Python: Machine Learning and Data Science

What’s included

$14.99 / $24.99

Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.

4.59

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Video Courses

Getting Started

Lectures	Duration
1. [Activity] Getting What You Need	5m 10s
2. [Activity] Installing Enthought Canopy	15m 58s
3. Python Basics, Part 1 [Optional]	9m 41s
4. [Activity] Python Basics, Part 2 [Optional]	3m 55s
5. Running Python Scripts [Optional]	10m 15s
6. Introducing the Pandas Library [Optional]	10m 14s

1. [Activity] Getting What You Need

5m 10s

2. [Activity] Installing Enthought Canopy

15m 58s

3. Python Basics, Part 1 [Optional]

9m 41s

4. [Activity] Python Basics, Part 2 [Optional]

3m 55s

5. Running Python Scripts [Optional]

10m 15s

6. Introducing the Pandas Library [Optional]

10m 14s

Statistics and Probability Refresher, and Python Practise

Lectures	Duration
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. 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

Predictive Models

Lectures	Duration
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. [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

Machine Learning with Python

Lectures	Duration
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. 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

Recommender Systems

Lectures	Duration
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. 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

More Data Mining and Machine Learning Techniques

Lectures	Duration
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. 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

Dealing with Real-World Data

Lectures	Duration
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. 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

Apache Spark: Machine Learning on Big Data

Lectures	Duration
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. [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

Experimental Design

Lectures	Duration
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. 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

Deep Learning and Neural Networks

Lectures	Duration
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. 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

Final Project

Lectures	Duration
1. Your final project assignment	6m 26s
2. Final project review	8m 59s

1. Your final project assignment

6m 26s

2. Final project review

8m 59s