Introduction and Overview
Machine Learning is moving past the “hype cycle”, with enterprises looking to automate analytics processes in areas like business intelligence and cyber security. In 2020, nearly 8 billion jobs will be created for the Machine Learning experts, generating USD $3 billion in the field of Machine Learning
What is Machine Learning?
Machine Learning are composed of multiple technologies and techniques, such as deep learning, neural networks, natural-language processing, which will trigger autonomous systems that can be programmed to operate independently of humans, allowing IT to go deeper and do more. In other words, machine learning is an application of artificial intelligence (AI) that allows systems the ability to automatically learn and improve from experience without being programmed. Machine learning focuses on the development of computer programs that can access data and use it as learning material for themselves.
The process of learning begins with observations or data, such as direct experience, examples or instructions This is to understand and look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Why do you need to follow a Machine learning course?
CMLS – Certified Machine Learning Specialist Course by RapidStart in Singapore, the Authorised Training Partner (ATP) of Global Science and Technology Forum – GSTF, will provide participants with an in-depth knowledge on Machine Learning algorithms, techniques and its applications. Participants will also be able to experience first-hand leading open-source technologies such as GO, WEKA and SciPy which are implemented by industry leaders such as Google, Oracle, Microsoft etc.
Real-cases and industry scenarios on implementation techniques for Machine Learning will also provide Participants with the necessary skills to apply machine learning methods.
“Modern enterprises are drowning in data and starving information”
32 Hours (4 Days)
- 70 -100 % CITREP+ Funding available. Conditions Apply!
- Participants are preferred to have min. 2 years of experience in software development, business domain or data/business analysis.
As part of the written examination, each participant will be assessed individually on the last day of the training for their understanding of the subject matter and ability to evaluate, choose and apply them in specific context and also the ability to identify and manage risks. The assessment focuses on higher levels of learning in Bloom’s taxonomy: Application, Analysis, Synthesis and Evaluation. This written examination will primarily consist of 40 multiple choice questions spanning various aspects as covered in the program. It is an individual, competency-based assessment.
- Learn the fundamentals of AI and machine learning and how it could impact your work through several real-life use case
- Understand Machine Learningtechniques/method: Supervised, Unsupervised & Reinforcement Learning through hands-on examples
- Learn key ML concepts like Principle Component Analysis (PCA), Hyperparameter tuning, Clustering, Classification, Regression, Neural Network etc.
- Get skilled in popular machine learning algorithms using Python Programming ( SckitLearn, TensorFlow ), Weka, RapidMiner
Unit 1: Introduction and basic concepts in Machine learning
- Definition of machine learning systems
- Goals and applications of machine learning
- Classification of machine learning algorithms
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Unit 2: Introduction to Theories used in Machine Learning
- Introduction to probability theory
- Discrete random variables and Fundamental rules
- Independence and conditional independence – What is Information theory?
- How Decision Theory is helpful in machine learning
- Learning Theory of the machine learnings
Unit 3: Supervised learning vs. Unsupervised learning
- Supervised learning setup
- Logistic regression
- Gaussian discriminant analysis and Naive Bayes
- Support vector machines
- Clustering and K-means
- PCA (Principal components analysis)
- ICA (Independent components analysis)
- Evaluating and debugging learning algorithms
Unit 4: Model selection in Machine learning
- Bayesian model selection
- Model selection for probabilistic models
- Model selection for non-probabilistic methods
- Probabilistic Generative Models
- Probabilistic Discriminative Models
Unit 5: Role of Weka in Machine Learning
- Introduction to weka
- How to install Weka
- The Knowledge Flow interface
- The Command Line interface
- Classification Rules and association Rules
- Attribute Selection and Fast attribute selection using ranking
Unit 6: Decision Tree and Rule mining using Weka
- ID3 based decision tree algorithm
- Entropy and Information gain
- ID3 implementation using weka
- Association rule mining using Frequent Pattern (FP) Growth algorithm
- FP-Tree structure
- FP-Growth Algorithm
- Implementation of FP-Growth using weka
Unit 7: A Brief review on SciPy
- SciPy – A introduction
- Scipy installation in ubuntu
- Python Scientific Computing Environment
- The SciPy Library/Package
- Data structures and function of Scipy
Unit 8: Random Forest and Markov Decision Process algorithm
- Decision tree learning
- Tree bagging
- Random forests generation
- Relationship to nearest neighbors
- Markov model and Hidden Markov Model (HMM)
- Inference in HMM
- Learning and generalizations of HMM
Unit 9: Google’s Go Programming with k-nearest neighbor’s algorithm
- Introduction to Go Programming
- Mark Bates on Go Core Techniques and Tools
- Mark Bates on Go Database Web Frameworks and Techniques
- k-nearest neighbors algorithm
- Parameter selection
- Metric learning and Feature extraction
- Dimension reduction and Decision boundary
- Selection of class-outliers
Unit 10: C 5.0 based decision tree algorithm
- Introduction to C4.5 and C 5 Decision Tree
- Divide and Conquer Technique
- Feature Selection
- Regression Trees
- Selecting and Candidate testing
- Estimating True Error rate
- Pruning Decision Trees
Visit our GSTF course schedule to check our class availability.
GSTF Authorized Training Partners:
RapidStart Pte Ltd
Find more GSTF Training Partners here.