Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
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课程内容
Welcome to the course! Here we will help you get started in the best conditions. ——————– Part 1: Data Preprocessing ——————– Data Preprocessing in Python Data Preprocessing in R ——————– Part 2: Regression ——————– Simple Linear Regression Multiple Linear Regression Polynomial Regression Support Vector Regression (SVR) Decision Tree Regression Random Forest Regression Evaluating Regression Models Performance Regression Model Selection in Python Regression Model Selection in R ——————– Part 3: Classification ——————– Logistic Regression K-Nearest Neighbors (K-NN) Support Vector Machine (SVM) Kernel SVM Naive Bayes Decision Tree Classification Random Forest Classification Classification Model Selection in Python Evaluating Classification Models Performance ——————– Part 4: Clustering ——————– K-Means Clustering Hierarchical Clustering ——————– Part 5: Association Rule Learning ——————– Apriori Eclat ——————– Part 6: Reinforcement Learning ——————– Upper Confidence Bound (UCB) Thompson Sampling ——————– Part 7: Natural Language Processing ——————– ——————– Part 8: Deep Learning ——————– Artificial Neural Networks Convolutional Neural Networks ——————– Part 9: Dimensionality Reduction ——————– Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Kernel PCA ——————– Part 10: Model Selection & Boosting ——————– Model Selection XGBoost Bonus Lectures
“Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”
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课程内容
Course Introduction Environment Set-Up Jupyter Overview Python Crash Course Python for Data Analysis – NumPy Python for Data Analysis – Pandas Python for Data Analysis – Pandas Exercises Python for Data Visualization – Matplotlib Python for Data Visualization – Seaborn Python for Data Visualization – Pandas Built-in Data Visualization Python for Data Visualization – Plotly and Cufflinks Python for Data Visualization – Geographical Plotting Data Capstone Project Introduction to Machine Learning Linear Regression Cross Validation and Bias-Variance Trade-Off Logistic Regression K Nearest Neighbors Decision Trees and Random Forests Support Vector Machines K Means Clustering Principal Component Analysis Recommender Systems Natural Language Processing Neural Nets and Deep Learning Big Data and Spark with Python BONUS SECTION: THANK YOU!
“Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning”
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课程内容
Part 1: Introduction The Field of Data Science – The Various Data Science Disciplines The Field of Data Science – Connecting the Data Science Disciplines The Field of Data Science – The Benefits of Each Discipline The Field of Data Science – Popular Data Science Techniques The Field of Data Science – Popular Data Science Tools The Field of Data Science – Careers in Data Science The Field of Data Science – Debunking Common Misconceptions Part 2: Probability Probability – Combinatorics Probability – Bayesian Inference Probability – Distributions Probability – Probability in Other Fields Part 3: Statistics Statistics – Descriptive Statistics Statistics – Practical Example: Descriptive Statistics Statistics – Inferential Statistics Fundamentals Statistics – Inferential Statistics: Confidence Intervals Statistics – Practical Example: Inferential Statistics Statistics – Hypothesis Testing Statistics – Practical Example: Hypothesis Testing Part 4: Introduction to Python Python – Variables and Data Types Python – Basic Python Syntax Python – Other Python Operators Python – Conditional Statements Python – Python Functions Python – Sequences Python – Iterations Python – Advanced Python Tools Part 5: Advanced Statistical Methods in Python Advanced Statistical Methods – Linear Regression with StatsModels Advanced Statistical Methods – Multiple Linear Regression with StatsModels Advanced Statistical Methods – Linear Regression with sklearn Advanced Statistical Methods – Practical Example: Linear Regression Advanced Statistical Methods – Logistic Regression Advanced Statistical Methods – Cluster Analysis Advanced Statistical Methods – K-Means Clustering Advanced Statistical Methods – Other Types of Clustering Part 6: Mathematics Part 7: Deep Learning Deep Learning – Introduction to Neural Networks Deep Learning – How to Build a Neural Network from Scratch with NumPy Deep Learning – TensorFlow 2.0: Introduction Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks Deep Learning – Overfitting Deep Learning – Initialization Deep Learning – Digging into Gradient Descent and Learning Rate Schedules Deep Learning – Preprocessing Deep Learning – Classifying on the MNIST Dataset Deep Learning – Business Case Example Deep Learning – Conclusion Appendix: Deep Learning – TensorFlow 1: Introduction Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset Appendix: Deep Learning – TensorFlow 1: Business Case Software Integration Case Study – What’s Next in the Course? Case Study – Preprocessing the ‘Absenteeism_data’ Case Study – Applying Machine Learning to Create the ‘absenteeism_module’ Case Study – Loading the ‘absenteeism_module’ Case Study – Analyzing the Predicted Outputs in Tableau Appendix – Additional Python Tools Appendix – pandas Fundamentals Appendix – Working with Text Files in Python Bonus Lecture
“Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2”
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课程内容
Hit The Ground Running Core Programming Principles Fundamentals Of R Matrices Data Frames Advanced Visualization With GGPlot2 Homework Solutions Bonus Tutorials
“Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!”
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课程内容
Get Excited What is Data Science? ————————— Part 1: Visualisation ————————— Introduction to Tableau How to use Tableau for Data Mining Advanced Data Mining With Tableau ————————— Part 2: Modelling ————————— Stats Refresher Simple Linear Regression Multiple Linear Regression Logistic Regression Building a robust geodemographic segmentation model Assessing your model Drawing insights from your model Model maintenance ————————— Part 3: Data Preparation ————————— Business Intelligence (BI) Tools ETL Phase 1: Data Wrangling before the Load ETL Phase 2: Step-by-step guide to uploading data using SSIS Handling errors during ETL (Phases 1 & 2) SQL Programming for Data Science ETL Phase 3: Data Wrangling after the load Handling errors during ETL (Phase 3) ————————— Part 4: Communication ————————— Working with people Presenting for Data Scientists Homework Solutions Bonus Lectures
“Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks”
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课程内容
“Getting Started Statistics and Probability Refresher, and Python Practice Predictive Models Machine Learning with Python Recommender Systems More Data Mining and Machine Learning Techniques Dealing with Real-World Data Apache Spark: Machine Learning on Big Data Experimental Design / ML in the Real World Deep Learning and Neural Networks Generative Models Final Project You made it!”
“Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis”
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课程内容
“Introduction Sample or population data? The fundamentals of descriptive statistics Measures of central tendency, asymmetry, and variability Practical example: descriptive statistics Distributions Estimators and estimates Confidence intervals: advanced topics Practical example: inferential statistics Hypothesis testing: Introduction Hypothesis testing: Let’s start testing! Practical example: hypothesis testing The fundamentals of regression analysis Subtleties of regression analysis Assumptions for linear regression analysis Dealing with categorical data Practical example: regression analysis Bonus lecture”
Learn how to use the R programming language for data science and machine learning and data visualization!
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课程内容
Course Introduction Course Best Practices Windows Installation Set-Up Mac OS Installation Set-Up Linux Installation Development Environment Overview Introduction to R Basics R Matrices R Data Frames R Lists Data Input and Output with R R Programming Basics Advanced R Programming Data Manipulation with R Data Visualization with R Data Visualization Project Interactive Visualizations with Plotly Capstone Data Project Introduction to Machine Learning with R Machine Learning with R – Linear Regression Machine Learning Project – Linear Regression Machine Learning with R – Logistic Regression Machine Learning Project – Logistic Regression Machine Learning with R – K Nearest Neighbors Machine Learning Project – K Nearest Neighbors Machine Learning with R – Decision Trees and Random Forests Machine Learning Project – Decision Trees and Random Forests Machine Learning with R – Support Vector Machines Machine Learning Project – Support Vector Machines Machine Learning with R – K-means Clustering Machine Learning Project – K-means Clustering Machine Learning with R – Natural Language Processing Machine Learning with R – Neural Nets Machine Learning Project – Neural Nets Bonus Section
“Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!”
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课程内容
“Introduction Machine Learning 101 Machine Learning and Data Science Framework The 2 Paths Data Science Environment Setup Pandas: Data Analysis NumPy Matplotlib: Plotting and Data Visualization Scikit-learn: Creating Machine Learning Models Supervised Learning: Classification + Regression Milestone Project 1: Supervised Learning (Classification) Milestone Project 2: Supervised Learning (Time Series Data) Data Engineering Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2 Storytelling + Communication: How To Present Your Work Career Advice + Extra Bits Learn Python Learn Python Part 2 Extra: Learn Advanced Statistics and Mathematics for FREE! Where To Go From Here? BONUS SECTION”
有数以千计的在线课程和课程可以帮助您提高 数据科学 技能并获得 数据科学 证书。