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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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Effective Learning with simple language.

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Hi Sir, I want a clearity up on these 1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.

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Ayush Bharti
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how can i download the finaldata.csv?

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Hello Kushal Sir! Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.

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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world. Extremely helpful and I recommend to my peer as well for this course.

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Very helpful and easy to understand all the concepts, best teacher for learning ML.

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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?

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    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
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  3. Environment Setup Part 2
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  4. Environment Setup Part 3
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  1. Data Wrangling in Machine Learning
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  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
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  5. Encoding Categorical Data in Machine Learning
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  6. Encoding Categorical Data in Machine Learning - Practical
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  7. Splitting Dataset in Machine Learning
    3m 38s
  8. Splitting Dataset in Machine Learning - Practical
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  9. Normalizing the Data in Machine Learning - Part 1
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  10. Normalizing the Data in Machine Learning - Part 2
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  11. Finding Machine Learning Datasets
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  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
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  15. Distribution Models in Machine Learning - Part 1
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  16. Distribution Models in Machine Learning - Part 2
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  2. Assignment : Machine Learning Paradigms
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  1. Overview of Classification
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  2. Logistic Regression in Machine Learning
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  3. Logistic Regression in Machine Learning - Practical - 1
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  4. Logistic Regression in Machine Learning - Practical - 2
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  6. KNN in Machine Learning Practical - 1
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  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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  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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  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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  8. Support Vector Regressor in Machine Learning Practical - 2
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  10. Decision Tree Regressor in Machine Learning Practical - 1
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  11. Decision Tree Regressor in Machine Learning Practical - 2
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  12. Regressor Model Selection in Machine Learning
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  13. Evaluating Regression Model Performance in Machine Learning
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  14. Evaluating Regression Model Performance in Machine Learning - Practical
    11m 35s
  15. Assignment : Regression Algorithms
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  2. K-Means Clustering in Machine Learning
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  3. K-Means Clustering in Machine Learning - Practical
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  4. Mall Customers Prediction using K Means Clustering in Machine Learning
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  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
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  6. Agglomerative Clustering in Machine Learning - Practical
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  7. Divisive Clustering in Machine Learning - Practical
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  1. Association Rule in Machine Learning - Apriori, FP Growth
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  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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  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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  2. Principal Component Analysis in Machine Learning
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  2. Cross Validation in Machine 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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  6. Loan Prediction Project Part 2
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  1. Course Summary
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  1. Interview Questions Part 1
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  3. Interview Questions Part 3
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