsScope of variablesPython Math modulePython Matplotlib moduleBuilding basic GUI applicationNumPy basicsFile systemFile system with statementFile system with read and writeRandom module basicsPandas basicsMatplotlib basicsBuilding Age Calculator app3. Machine Learning BasicsGet introduced to Machine Learning basicsMachine Learning basics in detail4. Types of Machine LearningGet introduced to Machine Learning typesTypes of Machine Learning in detail5. Multiple Regression6. KNN AlgorithmKNN introKNN algorithmIntroduction to Confusion MatrixSplitting dataset using TRAINTESTSPLIT7. Decision TreesIntroduction to Decision TreeDecision Tree algorithms8. Unsupervised LearningIntroduction to Unsupervised LearningUnsupervised Learning algorithmsApplying Unsupervised Learning9. AHC Algorithm10. K-means ClusteringIntroduction to K-means clusteringK-means clustering algorithms in detail11. DBSCANIntroduction to DBSCAN algorithmUnderstand DBSCAN algorithm in detailDBSCAN program

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