Machine learning is the study of computer algorithms that improve automatically through experience.
Curriculum
- 1 Section
- 36 Lessons
- 100 Hours
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- Machine learning36
- 2.11. Machine learning Intro
- 2.22. Frameworks for Building Machine Learning
- 2.33. Machine Learning Python Packages (Library)
- 2.44. Data Analysis Packages(Numpy)
- 2.55. Numpy & Codes
- 2.66. Pandas
- 2.77. Pandas & codes
- 2.88. Matplotlib
- 2.99. Machine Learning Perspective of Data – Feature Engineering
- 2.1010. Feature Engineering
- 2.1111. Regression
- 2.1212. Linear Regression
- 2.1313. Multiple Linear Regression
- 2.1414. Backward Elimination
- 2.1515. Classification
- 2.1616. Logistic Regression Classifier
- 2.1717. KNN
- 2.1818. Support Vector Machine Algorithm
- 2.1919. Naïve Bayes Classifier Algorithm
- 2.2020. Decision Tree Classification Algorithm
- 2.2121. Random Forest Algorithm
- 2.2222. Apriori
- 2.2323. Eclat
- 2.2424. Reinforcement Learning
- 2.2525. Upper Confidence Bound
- 2.2626. Natural Language Processing – Intro
- 2.2727. NLP Python
- 2.2828. Dimensionality Reduction
- 2.2929. Principal Component Analysis
- 2.3030. Linear Discriminant Analysis
- 2.3131. Kernel PCA
- 2.3232. Model Selection
- 2.3333. K-fold Cross Validation
- 2.3434. Artificial Neural Networks
- 2.3535. Convolution Neural Network
- 2.3636. Recurrent Neural Network
