An AWS Certified Machine Learning Specialist helps organizations in building, implementing, deploying, and maintaining Machine Learning solutions for business issues. legitimate the appropriate Machine Learning approach, determine the proper AWS administrations, and secure solutions of Machine Learning

The AWS Certified Machine Learning specialty certification is meant for folk that performs an improvement or data science position. It validates a candidate’s capability to design, implement, deploy, and hold machine learning (ML) answers for given enterprise issues.

We have recently started our AWS Certified Machine Learning – Specialty[MLS-C01] Training Program.

In this post, we will be sharing the Day 1 & 2 live session review with the FAQs of AWS Certified Machine Learning – Specialty[MLS-C01] Day 1 & 2 Training which will help help you in understanding some basic concepts.

First of all, there are 16 modules & 30+ hands-on labs which are important to learn to become an AWS Certified Machine Learning Specialist.

• Module 1: Getting Started with Python basics
• Module 2: Statistics & Probability
• Module 3: Data Engineering Basic
• Module 4: Data Engineering in AWS
• Module 5: Data Analysis in AWS
• Module 6: Modeling in AWS
• Module 7: Artificial Intelligence in AWS
• Module 8: Introduction to SageMaker
• Module 9: SageMaker Setup
• Module 10: SageMaker Built-in Algorithms
• Module 11: Model Training & Tuning
• Module 12: Model Deployment
• Module 13: Using Machine Learning Frameworks with SageMaker
• Module 14: Ground Truth using SageMaker
• Module 15: Monitoring & Watching
• Module 16: Using SageMaker SDK

Out of which, in the first 2 Live Sessions (Day 1 & 2) of the AWS Certified Machine Learning – Specialty [MLS-C01] training program, where we covered the Python basics, Statistics & Probability, and Data Engineering Basic. Also performed Hand-on Labs

>Getting Started With Python Basics

Q1: Why Python in Machine Learning?
A: Machine Learning is nothing but recognizing patterns in your data. It is important. So, an important task of a Machine learning engineer in his work life is to extract, process, define, clean, arrange and then understand the data to develop intelligent algorithms.
So Python would be recommended because it is easy to understand.
Some of the key points are :

1. Libraries and framework
2. Simple and Consistent
3. Platform independent
4. Great Community base

Python works on different platforms. Python works on completely different platforms. it’s a syntax that permits developers to put in writing programs with fewer lines than other programming languages. Python runs on an interpreter system, which means that code may be executed as before long soon is written.

Q2: What is Pandas?
A: Pandas is the most popular Python library that is used for data analysis. It provides highly optimized performance with back-end source code that is purely written in Python. You will learn about Pandas used for Data Analysis.

Pandas enable us to analyze huge data and build conclusions supported by statistical theories. It will clean mussy data sets, and build them clear and relevant. Relevant data is very important in data science.

>Statistics And Probability

Probability And Statistics are the 2 vital concepts in Maths. probability is all regarding chance. Whereas statistics is additional regarding however we tend to handle numerous data exploitation totally different techniques. It helps to represent sophisticated information in a very simple and graspable method

Q3: How many types of data?
A: There are 6 types of data:

1. Quantitative data: Quantitative data may be expressed as variety or may be quantified. Simply put, it may be measured by numerical variables.
2. Qualitative data: Qualitative data can’t be expressed as variety and can’t be measured. Qualitative data carries with it words, pictures, and symbols, not numbers
3. Nominal data: Nominal data is employed only for labeling variables, with no kind of quantitative value. The name ‘nominal’ comes from the Latin word “nomen” which implies ‘name’.
4. Ordinal data: A mixture of numerical and categorical
5. Discrete data: Discrete data is a count that involves only integers. The discrete values cannot be subdivided into parts.
6. Continuous data: Continuous data is data that can be measured on an infinite scale, It can take any value between two numbers, no matter how small. The measure can be virtually any value on the scale

Q4: What is mean, mode and median?
A:

Q5: What are standard deviation and variance?
A: Variation & Standard Deviation is all about the Spread of the data or Shape of the distribution

Variance ()^2 is simply the average of the squared differences from the mean
Standard Deviation is just the square root of the variance

Q6: What is linear regression?
A: Linear regression tries to model the connection between 2 variables by fitting an equation to determining data. One variable is taken into account to be an instructive variable, and therefore the alternative is taken into account to be a variable. for instance, a creator would possibly need to relate the weights of people to their heights employing a linear regression model

Q7: What are multiple regression and polynomial regression?
A: Polynomial regression could be a special case of linear regression wherever we tend to work a polynomial equation on the info with a curving relationship between the target variable and therefore the freelance variables.
In a curving relationship, the worth of the target variable changes in a very non-uniform manner with the relevancy of the predictor (s).
This linear equation may be accustomed to represent a linear relationship. But, in polynomial regression, we’ve got a polynomial equation of degree n drawn as:

Y= 0+1x+2(x)^2+3(x)^3+………...+n(x)^n

Here:
0 is the bias,
1, 2, …, n are the weights in the equation of the polynomial regression,
and n is the degree of the polynomial

Multiple Linear Regression tries to model the connection between 2 or additional options and a response by fitting an equation to determining data.

• The steps to perform multiple linear regression are nearly just like that of easy linear regression.
• The Difference Lies in the evaluation
• We can use it to find out which factor has the highest impact on the predicted output and now different variables relate to each other.

Here : Y = b0 + b1 * x1 + b2 * x2 + b3 * x3 + …… bn * xn

Y = Dependent variable and x1, x2, x3, …… xn = multiple independent variables

> Data Engineering Basics

Before a model is made, before the data is clean and did for exploration, even before the role of data scientist begins – this is often wherever data engineers get the image. each data-driven business must have a framework in place for the data science pipeline, otherwise, it’s a setup for failure.

Most people enter the data science world with the aim of turning into a data scientist, while not ever realizing what a data engineer is, or what that role entails. These data engineers are very important parts of any data science project and their demand within the business is growing exponentially within the current data-rich environment

Q8: What is overfitting?
A: In statistics, overfitting is “the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably”.

Q9: How to avoid overfitting?
A: These are some techniques to avoid overfitting:

1. Don’t use more degrees than you need for fitting your data
2. Visualize your data initial to ascertain however complicated of a curve there would possibly very be
3. Visualize the fit – is your curve going out of its thanks to accommodating outliers
4. A high r-squared merely means that your curve fits your training data well; however, it’s going to not be a decent predictor

Q10: What is k Fold cross-validation?
A: Cross-validation is a powerful preventative measure against overfitting.
The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model.
In commonplace k-fold cross-validation, we tend to partition the info into k subsets, known as folds. Then, we tend to iteratively train the algorithmic program on k-1 folds whereas victimization the remaining fold because of the check set (called the “holdout fold”).

Cross-validation permits you to tune hyperparameters with solely your original training set. this permits you to stay your check set as a very unseen dataset for choosing your final model.

Q11: What is data cleaning?
A: The reality is, much of your time as a data scientist will be spent preparing and “cleaning” your data

• Outliers (Ex: Weblog data)(Unwanted data)
• Missing Data
• Malicious Data (cheating, Fake recommendations)
• Erroneous Data (Software Bug with wrong value)
• Irrelevant Data (List of presidents in world & and Irrelevant data prime ministers in the list )
• Inconsistent Data (USA, United States, US, Same Book in different countries in different names with same value add)
• Formatting (07-11-2020,11-07-2020 or +91-754-087-6397, +917540876397)

Q12: What is Normalization ?
A: Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalisation is to alter the values of numeric columns within the dataset to use a typical scale, while not distorting variations within the ranges of values or losing data. normalisation is additionally needed for a few algorithms to model the data properly.

Q13: What is feature engineering?
A: Applying your knowledge of the data and the model you’re using to create better features to train your model with.

• Which features should I use?
• Do I need to transform these features in some way?
• How do I handle missing data?
• Should I create new features from the existing ones?

You can’t just throw in raw data and expect good results
This is the art of machine learning; where expertise is applied

Q14: What is Imbalance data?
A: Imbalance data distribution is an important part of the machine learning workflow. An unbalanced dataset means that instances of 1 of the 2 categories are above the opposite, in a different way, the amount of observations isn’t similar for all the categories in an exceedingly classification dataset. This downside is two-faced not solely within the binary category information however additionally within the multi-class data
The large discrepancy between “positive” and “negative” cases

• i.e., fraud detection. Fraud is rare, and most rows will be not-fraud

Don’t let the terminology confuse you; “positive” doesn’t mean “good”

• It means the thing you’re testing for is what happened.
• If your machine learning model is made to detect fraud, then fraud is the positive case.

Mainly a problem with neural networks

Q15: What is SMOTE?
A: SMOTE stands for Synthetic Minority Oversampling Technique. This is a statistical technique for increasing the number of cases in your dataset in a very balanced approach. The module works by generating new instances from existing minority cases that you just provide as input. This implementation of SMOTE doesn’t amend the number of majority cases

Q16: What is Binning?
A: Binning is a technique that accomplishes exactly what it sounds like. It will take a column with continuous numbers and place the numbers in “bins” supported ranges that we tend to verify. this can provide us a new categorical variable feature.
Example: estimated ages of people
Put all 20-somethings in one classification, 30-somethings in another, etc

Feedback Received…

From our AWS-ML day 1 & day 2 session, we received some good feedback from our trainees who had attended the session, so here is a sneak peek of it.
To know more about AWS-ML certification and whether it is the right certification for you, read our blog on AWS Certified Machine Learning – Specialty[MLS-C01]: Everything you must know

Quiz Time (Sample Exam Questions)!

With our AI/ML & Azure Data Science training program, we cover 150+ sample exam questions to help you prepare for the certification DP-100.
Check out one of the questions and see if you can crack this…

Ques: Which of the following of a random variable is a measure of spread?
A) Variance
B) Standard deviation
C) Empirical mean
D) All of the mentioned

Comment with your answer & we will tell you if you are correct or not!!

Related/References

• AWS Certificate Manager: Overview, Features and How it Works?
• AWS Database Services – Amazon RDS, Aurora, DynamoDB, ElastiCache
• Multi-Account Management Using AWS Organizations
• AWS Certified Solutions Architect: Roles & Responsibilities
• Amazon Kinesis Overview, Features And Benefits
• AWS Route 53 Introduction
• Create And Connect To Amazon AWS Windows EC2 Instance
• AWS Certified Machine Learning Specialty: All You Need To Know

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