Certificate Associate in Data Science - Machine Learning Basics
Machine Learning is a name that is gaining popularity as an umbrella for methods that have been studied and developed for many decades in different scientific communities and under different names, such as Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing, Image Processing and Analysis, System Identification and Control, Data Mining and Information Retrieval, Computer Vision, and Computational Learning. The name “Machine Learning” indicates what all these disciplines have in common, that is, to learn from data, and then make predictions. What one tries to learn from data is their underlying structure and regularities, via the development of a model, which can then be used to provide predictions.
The goal of this course is to approach the machine learning discipline in a unifying context, by presenting the major paths and approaches that have been followed over the years, without giving preference to a specific one.
This course is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all those scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering and natural language processing.
After completing this course, you should have the skills and be familiar with the following topics
- Apply mathematical concepts regarding the most common machine learning problems, including the concept of learnability and some elements of information theory.
- Explain the process of Machine Learning
- Describe the most important techniques used to preprocess a dataset, select the most informative features, and reduce the original dimensionality.
- Describe the structure of a continuous linear model, focusing on the linear regression algorithm. Explain Ridge, Lasso, and ElasticNet optimizations, and other advanced techniques.
- Describe the concept of linear classification, focusing on logistic regression and stochastic gradient descent algorithms.
- Demonstrate knowledge of evaluation metrics
Who should attend
Data Analysts, Data Engineers, Data Science Enthusiasts, Business Analysts, Project Managers
Foundational certificate in Big Data/Data Science
This course is meant for anyone who are comfortable developing applications in Python, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Python programming language will also find this course to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing python stack, this course is for you
Mix of Instructor-led, case study driven and hands-on for select phases
H/w, S/w Reqd
Python, Pandas, Numpy, System with at least 2GB RAM and a Windows /Ubuntu/Mac OS X operating system
24 Hours (2 days Instructor led + 8 hours online learning)
- Course Name:Certificate Associate in Data Science – Machine Learning Basics
- Duration:2 days classroom + 8 hours online
- Exam Time: 60 minutes
- Course Price: Call for price
- Minimum requirements: Foundational Certificate in Programming
|#||Topic||Method of Delivery|
1. Introduction to Machine Learning
Introduction – classic and adaptive machines
Types of learning
Beyond machine learning – deep learning and bio-inspired adaptive systems
Machine learning and big data
2. Important Elements in Machine LearningData formats
Underfitting and overfitting
Error measures PAC learning
Statistical learning approaches
3. Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Feature selection and filtering
Principal component analysis
Non-negative matrix factorization
4. Linear Regression
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Regressor analytic expression
Ridge, Lasso, and ElasticNet
Robust regression with random sample consensus
5. Logistic Regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
6. Naive Bayes
Naive Bayes classifiers
Naive Bayes in scikit-learn
Bernoulli naive Bayes
Multinomial naive Bayes
Gaussian naive Bayes
7. Evaluation methods based on the ground truth
Adjusted rand index
|Online Self paced|
- Certificate Title: Certificate Associate in Data Science – Machine Learning Basics
- Certificate Awarding Body: ITPACS
Information Technology Professional Accreditations and Certifications Society (ITPACS) is a non-profit organization focused on improving technology skills for the future. ITPACS offers associate level, professional level and leader certifications across 6 domains including data science, web development, mobile development, cyber security, IoT and blockchain. Applicants have to go through a exam eligibility process demonstrating their experience.
The Associate certification is catered to individuals with less than 1 year working experience in the field. This is ideal for newcomers starting out in the profession or those seeking to make an entry into the profession. Applicants are required to have completed the application process prior to taking the exam.
- Exam Format: Closed-book format.
Questions: 30 multiple choice questions, coding exercises
Passing Score: 65%
Exam Duration: 60 minutes
- Exam needs to be taken within 12 months from the exam voucher issue date
Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:
- Computer science
- Data engineering
- Domain-specific knowledge and approaches
With the advent of cheaper storage technology, more and more data has been collected and stored permitting previously unfeasible processing and analysis of data. With this analysis came the need for various techniques to make sense of the data. These large sets of data, when used to analyze data and identify trends and patterns, become known as big data.
The process of analyzing big data is not simple and evolves to the specialization of developers who were known as data scientists. Drawing upon a myriad of technologies and expertise, they are able to analyze data to solve problems that previously were either not envisioned or were too difficult to solve.
The various data science techniques that we will illustrate have been used to solve a variety of problems. Many of these techniques are motivated to achieve some economic gain, but they have also been used to solve many pressing social and environmental problems. Problem domains where these techniques have been used include finance, optimizing business processes, understanding customer needs, performing DNA analysis, foiling terrorist plots, and finding relationships between transactions to detect fraud, among many other data-intensive problems.
Data mining is a popular application area for data science. In this activity, large quantities of data are processed and analyzed to glean information about the dataset, to provide meaningful insights, and to develop meaningful conclusions and predictions. It has been used to analyze customer behavior, detecting relationships between what may appear to be unrelated events, and to make predictions about future behavior.
Machine learning is an important aspect of data science. This technique allows the computer to solve various problems without needing to be explicitly programmed. It has been used in self-driving cars, speech recognition, and in web searches. In data mining, the data is extracted and processed. With machine learning, computers use the data to take some sort of action.