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!
“Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R”
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课程内容
Introduction Setting up Python and Jupyter Notebook Setting up R Studio and R crash course Basics of Statistics Introduction to Machine Learning Data Preprocessing Linear Regression Introduction to the classification Models Logistic Regression Linear Discriminant Analysis (LDA) K-Nearest Neighbors classifier Comparing results from 3 models Simple Decision Trees Simple Classification Tree Ensemble technique 1 – Bagging Ensemble technique 2 – Random Forests Ensemble technique 3 – Boosting Support Vector Machines Support Vector Classifier Support Vector Machines Creating Support Vector Machine Model in Python Creating Support Vector Machine Model in R Introduction – Deep Learning Neural Networks – Stacking cells to create network ANN in Python ANN in R CNN – Basics Creating CNN model in Python Creating CNN model in R Project : Creating CNN model from scratch in Python Project : Creating CNN model from scratch Project : Data Augmentation for avoiding overfitting Transfer Learning : Basics Transfer Learning in R Time Series Analysis and Forecasting Time Series – Preprocessing in Python Time Series – Important Concepts Time Series – Implementation in Python Time Series – ARIMA model Time Series – SARIMA model Congratulations & About your certificate
“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!”
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
“A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.”
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课程内容
“Introduction Core Concepts Impacts, Importance and examples The Machine Learning Process How to apply Machine Learning for Data Science Conclusion Section 1 -Bonus course – Machine Learning in Python and Jupyter for Beginners Section 2 -Bonus course – Machine Learning in Python and Jupyter for Beginners Section 3 – Bonus course – Machine Learning in Python and Jupyter for Beginners Section 4 – Bonus course – Machine Learning in Python and Jupyter for Beginners Section 5 -Bonus course – Machine Learning in Python and Jupyter for Beginners Section 6 – Bonus course – Machine Learning in Python and Jupyter for Beginners Section 7 -Bonus course – Machine Learning in Python and Jupyter for Beginners Bonus Content”
“Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!”
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课程内容
Introduction Getting Started with Anaconda Regression Classification Support Vector Machine (SVM) Tree Ensemble Machine Learning k-Nearest Neighbours (kNN) Unsupervised Learning: Dimensionality Reduction Unsupervised Learning: Clustering Deep Learning Appendix A1: Foundations of Deep Learning Computer Vision and Convolutional Neural Network (CNN)
“Learn the latest Big Data technology – Spark and Scala, including Spark 2.0 DataFrames!”
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课程内容
Course Introduction Scala IDE Options and Overview Windows Scala and Spark Set-up and Installation Mac OS Setup and Installation Linux (Ubuntu) Setup and Installation Scala Programming: Level One Collections Scala Programming: Level Two Spark DataFrames with Scala Introduction to Machine Learning Regression with Spark Classification with Spark Model Evaluation Clustering with Spark PCA with Spark DataBricks and Spark BONUS SECTION: THANK YOU!
“Data science, machine learning, and artificial intelligence in Python for students and professionals”
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课程内容
Welcome 1-D Linear Regression: Theory and Code Multiple linear regression and polynomial regression Practical machine learning issues Conclusion and Next Steps Setting Up Your Environment (FAQ by Student Request) Extra Help With Python Coding for Beginners (FAQ by Student Request) Effective Learning Strategies for Machine Learning (FAQ by Student Request) Appendix / FAQ Finale
Hands on AWS ML SageMaker Course with Practice Test. Join Live Study Group Q&A!
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课程内容
Introduction and Housekeeping SageMaker Housekeeping Machine Learning Concepts Model Performance Evaluation SageMaker Service Overview SageMaker Service and SDK Changes XGBoost – Gradient Boosted Trees Invoke Model Endpoint from External Clients Endpoint Changes with Zero Downtime Emerging AI Trends and Social Issues Cloud Security and Access Management Principal Component Analysis (PCA) Recommender Systems – Factorization Machines Model Optimization and HyperParameter Tuning Time Series Forecasting – DeepAR Anomaly Detection – Random Cut Forest Artificial Intelligence (AI) Services S3 Data Lake Architecture – Data Consolidation Deep Learning and Neural Networks Bring Your Own Algorithm Storage for Servers AWS – Support Plans and Feedback Databases on AWS On-Premises usage and other technologies Practice Exam – AWS Certified Machine Learning Specialty Other Resources
有数以千计的在线课程和课程可以帮助您提高 机器学习 技能并获得 机器学习 证书。