Exclusive SALE Offer Today

Python: Machine Learning and Data Science

Best Seller 84 Lectures 11h 57m 46s
Prepare for your Microsoft examination with our training course. The No_code course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft No_code test with flying colors.
$14.99$24.99
Curriculum For This Course
  • 1. Types of Data 6m 58s
  • 2. Mean, Median, Mode 5m 26s
  • 3. [Activity] Using mean, median, and mode in Python 8m 30s
  • 4. [Activity] Variation and Standard Deviation 11m 12s
  • 5. Probability Density Function; Probability Mass Function 3m 27s
  • 6. Common Data Distributions 7m 45s
  • 7. [Activity] Percentiles and Moments 12m 33s
  • 8. [Activity] A Crash Course in matplotlib 13m 46s
  • 9. [Activity] Covariance and Correlation 11m 31s
  • 10. [Exercise] Conditional Probability 10m 16s
  • 11. Exercise Solution: Conditional Probability of Purchase by Age 2m 18s
  • 12. Bayes' Theorem 5m 23s
  • 1. [Activity] Linear Regression 11m 1s
  • 2. [Activity] Polynomial Regression 8m 4s
  • 3. [Activity] Multivariate Regression, and Predicting Car Prices 9m 53s
  • 4. Multi-Level Models 4m 36s
  • 1. Supervised vs 8m 57s
  • 2. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 5m 47s
  • 3. Bayesian Methods: Concepts 3m 59s
  • 4. [Activity] Implementing a Spam Classifier with Naive Bayes 8m 5s
  • 5. K-Means Clustering 7m 23s
  • 6. [Activity] Clustering people based on income and age 5m 14s
  • 7. Measuring Entropy 3m 9s
  • 8. Decision Trees: Concepts 8m 43s
  • 9. [Activity] Decision Trees: Predicting Hiring Decisions 9m 47s
  • 10. Ensemble Learning 5m 59s
  • 11. Support Vector Machines (SVM) Overview 4m 27s
  • 12. [Activity] Using SVM to cluster people using scikit-learn 5m 36s
  • 1. User-Based Collaborative Filtering 7m 57s
  • 2. Item-Based Collaborative Filtering 8m 15s
  • 3. [Activity] Finding Movie Similarities 9m 8s
  • 4. [Activity] Improving the Results of Movie Similarities 7m 59s
  • 5. [Activity] Making Movie Recommendations to People 10m 22s
  • 6. [Exercise] Improve the recommender's results 5m 29s
  • 1. K-Nearest-Neighbors: Concepts 3m 44s
  • 2. [Activity] Using KNN to predict a rating for a movie 12m 29s
  • 3. Dimensionality Reduction; Principal Component Analysis 5m 44s
  • 4. [Activity] PCA Example with the Iris data set 9m 5s
  • 5. Data Warehousing Overview: ETL and ELT 9m 5s
  • 6. Reinforcement Learning 12m 44s
  • 1. Bias/Variance Tradeoff 6m 15s
  • 2. [Activity] K-Fold Cross-Validation to avoid overfitting 10m 55s
  • 3. Data Cleaning and Normalization 7m 10s
  • 4. [Activity] Cleaning web log data 10m 56s
  • 5. Normalizing numerical data 3m 22s
  • 6. [Activity] Detecting outliers 7m
  • 1. [Activity] Installing Spark - Part 1 7m 2s
  • 2. [Activity] Installing Spark - Part 2 13m 29s
  • 3. Spark Introduction 9m 10s
  • 4. Spark and the Resilient Distributed Dataset (RDD) 11m 42s
  • 5. Introducing MLLib 5m 9s
  • 6. [Activity] Decision Trees in Spark 16m
  • 7. [Activity] K-Means Clustering in Spark 11m 7s
  • 8. TF / IDF 6m 44s
  • 9. [Activity] Searching Wikipedia with Spark 8m 11s
  • 10. [Activity] Using the Spark 2.0 DataFrame API for MLLib 7m 57s
  • 1. A/B Testing Concepts 8m 23s
  • 2. T-Tests and P-Values 5m 59s
  • 3. [Activity] Hands-on With T-Tests 6m 4s
  • 4. Determining How Long to Run an Experiment 3m 24s
  • 5. A/B Test Gotchas 9m 26s
  • 1. Deep Learning Pre-Requisites 10m 51s
  • 2. The History of Artificial Neural Networks 11m 15s
  • 3. [Activity] Deep Learning in the Tensorflow Playground 12m
  • 4. Deep Learning Details 9m 29s
  • 5. Introducing Tensorflow 12m 39s
  • 6. [Activity] Using Tensorflow, Part 1 9m 37s
  • 7. [Activity] Using Tensorflow, Part 2 13m 27s
  • 8. [Activity] Introducing Keras 14m 22s
  • 9. [Activity] Using Keras to Predict Political Affiliations 12m 30s
  • 10. Convolutional Neural Networks (CNN's) 11m 28s
  • 11. [Activity] Using CNN's for handwriting recognition 8m 15s
  • 12. Recurrent Neural Networks (RNN's) 11m 2s
  • 13. [Activity] Using a RNN for sentiment analysis 10m 15s
  • 14. The Ethics of Deep Learning 11m 2s
  • 15. Learning More about Deep Learning 1m 45s
  • 1. Your final project assignment 6m 26s
  • 2. Final project review 8m 59s

Hot Exams

How to Open Test Engine .dumpsarena Files

Use FREE DumpsArena Test Engine player to open .dumpsarena files

DumpsArena Test Engine

Windows

Refund Policy
Refund Policy

DumpsArena.com has a remarkable success record. We're confident of our products and provide a no hassle refund policy.

How our refund policy works?

safe checkout

Your purchase with DumpsArena.com is safe and fast.

The DumpsArena.com website is protected by 256-bit SSL from Cloudflare, the leader in online security.

Need Help Assistance?

Customer Support