Course name Software Engineering Courses Python: Machine Learning and Data Science: Python: Machine Learning and Data Science

Python: Machine Learning and Data Science Video Course

Python: Machine Learning and Data Science Video Course is developed by Software Engineering Courses Professionals to help you pass the Python: Machine Learning and Data Science exam.
You Will Learn:
Description
This course will improve your knowledge and skills required to pass Python: Machine Learning and Data Science exam.
Curriculum For This Course
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1. Getting Started 6 Videos 00:48:12
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[Activity] Getting What You Need 05:10
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[Activity] Installing Enthought Canopy 15:58
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Python Basics, Part 1 [Optional] 09:41
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[Activity] Python Basics, Part 2 [Optional] 03:55
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Running Python Scripts [Optional] 10:15
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Introducing the Pandas Library [Optional] 10:14
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2. Statistics and Probability Refresher, and Python Practise 12 Videos 01:39:05
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Types of Data 06:58
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Mean, Median, Mode 05:26
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[Activity] Using mean, median, and mode in Python 08:30
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[Activity] Variation and Standard Deviation 11:12
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Probability Density Function; Probability Mass Function 03:27
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Common Data Distributions 07:45
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[Activity] Percentiles and Moments 12:33
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[Activity] A Crash Course in matplotlib 13:46
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[Activity] Covariance and Correlation 11:31
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[Exercise] Conditional Probability 10:16
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Exercise Solution: Conditional Probability of Purchase by Age 02:18
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Bayes' Theorem 05:23
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3. Predictive Models 4 Videos 00:33:33
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[Activity] Linear Regression 11:01
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[Activity] Polynomial Regression 08:04
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[Activity] Multivariate Regression, and Predicting Car Prices 09:53
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Multi-Level Models 04:36
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4. Machine Learning with Python 12 Videos 01:17:06
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Supervised vs. Unsupervised Learning, and Train/Test 08:57
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[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47
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Bayesian Methods: Concepts 03:59
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[Activity] Implementing a Spam Classifier with Naive Bayes 08:05
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K-Means Clustering 07:23
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[Activity] Clustering people based on income and age 05:14
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Measuring Entropy 03:09
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Decision Trees: Concepts 08:43
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[Activity] Decision Trees: Predicting Hiring Decisions 09:47
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Ensemble Learning 05:59
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Support Vector Machines (SVM) Overview 04:27
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[Activity] Using SVM to cluster people using scikit-learn 05:36
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5. Recommender Systems 6 Videos 00:49:10
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User-Based Collaborative Filtering 07:57
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Item-Based Collaborative Filtering 08:15
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[Activity] Finding Movie Similarities 09:08
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[Activity] Improving the Results of Movie Similarities 07:59
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[Activity] Making Movie Recommendations to People 10:22
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[Exercise] Improve the recommender's results 05:29
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6. More Data Mining and Machine Learning Techniques 6 Videos 00:52:51
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K-Nearest-Neighbors: Concepts 03:44
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[Activity] Using KNN to predict a rating for a movie 12:29
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Dimensionality Reduction; Principal Component Analysis 05:44
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[Activity] PCA Example with the Iris data set 09:05
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Data Warehousing Overview: ETL and ELT 09:05
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Reinforcement Learning 12:44
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7. Dealing with Real-World Data 6 Videos 00:45:38
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Bias/Variance Tradeoff 06:15
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[Activity] K-Fold Cross-Validation to avoid overfitting 10:55
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Data Cleaning and Normalization 07:10
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[Activity] Cleaning web log data 10:56
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Normalizing numerical data 03:22
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[Activity] Detecting outliers 07:00
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8. Apache Spark: Machine Learning on Big Data 10 Videos 01:36:31
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[Activity] Installing Spark - Part 1 07:02
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[Activity] Installing Spark - Part 2 13:29
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Spark Introduction 09:10
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Spark and the Resilient Distributed Dataset (RDD) 11:42
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Introducing MLLib 05:09
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[Activity] Decision Trees in Spark 16:00
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[Activity] K-Means Clustering in Spark 11:07
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TF / IDF 06:44
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[Activity] Searching Wikipedia with Spark 08:11
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[Activity] Using the Spark 2.0 DataFrame API for MLLib 07:57
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9. Experimental Design 5 Videos 00:33:16
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A/B Testing Concepts 08:23
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T-Tests and P-Values 05:59
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[Activity] Hands-on With T-Tests 06:04
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Determining How Long to Run an Experiment 03:24
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A/B Test Gotchas 09:26
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10. Deep Learning and Neural Networks 15 Videos 02:39:12
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Deep Learning Pre-Requisites 10:51
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The History of Artificial Neural Networks 11:15
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[Activity] Deep Learning in the Tensorflow Playground 12:00
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Deep Learning Details 09:29
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Introducing Tensorflow 12:39
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[Activity] Using Tensorflow, Part 1 09:37
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[Activity] Using Tensorflow, Part 2 13:27
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[Activity] Introducing Keras 14:22
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[Activity] Using Keras to Predict Political Affiliations 12:30
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Convolutional Neural Networks (CNN's) 11:28
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[Activity] Using CNN's for handwriting recognition 08:15
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Recurrent Neural Networks (RNN's) 11:02
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[Activity] Using a RNN for sentiment analysis 10:15
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The Ethics of Deep Learning 11:02
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Learning More about Deep Learning 01:45
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11. Final Project 2 Videos 00:16:52
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Your final project assignment 06:26
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Final project review 08:59
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