Great Start for Coding - Python Crash Course for Beginners | Free Udemy Course

This Python Crash Course is designed to foundations in order to write simple programs in Python . No previous knowledge to programming is needed. After Finishing this Course, you'll understand the benefits of programming in IT roles; be able to write simple programs using Python.What you will learnEverything you need know about PythonPython - a tool, not a reptileThere is more than one PythonLet's start our Python adventureYour first programPython literalsOperators - data manipulation toolsVariables - data-shaped boxesHow to talk to computer?Making decisions in PythonPython's loopsLogic and bit operations in PythonLists - collections of dataSorting simple lists - the bubble sort algorithmLists - some more detailsLists in advanced applicationsWriting functions in PythonHow functions communicate with their environment?Returning a result from a functionScopes in PythonLet's make some fun... sorry, functionsTuples and dictionariesUsing modulesSome useful modulesWhat is package?Errors - the programmer's daily breadThe anatomy of exceptionSome of the most useful exceptionsCharacters and strings vs. computersPython's nature of stringsString methodsStrings in actionand many more things This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, This is best course for any one who wants to start career in data science.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn libraryThis Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.Who this course is for:Beginner Python Developer

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