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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-python-for-dummies-the-complete-guide/
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课程名称：面向初学者的完整Python机器学习与数据科学 课程概述： 欢迎参加我的新课程《面向初学者的Python机器学习》。在这个课程中，我们将讨论人工智能、机器学习和深度学习神经网络等当前科技界常见且易混淆的术语。人工智能是一个广泛的科学领域，旨在使机器具备人类的智能，而机器学习和神经网络是其中的两个子集。 我们将重点探讨机器学习，其运作方式类似于人类的学习过程。通过经历多次试错，我们积累经验并逐渐学习如何解决问题。在课程中，我们将准备机器进行预测测试，类似于学校或大学的数学测试。 主要内容包括： - 系统与环境准备：安装Python及所需库（Anaconda）。 - Python与SciPy基础：快速学习Python、NumPy、Matplotlib和Pandas。 - 数据集加载与描述：从CSV/URL加载数据，进行数据维度、类型和统计的汇总。 - 数据可视化：生成单变量和多变量图。 - 数据预处理：数据变换、重新缩放和标准化。 - 特征选择与算法评估：使用不同技术评估机器学习算法的性能。 - 分类与回归算法：包括线性和非线性算法。 - 模型选择与优化：比较不同的机器学习模型，提升性能的方案。 - 机器学习模型的保存与加载。 - 实际案例研究：使用Pima印度糖尿病数据集、鸢尾花多类数据集和波士顿住房成本数据集进行预测。 学习此课程将使你在当前技术领域中具备竞争力，希望你能积极参与，祝学习顺利！
Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platformLets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning MechanismThen we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as 'Testing Input' and our answers as 'Predicted Output'. Later, our professor will evaluate these answers and compare it with its actual answers, we call the actual answers as 'Test Output'. Then a mark will be given on basis of the correct answers. We call this mark as our 'Accuracy'. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures.Here are the major topics that are included in this course. We are using Python as our programming language. Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python really makes things easy.These are the main topics that are included in our courseSystem and Environment preparation------------------Installing Python and Required Libraries (Anaconda)Basics of python and sci-py--------------Python, Numpy , Matplotlib and Pandas Quick CoursesLoad data set from csv / url---------------Load CSV data with Python, NumPY and PandasSummarize data with description----------------Peeking data, Data Dimensions, Data Types, Statistics, Class Distribution, Attribute Correlations, Univariate SkewSummarize data with visualization------------------Univariate, Multivariate PlotsPrepare data-------Data Transforms, Rescaling, Standardizing, Normalizing and BinarizationFeature selection - Automatic selection techniques------------------Univariate Selection, Recursive Feature Elimination, Principle Component Analysis and Feature ImportanceMachine Learning Algorithm Evaluation------------------Train and Test Sets, K-fold Cross Validation, Leave One Out Cross Validation, Repeated Random Test-Train Splits.Algorithm Evaluation Metrics---------------Classification Metrics - Classification Accuracy, Logarithmic Loss, Area Under ROC Curve, Confusion Matrix, Classification Report.Regression Metrics - Mean Absolute Error, Mean Squared Error, R 2.Spot-Checking Classification Algorithms------------------Linear Algorithms - Logistic Regression, Linear Discriminant Analysis.Non-Linear Algorithms - k-Nearest Neighbours, Naive Bayes, Classification and Regression Trees, Support Vector Machines.Spot-Checking Regression Algorithms------------------Linear Algorithms - Linear Regression, Ridge Regression, LASSO Linear Regression and Elastic Net Regression.Non-Linear Algorithms - k-Nearest Neighbours, Classification and Regression Trees, Support Vector Machines.Choose The Best Machine Learning Model------------------Compare Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbours, Classification and Regression Trees, Naive Bayes, Support Vector Machines.Automate and Combine Workflows with Pipeline------------------Data Preparation and Modelling PipelineFeature Extraction and Modelling PipelinePerformance Improvement with Ensembles------------------Voting EnsembleBagging: Bagged Decision Trees, Random Forest, Extra TreesBoosting: AdaBoost, Gradient BoostingPerformance Improvement with Algorithm Parameter Tuning----------------------------Grid Search Parameter Random Search Parameter TuningSave and Load (serialize and deserialize) Machine Learning Models------------------Using pickleUsing Joblibfinalize a machine learning project------------------steps For Finalizing classification models - pima indian datasetDealing with imbalanced class problemsteps For Finalizing multi class models - iris flower datasetsteps For Finalizing regression models - boston housing datasetPredictions and Case Studies--------------Case study 1: predictions using the Pima Indian Diabetes DatasetCase study: Iris Flower Multi Class DatasetCase study 2: the Boston Housing cost DatasetMachine Learning and Data Science is the most lucrative job in the technology arena now a days. Learning this course will make you equipped to compete in this area. Best wishes with your learning. Se you soon in the class room.
