Data Science and Machine Learning with Python - Hands On! скачать в хорошем качестве

Data Science and Machine Learning with Python - Hands On!

Perform data mining and Machine Learning efficiently using Python and Spark Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Learning Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using Decision Trees and Random Forests Visualize the results of your analysis using Python’s Matplotlib library Visualize the results of your analysis using Python’s Matplotlib library About The job of a data scientist is one of the most lucrative jobs out there today – it involves analyzing large amounts of data, and gathering actionable business insights from it using a variety of tools. This course will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. You don’t have to be an expert coder in Python to get the most out of this course – just a basic programming knowledge of Python is sufficient. Getting Started Introduction [Activity] Getting What You Need [Activity] Installing Enthought Canopy Python Basics – Part 1 [Activity] Python Basics – Part 2 Running Python Scripts Statistics and Probability Refresher, and Python Practise Types of Data Mean, Median, and Mode [Activity] Using Mean, Median, and Mode in Python [Activity] Variation and Standard Deviation Probability Density Function and Probability Mass Function Common Data Distributions [Activity] Percentiles and Moments [Activity] A Crash Course in matplotlib [Activity] Covariance and Correlation [Exercise] Conditional Probability Exercise Solution – Conditional Probability of Purchase by Age Bayes' Theorem Predictive Models [Activity] Linear Regression [Activity] Polynomial Regression [Activity] Multivariate Regression and Predicting Car Prices Multi-Level Models Machine Learning with Python Supervised versus Unsupervised Learning and Train/Test [Activity] Using Train/Test to Prevent Overfitting of a Polynomial Regression Bayesian Methods – Concepts [Activity] Implementing a Spam Classifier with Naive Bayes K-Means Clustering [Activity] Clustering People Based on Income and Age Measuring Entropy Decision Trees – Concepts [Activity] Decision Trees – Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview [Activity] Using SVM to Cluster People by using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering [Activity] Finding Movie Similarities [Activity] Improving the Results of Movie Similarities [Activity] Making Movie Recommendations to People [Exercise] Improve the Recommender's Results More Data Mining and Machine Learning Techniques K-Nearest Neighbors – Concepts [Activity] Using KNN to predict a rating for a movie Dimensionality Reduction and Principal Component Analysis [Activity] A PCA Example with the Iris Dataset Data Warehousing Overview – ETL and ELT Reinforcement Learning Dealing with Real-World Data Bias/Variance Trade-off [Activity] K-Fold Cross-Validation to Avoid Overfitting Data Cleaning and Normalization [Activity] Cleaning Web Log Data Normalizing Numerical Data [Activity] Detecting Outliers Apache Spark – Machine Learning on Big Data [Activity] Installing Spark – Part 1 [Activity] Installing Spark – Part 2 Spark Introduction Spark and the Resilient Distributed Dataset (RDD) Introducing MLLib [Activity] Decision Trees in Spark [Activity] K-Means Clustering in Spark TF/IDF [Activity] Searching Wikipedia with Spark [Activity] Using the Spark 2.0 DataFrame API for MLLib Experimental Design A/B Testing Concepts T-Tests and P-Values [Activity] Hands On with T-Tests Determining How Long to Run an Experiment A/B Test Gotchas You Made It! More to Explore