How to Become A Machine Learning Engineer ? | How to Learn Machine Learning? - PowerPoint PPT Presentation

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

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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. – PowerPoint PPT presentation

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


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SynergisticIT
  • The best programmers in the bay arePeriod!

2

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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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Thanks!
  • Does anyone have any questions?
  • 510-550-7200
  • https//www.synergisticit.com
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