How to Become A Machine Learning Engineer ? | How to Learn Machine Learning?

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SynergisticIT

• The best programmers in the bay arePeriod!

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How to Become A Machine Learning Engineer ? How
to Learn Machine Learning?
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(No Transcript)
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1. Introduction to Machine Learning

• Machine learning is a branch of Artificial
  Intelligence and computer science that focuses on
  use of data and algorithms to imitate the way
  humans learn, gradually improving accuracy.
  Machine learning is an important component of the
  extending field of data science.

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• Using statistical methods, algorithms are trained
  to make classifications or predictions,
  uncovering key insights within data mining
  projects. Machine learning training can help you
  excel in the career as a specialist in the IT
  field.

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Machine learning projects usually involve
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2. Machine learning Procedure

• Machine learning has three main parts

• A decision process- Machine learning algorithms
  are used for making predictions or
  classifications. Based on some input data, that
  is labelled or unlabeled your algorithm can
  produce an estimate about a pattern in the data.
• An Error function- An error function serves to
  evaluate the prediction of model. An error
  function can make comparison for assessing
  accuracy of the model.

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• Day 1

• A model optimization process- If model can fit
  better to the data points in training set,
  weights are adjusted for reducing the discrepancy
  between known example and the model estimate.
  Algorithm can repeat this to evaluate and
  optimize process, updating weights autonomously
  until a threshold of accuracy has been met.

• Day 2

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3. Machine learning methods

• Machine learning has three methods

• Supervised Machine learning Supervised Machine
  learning is defined by use of labelled datasets
  to train algorithms that to classify data or
  predict outcomes accurately. As input data is fed
  in the model, it adjusts its weights until model
  has been fitted appropriately. This occurs as
  part of cross validation process to ensure models
  avoid overfitting or underfitting. It helps in
  organizing and solving for several real-world
  problems at scale, such as classifying spam in a
  separate folder from inbox. Some methods for
  supervise machine learning include- neural
  networks, naive bayes, logistic regression,
  linear regression, support vector machine, random
  forest, and more.

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• Unsupervised machine learning- Uses machine
  learning algorithms for analyzing and cluster
  unlabeled datasets. These algorithms discover
  hidden patterns or data grouping without the need
  for human mingling. It has ability to discover
  similarity and difference in information making
  it ideal solution for exploratory data analysis,
  customer segmentation, image and pattern
  recognition, and cross-selling strategies. It
  also can be used to reduce the number of feature
  in model through the process of dimensionality
  reduction, PCA and SVD. The other algorithms
  include neural networks, probabilistic clustering
  methods, K-means clustering, and many more.

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• Semi-supervised learning- Semi-supervised
  learning offers a middle path between supervised
  and unsupervised learning. During training, it
  uses smaller labelled data for guiding
  classification and feature extraction from a
  larger, unlabelled data set. Semi-supervised
  learning could solve issues of having not
  sufficient labelled data for training a
  supervised learning algorithm. Machine learning
  training can help you in shifting gears in this
  rewarding career.

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There are several uses of machine learning
4. Usage of Machine learning
By consisting a machine learning bootcamp you
can be part of numerous machine learning projects.

• Email Spam and Malware Filtering
• Virtual Personal Assistant
• Online Fraud Detection
• Stock Market trading
• Medical Diagnosis
• Automatic Language Translation

• Image Recognition
• Speech Recognition
• Traffic prediction
• Product recommendations
• Self-driving cars

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5. Career in Machine Learning

• As per Indeed.com, salaries of ML specialists
  depend on various factors such as geographical
  location, role, and years of experience. ML
  specialists in USA make about 150,000 per year.
  Some of the top companies like eBay, Twitter,
  AirBnB, and Wish are ready to pay developers
  anything from 200,000 to 335,000. So surely,
  machine learning training through SynergisticIT
  can help you make a career in the field!

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