1. **Quality of data**: Machine learning needs high-quality data. With low-quality data, it causes error decisions.

2. **Time lagging**: Machine learning needs a lot of time to make decisions on data.

3. Machine learning uses complex algorithms that make it difficult to deploy.

- Emotional analysis
- Error detection and correction
- Weather prediction
- Market prediction
- Speech recognition
- Fraud detection
- The recommendations in online shopping

1. Supervised learning

2. Unsupervised learning

3. Semi-supervised learning

4. Reinforcement learning

5. Online learning

6. Instance-based learning

7. Model-based learning

2. Unsupervised learning

3. Semi-supervised learning

4. Reinforcement learning

5. Online learning

6. Instance-based learning

7. Model-based learning

**Type I error** means a false positive which means it claiming that something happened but in reality, it has not happened.

**Type II error** means a false negative which means it claiming nothing has happened but in reality, it happened.

**Data mining**: It is the process where the system or data trying to extract the patterns from the data using machine learning algorithms.

**Machine Learning**: It is the development of programs that help the system to learn from data without being programmed.

**P-value** is used to determine the importance of the statistical test. The value of P will be between 0 and 1 which help the users to determine conclusions.

The answer is 'no' because in some cases it reaches the local minimum, so in all cases, we cant reach the global point. It depends on the data and conditions.

- Classification
- Speech Recognition
- Regression
- Predict Time Series
- Annotate Strings

- To find clusters of the data
- To find low-dimensional representations of the data
- To find interesting directions in data
- To find novel observations/ database cleaning
- To find interesting coordinates and correlations

- Decision Trees
- Neural Networks (back propagation)
- Probabilistic networks
- Nearest Neighbor
- Support vector machines

**Precision** can be a positive predictive value. Among the received instances it is the fraction of relevant instances.

**Recall **also known as **sensitivity **is the fraction of relevant instances that have been retrieved over the total amount or relevant instances.

- Combining binary classifiers
- Modifying binary to incorporate multiclass learning

**Bagging**is a process in ensemble learning. For improving, unstable estimation or classification schemes bagging can be used**Boosting**methods are used sequentially to reduce the bias of the combined model.

**Logical**: It has a set of Bayesian Clauses, that can capture the qualitative structure of the domain.**Quantitative**: It is used to encode quantitative information about the domain.

Local Minima is the smallest value of the function.

2. Prevents from getting stuck in local optima.

3. It gives a better error surface shape.

4. Weight decay and Bayes optimization can be done more conveniently.

These both are errors. The errors due to erroneous or overly simplistic assumptions in the learning algorithm are called Bias errors. This error leads to the model under-fitting the data and make high predictive accuracy very hard

The error due to too much complexity in the learning algorithm is called Variance. This error leads to the model overfit the data

- An
**array**is a group of elements of a similar data type. In this elements are stored consecutively in the memory. And it supports Random Access. **Linked List**is an ordered group of elements of the same type, which are connected using pointers. In this new elements can be stored anywhere in memory. And it supports Sequential Access.

- Computer Vision
- Speech Recognition
- Data Mining
- Statistics
- Informal Retrieval
- Bio-Informatics

- Platt Calibration
- Isotonic Regression

These are the two methods used for the best prediction of probabilities in Supervised Learning. It is created for binary classification, and it is not trivial.

- Sequential ensemble methods
- Parallel ensemble methods

- Agglomerative hierarchical clustering
- Divisive hierarchical clustering

**Binary Logistic Regression**: In this only two outcomes possible.**Multinomial Logistic Regression**: In this, the output consists of three or more unordered categories.**Ordinal Logistic Regression**: In this, the output consists of three or more ordered categories

- Data Acquisition
- Ground Truth Acquisition
- Cross-Validation Technique
- Query Type
- Scoring Metric
- Significance Test

** **An epoch indicates the number of passes of the entire training dataset the machine learning algorithm has completed. In the case of huge data, datasets are divided into several batches and each of these batches goes through the given model this process is known as iteration.

**Inductive learning**: It is the process of using observations to make the conclusions**Deductive learning**: It is the process of using conclusions to form observations

**Entropy:**it is an indicator of how messy your data is. when we reach closer to the leaf node entropy started to decrease.**The Information Gain:**It is based on the decrease in entropy after a dataset is split on an attribute. when we reach closer to the leaf node entropy started to increase.

**Precision:** In pattern recognition Precision is the fraction of relevant instances among the retrieved instances.The situation where precision is used when False Positive is important to our output.

**Recall: **In pattern recognition Recall is the fraction of relevant instances that were retrieved. The situation where Recall is used when False Negative is important to our output.

- Train the model
- Test the model
- Deploy the model

**False positives:** The cases that get classified as wrongly True but are False.

**False negatives:** The cases that get classified as wrongly False but are True.

It is a type of unsupervised learning technique. Association checks for the dependency between data items. It maps the data items according to the dependency and make it more profitable. Association rule will be of three types.

- Apriori
- Eclat
- F-P Growth Algorithm

- Predicting yes or no
- Estimating gender
- Breed of an animal
- Type of color