McAfee-Secured Website

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:

Was 21.99 USD
Now 19.99 USD

Description

This course will improve your knowledge and skills required to pass Python: Machine Learning and Data Science exam.

Curriculum For This Course

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