Complete Machine Learning Course > Projects

Reinforcement Learning Project - Teach a Taxi Part 3

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four types of reinforcement:

  • Positive reinforcement
  • negative reinforcement
  • extinction
  • punishment.
#BBD0E0 »

A reinforcement learning system has four primary components, in addition to the agent and the environment: a policy, a reward, a value function, and, optionally, a model of the environment. A policy describes how an agent acts at a specific point in time.

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R
Rehan mujawar
5

very nice video

S
Sushil Vyas
5

How can i get all the learning resources, like PPT and code ?

D
Devidas Mawaskar
5

Nice course long time your jerny and very beautiful 😍

A
Abhishek Jatav
5

easy explanation

S
Sachin Pandey
4

in my jupyter notebook recommendations is not showing for any functions

Z
Zeyan Khan
5

How to Learn a Deep Learning Course. As in the video, Sir says you can learn sequential in the Deep Learning course, so how can i learn? Please tell me anyone.

K
Krishna
5

very easy explaination for career

O
Omsingh Sachin Thakur
5

Amazing course with hands on practicals

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Laxmikant Raghuwanshi
4

Effective Learning with simple language.

H
Haseen Ur Rahman
5

Very helping Platform for learning different skills.

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  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
    3m 32s
  3. Environment Setup Part 2
    4m 43s
  4. Environment Setup Part 3
    2m 34s

  1. Data Wrangling in Machine Learning
    12m 26s
  2. Importing Libraries and Dataset in Machine Learning
    11m 19s
  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
    17m 3s
  5. Encoding Categorical Data in Machine Learning
    6m 34s
  6. Encoding Categorical Data in Machine Learning - Practical
    8m 3s
  7. Splitting Dataset in Machine Learning
    3m 38s
  8. Splitting Dataset in Machine Learning - Practical
    9m 36s
  9. Normalizing the Data in Machine Learning - Part 1
    6m 19s
  10. Normalizing the Data in Machine Learning - Part 2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis in Machine Learning
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  13. Plotting Graphs in Machine Learning - Part 1
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  14. Plotting Graphs in Machine Learning - Part 2
    23m 45s
  15. Distribution Models in Machine Learning - Part 1
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  16. Distribution Models in Machine Learning - Part 2
    13m 56s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 40s

  1. Machine Learning Paradigms
    9m 55s
  2. Assignment : Machine Learning Paradigms
    3m 25s

  1. Overview of Classification
    5m 22s
  2. Logistic Regression in Machine Learning
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  3. Logistic Regression in Machine Learning - Practical - 1
    32m 40s
  4. Logistic Regression in Machine Learning - Practical - 2
    12m 37s
  5. KNN in Machine Learning
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  6. KNN in Machine Learning Practical - 1
    35m 1s
  7. KNN in Machine Learning - Practical 2
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  8. Decision Trees for Classification in Machine Learning
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  9. Decision Trees for Classification in Machine Learning - Practical - 1
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  10. Decision Trees for Classification in Machine Learning - Practical - 2
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  11. Decision Tree Iterative Dichotomiser in Machine Learning - 3
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  12. Random Forest in Machine Learning
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  13. Support Vector Machine Classifier
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  14. Support Vector Machine Classifier - Practical - 1
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  15. Support Vector Machine Classifier - Practical - 2
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  16. Naive Bayes Classifier in Machine Learning
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  17. Naive Bayes Classifier in Machine Learning Practical - 1
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  18. Naive Bayes Classifier in Machine Learning Practical - 2
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  19. Evaluating Classification Models Performance in Machine Learning
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  20. Evaluating Classification Models Performance in Machine Learning Practical
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  21. Assignment : Supervised Learning Algorithms
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  1. Simple Linear Regression in Machine Learning
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  2. Simple Linear Regression in Machine Learning - Practical
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  3. Salary Prediction using Linear Regression in Machine Learning
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  4. Multi-Linear Regression in Machine Learning
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  5. Startup Prediction using Multiple Regression in Machine Learning
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  6. Support Vector Regressor in Machine Learning
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  7. Support Vector Regressor in Machine Learning - Practical - 1
    6m 48s
  8. Support Vector Regressor in Machine Learning Practical - 2
    15m 46s
  9. Decision Tree Regressor in Machine Learning
    4m 53s
  10. Decision Tree Regressor in Machine Learning Practical - 1
    5m 2s
  11. Decision Tree Regressor in Machine Learning Practical - 2
    17m 10s
  12. Regressor Model Selection in Machine Learning
    9m 7s
  13. Evaluating Regression Model Performance in Machine Learning
    8m 46s
  14. Evaluating Regression Model Performance in Machine Learning - Practical
    11m 35s
  15. Assignment : Regression Algorithms
    1m 12s

  1. Distance Metrics in Machine Learning
    6m 10s
  2. K-Means Clustering in Machine Learning
    11m 3s
  3. K-Means Clustering in Machine Learning - Practical
    9m 49s
  4. Mall Customers Prediction using K Means Clustering in Machine Learning
    18m 3s
  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
    4m 45s
  7. Divisive Clustering in Machine Learning - Practical
    14m 42s
  8. DBscan Spatial Clustering in Machine Learning
    8m 50s
  9. Mall Customers Prediction using Hierarchical Clustering in Machine Learning
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  10. Assignment : Unsupervised Learning Algorithms
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  1. Association Rule in Machine Learning - Apriori, FP Growth
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  2. Association Rule in Machine Learning - Apriori Practical
    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
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  4. FP Growth in Machine Learning
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  5. Market Basket Analysis using FP Growth in Machine Learning
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  6. Assignment : Association Rule Mining
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  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
    21m 4s
  2. Upper Confidence Bound in Machine Learning - Practical
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  3. Thompson Sampling in Machine Learning - Practical
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  4. Q Learning in Machine Learning
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  5. Assignment : Reinforcement Learning
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  1. Overview of Dimensionality Reduction in Machine Learning
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  2. Principal Component Analysis in Machine Learning
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  3. Principal Component Analysis in Machine Learning - Practical
    6m 35s
  4. Linear Discriminant Analysis in Machine Learning
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  5. Linear Discriminant Analysis in Machine Learning - Practical
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  6. Assignment : Dimensionality Reduction
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  1. Basics of Regularization and Optimization in Machine Learning
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  2. Cross Validation in Machine Learning
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  3. Hyperparameter Tuning in Machine Learning
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  4. Sampling Methods in Machine Learning
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  5. Underfitting and Overfitting in Models in Machine Learning
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  6. Variance and Bias in Machine Learning
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  7. Assignment : Regularization and Optimization
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  1. Introduction to Keras and Deep Learning
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  1. Reinforcement Learning Project - Teach a Taxi Part 1
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  2. Reinforcement Learning Project - Teach a Taxi Part 2
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  3. Reinforcement Learning Project - Teach a Taxi Part 3
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  4. Reinforcement Learning Project - Teach a Taxi Part 4
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  5. Loan Prediction Project Part 1
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  6. Loan Prediction Project Part 2
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  1. Course Summary
    3m 52s

  1. Interview Questions Part 1
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  2. Interview Questions Part 2
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  3. Interview Questions Part 3
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  1. Career Guideline
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