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4 Weekends Machine Learning training course for Beginners is a 4 weekends long Instructor-led and guided training with Practical Hands-On Lab exercises to be taught over 4 Weekends, 2 sessions per weekend, 2 hours per session.

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

4 Weekends Machine Learning Training Course Weekly Schedule

• June 26, 2021 - July 18, 2021 US Pacific time
• 4 Weekends | 2 Hours Saturdays, 2 Hours Sundays every weekend US Pacific time
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on June 26, 2021 at 8:30 AM US Pacific Time.

Features and Benefits

• 4 weekends, 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
• Actual code and scripts provided
• Real-life Scenarios

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.

Who can take this course

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

8. Spark Core and MLLib

• Spark Core
• Spark Architecture
• Working with RDDs
• Machine learning with Spark – Mllib

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