Machine Learning Training Course

Machine Learning Training Course For Beginners

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Machine Learning Training Course For Beginners Summary

 

Machine Learning Training Course For Beginners is a 4 weeks long Instructor-led and guided training with Practical Hands-On Lab exercises to be taught over 16 hours, 2 sessions per week, 2 hours per session.

  • The medium of instruction is English.
  • All Published Ticket Prices are in US Dollars.

Machine Learning Training Course For Beginners Schedule

 

Please choose from one of the dates in the table below to begin your enrollment :

Dates Weekly Schedule (US Pacific Time)* Price Add to Cart
Dec 6 to Dec 29 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Dec 7 to Dec 30 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Jan 8 to Jan 30 Sat/Sun 7:30 AM - 9:30 AM each day $394.00 Add to cart
Jan 10 to Feb 2 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Jan 11 to Feb 3 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Feb 5 to Feb 27 Sat/Sun 7:30 AM - 9:30 AM each day $394.00 Add to cart
Feb 7 to Mar 2 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Feb 8 to Mar 3 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Mar 14 to Apr 6 Mon/Wed 6:30 PM - 8:30 PM each day $394.00 Add to cart
Mar 15 to Apr 7 Tue/Thu 8:30 AM - 10:30 AM each day $394.00 Add to cart
Mar 19 to Apr 10 Wed/Sun 8:30 AM - 10:30 AM each day $394.00 Add to cart
*click on date/time hyperlink to add your location and find local date/time for first session
Dates and Weekly Schedule (US Pacific Time)* Price
Dec 6 to Dec 29
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Dec 7 to Dec 30
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Jan 8 to Jan 30
Sat/Sun 7:30 AM - 9:30 AM each day
$394.00
Enroll
Jan 10 to Feb 2
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Jan 11 to Feb 3
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Feb 5 to Feb 27
Sat/Sun 7:30 AM - 9:30 AM each day
$394.00
Enroll
Feb 7 to Mar 2
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Feb 8 to Mar 3
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Mar 14 to Apr 6
Mon/Wed 6:30 PM - 8:30 PM each day
$394.00
Enroll
Mar 15 to Apr 7
Tue/Thu 8:30 AM - 10:30 AM each day
$394.00
Enroll
Mar 19 to Apr 10
Wed/Sun 8:30 AM - 10:30 AM each day
$394.00
Enroll
*click on date/time hyperlink to add your location and find local date/time for first session

Course Objectives

 
  • Demonstrate the knowledge of decision tree learning, artificial neural networks, Bayesian networks, instance-based learning, analytic learning, reinforcement learning, genetic programming, deep learning, etc.
  • Demonstrate the knowledge of MLlib on Spark, or other machine learning libraries.
  • Practice examples of machine learning programming and open source machine learning tools, and implement example machine learning applications.

Features and Benefits

 
  • 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided training
  • Training material, instructor handouts and access to useful resources on the cloud provided
  • Practical Hands-on Lab exercises provided
  • Real-life Scenarios

Who should attend ?

 
  • Anyone who wants to learn machine learning for any purpose

Prerequisites

 
  • Computer concepts.
  • Knowledge of any programming language is desirable but not required.
  • Knowledge of these concepts is nice to have but not required: Data Structures and Computer Algorithms, Statistics, Probability, Linear Algebra, Calculus.

Course Outline

 

1. Introduction to Machine Learning

2. Working with Python or R

3. Machine Learning Techniques – Types of Learning

  • Applying Machine Learning
  • Machine learning system, design

4. Types of Machine Learning Algorithms

  • Supervised Learning
  • Regression
  • Classification
  • Unsupervised Learning
  • Clustering
  • Recommendation
  • deep learning
  • Semi-Supervised Learning
  • Reinforcement

5. Supervised Learning Regression

6. Supervised Learning Classification

7. Unsupervised Learning

  • Clustering
  • Recommendation
  • deep learning

9. Common Machine Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • SVM
  • Naive Bayes
  • KNN
  • KMeans
  • Random Forest
  • Dimensionality Reduction Algorithms
  • Gradient Boost & Adaboost
  • artificial neural networks
  • Bayesian networks
  • instance-based learning
  • analytic learning,
  • reinforcement
  • genetic programming
  • Refund Policy

     
    • 100% refund can be applied if request is initiated 24 hours before the 1st course session.
    • If a class is rescheduled/cancelled by the organizer, registered students will be offered a credit towards any future course or a 100% refund