BEST WAYS TO LEARN MACHINE LEARNING

MACHINE LEARNING

Machine learning is the study of computer algorithms that helps in analyzing data, learning from the data, and improving from its experience without any human intervention.

It is a subset of artificial intelligence that allows the machine to make accurate predictions and automatically learn from the data.

TYPES OF MACHINE LEARNING

1. SUPERVISED LEARNING
2. UNSUPERVISED LEARNING
3. REINFORCEMENT LEARNING

WHY LEARN MACHINE LEARNING?

• It helps in increasing the efficiency and helps in understanding the customer better
• It has brought a new age of personalization for marketing and advertisement campaigns as per the need of the organizations.
• With the help of Fear Of Missing Out it helps in recommending the products and services to us which other customers have liked.
• It helps in detecting and catching fraud and suspicious transactions and prevent vicious transactions from happening.
• It provides better career opportunities with high salary packages in the fields like cybersecurity, medicine, face recognition, etc.
• Machine Learning and Data Science are interlinked.

METHODS OF MACHINE LEARNING

• REGRESSION:It is the statistical method to investigate and explain the relationship between target and response variables by predicting a continuous outcome.
• CLASSIFICATIONS:It helps in specifying the data into classes by making machine learning algorithms learning how to assign the different labels for different datasets.
• CLUSTERING:It gives insights from the data by discovering the natural grouping of data in the space with the help of clustering algorithms.
• DIMENSIONALITY REDUCTION:?Dimensionality reduction is defined as the technique used to shift data from high dimensions to low dimensions i.e. to reduce the input variables in a dataset without affecting the features of the variables before being fed into the system.

The techniques used for Dimensionality Reduction:

1. Missing Values Ratio
2. Low Variance Filter
3. High Correlation Filter
4. Random Forests/Ensemble Trees
5. Principal Component Analysis
6. Backward Feature Elimination
7. Forward Feature Construction

• NATURAL LANGUAGE PROCESSING:Natural Language Processing (NLP) is a subfield of artificial intelligence which helps the computer to understand, analyze and process human language like speech and text with the help of software.
• WORD EMBEDDINGS:Word embedding is a set of feature learning technique NLP where words or phrases that have same meaning are mapped to vectors of real numbers and have same representations.
• TRANSFER LEARNING:Transfer learning is a research problem that reuses pre-trained model for solving one problem and applying that knowledge to another problem. This pre-trained model is used as a starting point for other related problem.

HOW DO YOU LEARN MACHINE LEARNING?

PREREQUISITES FOR MACHINE LEARNING

Before moving forward with how to learn machine learning we should understand what prerequisites are needed to learn for machine learning.

1. LINEAR ALGEBRA:

Linear algebra is used to process a large amount of data for tasks like face morphing, image compression, edge detection, etc. by learning integral and differential calculus and also apply them to vectors and tensors.

Some examples of Linear Algebra in Machine Learning:

• Dataset?and?Data Files
• Images and Photographs
• One-Hot Encoding
• Linear Regression
• Regularization

2.STATISTICS:

It is used to observe data from data sets and then answering questions about the different samples of the data.

• Descriptive Statistics:Descriptive statistics is defined as the method of using graphical representations for visualizing and summing up the raw samples of the data into information that is easily understandable and shareable.
• Inferential Statistics:Inferential statistics is the method that helps in estimating the quantified properties of the population from the samples of data.

3.PROBABILITY:

It helps in making decisions about events based on the pattern of data.

It helps in designing, training, tuning and evaluating models with probabilistic framework.

4.PYTHON:

It is the widely used language for machine learning because it is an object-oriented, interpreted, and interactive programming language.

It can do complex ML tasks with ease and the codes that are created by python are easy to read.

It is very versatile and flexible to use and also helps in creating prototypes at a fast rate for the testing of the product.

5.MACHINE LEARNING ALGORITHMS WITH LIBRARIES:

Machine learning libraries are used for classification, regression, data modeling, time series analysis, and avoid error and bugs with the help of logarithmic and exponential functions.

Some of the ML Libraries:

• Numpy
• Pandas
• Scikit Learn
• Statsmodels
• Pytorch
• NLTK

6.DEEP LEARNING ALGORITHMS WITH LIBRARIES:

Deep learning libraries are used for fine-grain customization and flexibility and support distributed computing using data flow graphs.

Some of the DL Libraries:

• Theano
• TensorFlow
• Lasagne
• Keras
• MXNet

MACHINE LEARNING CONCEPTS

• MODEL:A machine learning model is defined as a file or a mathematical representation that is used to recognize patterns.
• FEATURE:A feature is defined as the descriptive attribute or a technique for observing a phenomenon to predict an outcome.
• TARGET:A target is an individual class or an algorithm used to map the input variables and its target value.
• TRAINING:It means building a model and learning to predict test data.
• TEST SET:It is a subset to the test that is used to train the models.
• PREDICTION:It is basically an output of the algorithm of the datasets that have been trained and applied to the new data.
• REGULARIZATION:It is a technique used to nullify the risk of overfitting by adding an additional penalty in the error function.

APPROACHES TO LEARN MACHINE LANGUAGE

There are two types of approach which can help in learning machine language:

• BOTTOM-UP:It suggests where and when the customer traffic increased or the purchases occurred after a brief analysis of the datasets.
• It uses information and domain knowledge for solving problems while using data.
• TOP-DOWN:It is used to track fraud transactions and shady customer activities by collecting the transactional data and building a model to flag such transactions for investigations.

This approach helps to take decisions and implement them quickly.

RESOURCES FOR LEARNING MACHINE LEARNING

• BOOKS:

1. Neural Networks and Deep Learning by Michael Nielsen
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron
3. An Introduction to Statistical Learning

• ONLINE TRAINING:

1. Learnbay
2. Simplilearn
3. Upgrad
4. GreatLearning

• DATASETS:

1. Kaggle.com
2. Google Dataset Search
3. Open Data on AWS

Conclusion :

Machine learning is a exciting area that grows every year. Day by day machine learning fast expanding sector. To recap, we discussed some of the most relevant BEST WAYS TO Understand MACHINE LEARNING: Types of Machine Learning, Why Machine Learning, Methods of Machine Learning, Why Machine Learning.

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