Machine Learning Training

Learn Machine Learning: Get hands-on Python and R and become a skilled Machine Learning Engineer

  • 10 - 20 weeks

  • 102 Lectures

  • 502 Student Enrolled
4.5 3572 Reviews


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Machine Learning Training

BigData Training

Course Overview

This is a beginner-level machine course, that covers all the essential topics of the machine learning domain, presented in a well-thought-out structure.

With this course, learn how to use Python and R as statistical software, develop robust algorithms using supervised learning, regression, time series modeling, etc. and determine your model's effectiveness.

Developed by the experienced and expert data scientists team of Kode Campus, this course is packed with practical exercises based on real-life examples. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.

What you'll learn

  • Get a thorough introduction to machine learning, its popular types, and statistical pattern recognition
  • Learn how to represent data so that a program can learn from it and understand which machine learning to choose for depending on the problem
  • Get familiar with various regression algorithms, and learn the application of Python and R as a statistical software in Machine Learning as well as Data Science
  • Develop robust algorithms using supervised learning, regression, classification, conditional probability estimation, and time series modelling, and determine whether your model is effective

Machine Learning is all about giving machines the ability to learn on their own. That’s the simplest definition anyone can give you of it. But let’s go a little broader.

Machine Learning is a subset field of Artificial Intelligence (AI) that allows computer algorithms to automatically improve and get more accurate with experience. But to allow machine learning algorithms to learn (and produce accurate, reliable results), sample data, known as "training data" has to be fed initially.

Machine Learning growth has been primarily due to 3 factors:

  • Variety and Volume of digital data being produced every day, and the ability to capture it
  • Cheaper and powerful computational processing available today
  • Affordable data storage and Cloud Technology

To have a great career and earn a handsome salary! You want that, right?

An experienced machine learning engineer can earn up to $195, 752 while the average machine learning engineer salary is $142,000 (SimplyHired). In India, an experienced machine learning engineer takes home anywhere between 15 to 23 lakhs per annum (Glassdoor).

A Few reasons why learning Machine Learning NOW would be a great decision:

  • From Healthcare to finance, industries from all sorts of domains are looking to apply AI (and machine learning) to improve their efficiency or personalize their marketing campaigns, creating huge demands for data-skilled professionals.
  • Machine Learning is witnessing explosive growth. MLaaS (Machine learning as a Service) is predicted to grow from a mere $1.07 billion in 2016 to $19.9 billion by the end of 2025 (TMR).
  • There is a huge gap between demand and supply, with only a few skilled workers to meet the ever-growing demand. There were less than 10,000 people qualified for AI and machine learning-related jobs (New York Times, 2017).

Every day, you interact with technologies using machine learning, without even knowing that. Listed below are some of the machine learning applications.

  • Product recommendation: Used by e-commerce platforms like Amazon to suggest the product based on your previous purchase, cart history, historical data, searching pattern, etc. Entertainment companies like Netflix also use machine learning and AI to recommend content you're likely to watch.
  • Social media Features: Digital companies like Facebook, Instagram, YouTube, powered by machine learning and AI, create a personalized feed for each user based on their recent activities, users' database they have, search history, etc.
  • Image recognition: To identify objects, persons, places, digital images, etc. You must have observed the "Automatic friend tagging suggestion" feature on Facebook. It’s powered by face detection and recognition algorithms.
  • Speech recognition and virtual assistants: You use this when you use Google Assistant, Siri, Cortana, or Alexa!
  • Stock Market Trading: To help in the prediction of stock market trends.
  • Traffic prediction: Employed by GOOGLE MAPS to predict the traffic conditions and recommend the quickest route.
  • Email Spam Technology: Ever wondered how some mails sent to you automatically land into the spam folder? Machine Learning is the technology behind this!
  • Self-driving cars: TESLA CARS would be the best example for this. With unsupervised machine learning methods, these cars have been trained to detect (and avoid) people and objects while driving.
  • Sentiment Analysis: If you've used the GRAMMARLY writing tool, you might know this. The tool uses machine learning to instantly determine the emotions or the opinion of the writer based on the words written.
  • Language Translation: You must have visited a website in an unfamiliar language, and were likely presented with a translate option. It's machine learning powering that!

This machine learning course requires at least high school level math knowledge and a basic understanding of statistics. Familiarity with Python programming or some prior coding or scripting experience would be greatly beneficial.

Here's who this course is ideal for:

- Anyone having the interest to learn machine learning.

- College students looking to start a career in the Data-science field.

- People who aren't satisfied with their current job and are looking to switch into the data field.

- Software developers or developers looking to transition into the lucrative machine learning (and data science) career path.

- Data analysts in the finance or other non-tech industry looking to transition into the tech industry.

  • - Most comprehensive and well-structured course covering basics to advanced topics, allowing you to master the complete niche.
  • - Certified Trainers with extensive real-time experience in the Data Science domain and an immense passion for teaching.
  • - Top-notch course with a perfect blend of theory, case studies, and capstone projects, along with an assignment for every taught concept.
  • - 100% Job Placement assistance. Frequent mock interviews to evaluate and improve your knowledge and expertise. Facilitation of interviews with various top companies. Help in building a great resume, optimizing LinkedIn profile, and improving your marketability.

    Listed below are some of the leading machine learning-based careers you can break after completing the machine learning course.

    • Machine Learning Engineer
    • Data Scientist
    • Natural Language Processing (NLP) Scientist
    • Human-centered Machine Learning Designer
    • Software Developer/Engineer (AI/ML)
    • Business Intelligence Developer

Course Circullum

  • Random Variable
  • Probability
  • Probability Distribution
  • Sampling Funnel
  • Measure Of central tendency
  • Measures of Dispersion
  • Expected Value
  • Graphical Techniques
  • Introduction to R
  • R Studio
  • Introduction to Python (Installation basic commands)
  • Python & R Contd...Skewness & Kurtosis
  • Box Plot
  • Normal Distribution
  • Sampling Variation
  • CLT
  • Confidence interval
  • Intro to HT, 2 sample t test, 1 sample tests
  • Other parametric and non parametric tests
  • Scatter Diagram
  • Corr Analysis
  • Principles of Regression
  • Intro to Simple Linear Regression
  • Multiple Linear Regression
  • Principles of Logistic regression
  • Multiple Logistic Regression
  • ROC curve
  • Gain chart
  • Binomial
  • Neg Binomial
  • Possion
  • Poission
  • Neg Binomial
  • Models with Excessive '0's
  • Multinomial Regression
  • Decision Tree & Random Forest

Upcoming Batches

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FAQs

There are 3 types of machine learning. Supervised, unsupervised, and reinforcement learning. This foundation course covers supervised learning in-depth while touching on essential topics of unsupervised and reinforcement learning.

No! Just a basic laptop should be sufficient for most of your personal projects.

Yes! We already mentioned in the section "who can take up this course" that basic knowledge of any coding languages like Python, R, or Java is highly beneficial before taking up this course. That's because performing certain machine learning tasks like statistical analysis do require coding knowledge.

Data mining is a process to extract useful information or knowledge (patterns or insights) from Big Data. Machine Learning is a subset of Artificial Intelligence (AI) that allows computer algorithms to automatically improve and get more accurate with experience while Deep learning is a subset of Machine Learning where artificial neural networks and algorithms inspired by the human's neural network learn from large amounts of data.

This data science course is the most comprehensive, relevant, and contemporary, meeting all the present demands of the Data Industry. Don't expect it to be some repurposed or repackaged content of redundant archaic course materials.
What's more is that we continually upgrade the content of this course with the changes in technology, trends, and demands to provide you the best learning resource.

Our team has compiled a list of the best data science resources including study materials, cheat sheets, data sets, videos, which you get access to when you join our course.

Kode Campus has its dedicated Placement Assistance Team(PAT). The team helps you in all the aspects of securing your dream job, from improving your marketability to conducting mock interviews.

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Jonathan Campbell

  • 72 Videos
  • 102 Lectures
  • Exp. 4 Year

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Item Reviews

Rahul Yadav27 Oct 2019

4.9

" This machine learning training course was awesome. There was a well-balanced ratio of theoretical and practical based approach. The instructor is amazing, knowledgeable, and maintained a great pace throughout the course. Thank you, KodeCampus!"

Ehtesham Sheikh2 May 2019

3.7

" I'm a B.Sc Computers graduate. Let me be honest. This was the first time I attended any online class, and the experience exceeded my expectations. Big thanks to the support team for clarifying all my doubts and their quick response time. I can say that the Kode Campus is the most convenient platform to develop a solid machine learning foundation."

Vamsi Angara10 Nov 2019

4.2

"Fantastic course. This course has helped me to move ahead in my career path. After completing this course, I have started pursuing an MS in Data Science. Thank you, Kode Campus, for such a great learning journey!"

Manish Rajesh Emanuel2 dec 2019

3.7

" Fantastic course. This course has helped me to move ahead in my career path. After completing this course, I have started pursuing an MS in Data Science. Thank you, Kode Campus, for such a great learning journey!"

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Machine Learning Training
Course Features
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  • Help Code to Code
  • Free Trial 7 Days
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Course Features

  • Student Enrolled:1740
  • lectures:10
  • Quizzes:4
  • Duration:60 hours
  • Skill Level:Beginner
  • Language:English
  • Assessment:Yes
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