Machine Learning

Though it seems to be an attractive learning proposition, it is certainly not for the faint-hearted. It is an incessantly challenging field that demands that the learners be on their toes always, keeping themselves abreast with the latest trends and developments in the field.

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While it is a fact that Machine Learning courses do not necessarily mandate the presence of prior skills in the domain, it finally boils down to how well you are versed with the fundamentals of programming languages, linear equations, variables and histograms. Having a general overview of these topics can go a long way in making your journey of learning it an enriching and rewarding experience.

The Top 5 Essential Prerequisites in Machine Learning

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1. Statistics

Statistics, as a branch of mathematical sciences, concerns itself predominantly with a collection of datasets, sorting of analytical points and their final interpretation. Statistics lies at the core of the standard principles of ML.

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Statistics is the science of managing and presenting data in a meaningful way. With the omnipresence of Big Data, across the commercial and academic domain, mastery of statistics is an essential prerequisite to understanding the nuances of Machine Learning technology.

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The majority of the Machine Learning techniques including their core algorithms are derived from statistics. The concepts of standard deviations, mode, and histogram, mean and median are applied to conduct statistical modeling.

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Also, the distribution theories are deployed to conduct data modeling about real-world scenarios through the use of decreased parameters. Candidates should be well-adept in data sampling, descriptive statistics and data distribution techniques to optimize the given set of data for computational analysis.

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An ML expert has to be proficient in the following components of statistics:

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• Median
• Standard deviation analysis
• Mean
• Histogram structures
• Outliers

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2. Probability

Probability signifies the likelihood of a specific event to occur. The majority of data-driven decisions are based on the central foundation probability. As the name suggests, probability denotes mathematical clarification to the happening of a specific situation or its non-happening.

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It also deals with complex sub-branches such as Notation, mutual and conditional probability distributions and Variable Independence. Learners should gain expertise on the fundamental rules and theorems such as sum & product rules, randomized continuous variables and Bayes theorem. With respect to Machine Learning, a few of the important topics in the probability that need to be mastered are:

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• Variance and Expectation
• Standard distribution( Binomial, Multinomial, Uniform and Gaussian)
• Maximum Likelihood Estimations (MLE)
• Generating Functions
• Sampling methods
• Maximum Posteriori Estimation
• Moment Generating Functions

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3. Linear Algebra

Linear algebra is regarded as a central component in the field of Machine Learning. The interconnectedness between Linear Algebra and Machine Learning is a little vague and can only be understood through the understanding of complex concepts such as matrix operations and vector spaces.

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Experts in ML consider Linea Algebra as the defining branch of mathematics of the 21st century. Linear algebra is omnipresent in the science of Machine Learning.

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The important role of Machine Learning is to give a new shape to operations on the dataset. Matrix spaces and vector analysis are the predominant aspects of Linear Algebra that find use in ML applications. A few features of Linear Algebra that are deployed to conduct data operations in Machine Learning are:

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• Algorithm code applications
• Linear Transformations
• Mathematical Notations
• Matrix multiplication
• Tensor rank analysis

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4. Calculus

Calculus is pivotal to generate a Machine Learning model. In addition to being a vital part of multiple ML algorithms, Calculus is a sure-shot way for fresher’s to make a good mark in the field of Machine Learning. To scale the highly competitive ladder in the ML sector, graduates and working professionals need to get extensive knowledge in the following:

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• In-depth knowledge of the techniques in Integration and differentiation
• Theories of Partial Differentiation
• Gradient and Slope theory
• Chain rule theory for extensive training neural networks

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5. Programming Languages

Having a robust foundation in programming concepts is beneficial in quickly learning ML algorithms. Programming inputs are particularly useful for ML coding.

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In addition to having a good grasp of mathematical concepts, it is always advisable to gain expertise in at least one programming language to consolidate your knowledge on the internal mechanisms of Machine Learning.

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Establishing an optimal level of comfort with programming languages will enable learners to glean valuable inputs in ML algorithms. Some of the popular programming languages in Machine learning are:

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5.1. Python

This is a highly popular language for ML with powerful attributes such as built-in function, syntax, and wide package support. The presence of highly-supported libraries makes this language the most ideal for beginners. The following are the prerequisites for applying Python techniques in Machine Learning:

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• Deep Learning TensorFlow techniques
• Mathematical operations related to Python
• OpenCV learning conception for computer vision
• Pandas concepts for file operations
• Regression algorithms related to Scikit Learn methodologies

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5.2. R Programming

This language is the amalgamation of AI and Machine Learning techniques. This language boasts of robust library support and statistical graphs. The key components of this language are:

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• Data Exploration
• Regression analysis using Kernel and Caret
• Machine Learning workflows using Mir3
• Data visualization using ggplot and Plotly techniques.

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Machine Learning Essentials

In addition to the above essential points, Machine Learning aspirants should display a high level of comfort in working with data and its various analytical methods. ML is an indispensable tool in modern marketing to generate customer segmentation models and to craft highly effective campaigns.

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In a poll among marketing managers, more than 80 per cent believe that having a strong foundation in ML fundamentals leads to favorable business outcomes. Learners are advised to inculcate an incisive focus in statistics, calculus, probability and other related fields to implement algorithms, codes and syntax in various real-time Machine Learning scenarios.

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