Data Academy Subscription > Machine Learning

The Machine Learning Workshop

Introduction to Scikit-Learn
1 hour 19 minutes
Clustering
1 hour 15 minutes
Key Concepts of Supervised Learning
1 hour 16 minutes
Supervised Learning Algorithms
49 minutes
Introduction to Artificial Neural Networks
50 minutes
Building A Trained Model
1 hour 9 minutes
Introduction to Scikit-Learn
1 hour 19 minutes
Clustering
1 hour 15 minutes
Key Concepts of Supervised Learning
1 hour 16 minutes
Supervised Learning Algorithms
49 minutes
Introduction to Artificial Neural Networks
50 minutes
Building A Trained Model
1 hour 9 minutes
Introduction to Scikit-Learn
1 hour 19 minutes
Clustering
1 hour 15 minutes
Key Concepts of Supervised Learning
1 hour 16 minutes
Supervised Learning Algorithms
49 minutes
Introduction to Artificial Neural Networks
50 minutes
Building A Trained Model
1 hour 9 minutes
Introduction to Scikit-Learn
1 hour 19 minutes
Clustering
1 hour 15 minutes
Key Concepts of Supervised Learning
1 hour 16 minutes
Supervised Learning Algorithms
49 minutes
Introduction to Artificial Neural Networks
50 minutes
Building A Trained Model
1 hour 9 minutes

The Supervised Learning Workshop

Introduction to Supervised Learning with Python
1 hour 3 minutes
Exploring and Visualizing Data with Python
1 hour 56 minutes
Performing Linear Regression with Python
2 hours 51 minutes
Performing Classification Tasks with Python
1 hour 39 minutes
Creating Ensemble Models with Python
1 hour 46 minutes
Evaluating Supervised Learning Models with Python
1 hour 12 minutes
Introduction to Supervised Learning with Python
1 hour 3 minutes
Exploring and Visualizing Data with Python
1 hour 56 minutes
Performing Linear Regression with Python
2 hours 51 minutes
Performing Classification Tasks with Python
1 hour 39 minutes
Creating Ensemble Models with Python
1 hour 46 minutes
Evaluating Supervised Learning Models with Python
1 hour 12 minutes
Introduction to Supervised Learning with Python
1 hour 3 minutes
Exploring and Visualizing Data with Python
1 hour 56 minutes
Performing Linear Regression with Python
2 hours 51 minutes
Performing Classification Tasks with Python
1 hour 39 minutes
Creating Ensemble Models with Python
1 hour 46 minutes
Evaluating Supervised Learning Models with Python
1 hour 12 minutes
Introduction to Supervised Learning with Python
1 hour 3 minutes
Exploring and Visualizing Data with Python
1 hour 56 minutes
Performing Linear Regression with Python
2 hours 51 minutes
Performing Classification Tasks with Python
1 hour 39 minutes
Creating Ensemble Models with Python
1 hour 46 minutes
Evaluating Supervised Learning Models with Python
1 hour 12 minutes

The Unsupervised Learning Workshop

Introduction to Clustering
45 minutes
Hierarchical Clustering
40 minutes
Neighborhood Approaches and DBSCAN
37 minutes
Dimensionality Reduction Techniques and PCA
1 hour 6 minutes
Autoencoders
1 hour 15 minutes
t-Distributed Stochastic Neighbor Embedding
50 minutes
Topic Modeling
2 hours 20 minutes
Market Basket Analysis
1 hour 15 minutes
Hotspot Analysis
1 hour
Introduction to Clustering
45 minutes
Hierarchical Clustering
40 minutes
Neighborhood Approaches and DBSCAN
37 minutes
Dimensionality Reduction Techniques and PCA
1 hour 6 minutes
Autoencoders
1 hour 15 minutes
t-Distributed Stochastic Neighbor Embedding
50 minutes
Topic Modeling
2 hours 20 minutes
Market Basket Analysis
1 hour 15 minutes
Hotspot Analysis
1 hour
Introduction to Clustering
45 minutes
Hierarchical Clustering
40 minutes
Neighborhood Approaches and DBSCAN
37 minutes
Dimensionality Reduction Techniques and PCA
1 hour 6 minutes
Autoencoders
1 hour 15 minutes
t-Distributed Stochastic Neighbor Embedding
50 minutes
Topic Modeling
2 hours 20 minutes
Market Basket Analysis
1 hour 15 minutes
Hotspot Analysis
1 hour
Introduction to Clustering
45 minutes
Hierarchical Clustering
40 minutes
Neighborhood Approaches and DBSCAN
37 minutes
Dimensionality Reduction Techniques and PCA
1 hour 6 minutes
Autoencoders
1 hour 15 minutes
t-Distributed Stochastic Neighbor Embedding
50 minutes
Topic Modeling
2 hours 20 minutes
Market Basket Analysis
1 hour 15 minutes
Hotspot Analysis
1 hour

The Reinforcement Learning Workshop

Introduction to Reinforcement Learning
1:16:00
Introduction to The Markov Decision Process and Dynamic Programming
0:50:45
Practice Deep Learning with TF2
1 hour 30 minutes
Getting Started with OpenAI and TensorFlow for RL
1:02:00
Introduction to Dynamic Programming
1:07:07
Introduction to Monte Carlo Methods
1:07:51
Introduction to Temporal-Difference Learning
1:34:06
Solving the Multi Armed Bandit Problem
1:23:00
Introduction to Deep Q Learning
1:40:57
Playing an Atari Game with Deep Recurrent Q Networks
0:54:00
Introduction to Policy Based Methods for Reinforcement Learning
1:02:23
Discussing Evolutionary Strategies for Reinforcement Learning
1:36:30
Discussing Advancements for Reinforcement Learning
1:49:39
Introduction to Reinforcement Learning
1:16:00
Introduction to The Markov Decision Process and Dynamic Programming
0:50:45
Practice Deep Learning with TF2
1 hour 30 minutes
Getting Started with OpenAI and TensorFlow for RL
1:02:00
Introduction to Dynamic Programming
1:07:07
Introduction to Monte Carlo Methods
1:07:51
Introduction to Temporal-Difference Learning
1:34:06
Solving the Multi Armed Bandit Problem
1:23:00
Introduction to Deep Q Learning
1:40:57
Playing an Atari Game with Deep Recurrent Q Networks
0:54:00
Introduction to Policy Based Methods for Reinforcement Learning
1:02:23
Discussing Evolutionary Strategies for Reinforcement Learning
1:36:30
Discussing Advancements for Reinforcement Learning
1:49:39
Introduction to Reinforcement Learning
1:16:00
Introduction to The Markov Decision Process and Dynamic Programming
0:50:45
Practice Deep Learning with TF2
1 hour 30 minutes
Getting Started with OpenAI and TensorFlow for RL
1:02:00
Introduction to Dynamic Programming
1:07:07
Introduction to Monte Carlo Methods
1:07:51
Introduction to Temporal-Difference Learning
1:34:06
Solving the Multi Armed Bandit Problem
1:23:00
Introduction to Deep Q Learning
1:40:57
Playing an Atari Game with Deep Recurrent Q Networks
0:54:00
Introduction to Policy Based Methods for Reinforcement Learning
1:02:23
Discussing Evolutionary Strategies for Reinforcement Learning
1:36:30
Discussing Advancements for Reinforcement Learning
1:49:39
Introduction to Reinforcement Learning
1:16:00
Introduction to The Markov Decision Process and Dynamic Programming
0:50:45
Practice Deep Learning with TF2
1 hour 30 minutes
Getting Started with OpenAI and TensorFlow for RL
1:02:00
Introduction to Dynamic Programming
1:07:07
Introduction to Monte Carlo Methods
1:07:51
Introduction to Temporal-Difference Learning
1:34:06
Solving the Multi Armed Bandit Problem
1:23:00
Introduction to Deep Q Learning
1:40:57
Playing an Atari Game with Deep Recurrent Q Networks
0:54:00
Introduction to Policy Based Methods for Reinforcement Learning
1:02:23
Discussing Evolutionary Strategies for Reinforcement Learning
1:36:30
Discussing Advancements for Reinforcement Learning
1:49:39

The Applied AI and Natural Language Processing Workshop

An Introduction to AWS
35 minutes
Analyzing Documents and Text with Natural Language Processing
1 hour
Topic Modeling and Theme Extraction
45 minutes
Introduction to Conversational Artificial Intelligence
50 minutes
Using Speech with the Chatbot
50 minutes
Introduction to Computer Vision and Image Processing
45 minutes
An Introduction to AWS
35 minutes
Analyzing Documents and Text with Natural Language Processing
1 hour
Topic Modeling and Theme Extraction
45 minutes
Introduction to Conversational Artificial Intelligence
50 minutes
Using Speech with the Chatbot
50 minutes
Introduction to Computer Vision and Image Processing
45 minutes
An Introduction to AWS
35 minutes
Analyzing Documents and Text with Natural Language Processing
1 hour
Topic Modeling and Theme Extraction
45 minutes
Introduction to Conversational Artificial Intelligence
50 minutes
Using Speech with the Chatbot
50 minutes
Introduction to Computer Vision and Image Processing
45 minutes
An Introduction to AWS
35 minutes
Analyzing Documents and Text with Natural Language Processing
1 hour
Topic Modeling and Theme Extraction
45 minutes
Introduction to Conversational Artificial Intelligence
50 minutes
Using Speech with the Chatbot
50 minutes
Introduction to Computer Vision and Image Processing
45 minutes

Clustering and Classification with Machine Learning in Python

The Key Concepts and Software Tools for Machine Learning in Python
31 Minutes
Read in Data from Different Sources with Pandas
46 Minutes
Data Cleaning & Munging
1 Hour 02 Minutes
Unsupervised Learning in Python
45 Minutes
Dimension Reduction & Feature Selection for Machine Learning
29 Minutes
Supervised Learning: Classification
1 Hour 23 Minutes
Neural Networks and Deep Learning Based Classification Techniques
25 Minutes
The Key Concepts and Software Tools for Machine Learning in Python
31 Minutes
Read in Data from Different Sources with Pandas
46 Minutes
Data Cleaning & Munging
1 Hour 02 Minutes
Unsupervised Learning in Python
45 Minutes
Dimension Reduction & Feature Selection for Machine Learning
29 Minutes
Supervised Learning: Classification
1 Hour 23 Minutes
Neural Networks and Deep Learning Based Classification Techniques
25 Minutes
The Key Concepts and Software Tools for Machine Learning in Python
31 Minutes
Read in Data from Different Sources with Pandas
46 Minutes
Data Cleaning & Munging
1 Hour 02 Minutes
Unsupervised Learning in Python
45 Minutes
Dimension Reduction & Feature Selection for Machine Learning
29 Minutes
Supervised Learning: Classification
1 Hour 23 Minutes
Neural Networks and Deep Learning Based Classification Techniques
25 Minutes
The Key Concepts and Software Tools for Machine Learning in Python
31 Minutes
Read in Data from Different Sources with Pandas
46 Minutes
Data Cleaning & Munging
1 Hour 02 Minutes
Unsupervised Learning in Python
45 Minutes
Dimension Reduction & Feature Selection for Machine Learning
29 Minutes
Supervised Learning: Classification
1 Hour 23 Minutes
Neural Networks and Deep Learning Based Classification Techniques
25 Minutes
View all courses in Data Academy Subscription