Six-Months Diploma in AI and Machine Learning | Data Science Diploma with Artificial Intelligence

6-Months Diploma in AI and Machine Learning 

In the current world, which is driven by data, organizations place a large amount of reliance on data in order to make decisions, discover innovative solutions to problems, and propel innovation. As a result, there is an increasing demand for professionals who are data scientists. The comprehensive data science diploma that is offered by Craw Security is designed to equip you with the information and abilities that are necessary to be successful in this fast-paced sector. The diploma is offered for a period of six months. Python, machine learning, and artificial intelligence are the three primary subjects that are brought up throughout the course of this certificate.

What Will You Learn in 6-Months Diploma in AI and Machine Learning?

Learners who have a solid understanding of how to achieve something spectacular with statistics, insights, model building, and analysis have the opportunity to seek their bright future in the highly booming field of Data Science Diploma with AI through the world-class training faculties at Craw Security.  An individual who is interested in learning will, in all honesty, enjoy the best learning environment at Craw Security, offering them the opportunity to learn through the most carefully curated training environment.

In this marvelous discipline of data science, one will be able to obtain a diploma in data science after completing six months of study, during which time they will learn the following:

1. Artificial Intelligence

2. Machine Learning

3. Python Programming for Data Science

Let’s take a more in-depth look at the topics that you will be learning in this class, as well as the ways in which having this knowledge will help you become an effective data science expert.

Skills Required for Data Scientists

A data scientist’s success in this industry necessitates a combination of technical and non-technical skills. These abilities encompass:

1. Programming Skills,

2. Statistical Analysis,

3. Machine Learning,

4. Data Visualization,

5. Big Data Tools,

6. Communication Skills, etc.

Why Choose Craw Security to Learn 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?

When it comes to achieving good growth in life and considerable advancement in one’s job, selecting Craw Security, the Best AI Training Institute in Singapore, as the provider of comprehensive training in Data Science with AI from highly sought-after professionals who have many years of quality expertise can be quite advantageous. In this regard, you would want to take into consideration the following top factors before selecting Craw Security as your desired partner in this field:

Complete freedom to select the mode of instruction, including:

VILT (Virtual Instructor-Led Training) Sessions

Pre-recorded Video Sessions, and

Offline Classroom Sessions.

World-Class Experienced Training Faculties.

Both soft and hard copies of the study materials are available.

Study resources that have been verified by data scientists who are employed in a variety of firms all over the world.

After completing the course and passing the internal exam(s), the student will receive a certificate of completion.

Job Scope of Data Scientists: Exploring a Promising Career Path

Within the context of modern, technologically advanced culture, the position of Data Scientist has emerged as one of the most coveted employment opportunities that are open to individuals. It is anticipated that the demand for skilled data scientists will continue to rise as a consequence of the increasing reliance that organizations have on data in order to make decisions, innovate, and keep their advantage over their competitors. Due to the fact that this career involves a wide variety of different sectors, it is a line of work that has the potential to be both lucrative and quite diverse.

As part of an overview of the job scope of data scientists, the following is a summary of the key industries that have a high demand for data scientists, as well as the career opportunities that are available to data scientists.

Career Prospects and Growth Opportunities

A data scientist should anticipate a career trajectory that is both dynamic and offers a diverse range of responsibilities and opportunities for specialization. The following are some of the most prevalent job titles in this sector:

Python Programming for Data Science

It is Python that serves as the basis upon which contemporary data science is constructed. In order to provide students with a more basic knowledge of the complexities inherent in data analysis, the purpose of this course is to provide students with a firm foundation in Python programming. This is what you are going to get:

Module 1 : Introduction

. Programming language introduction

. Translators (Compiler, Interpreter)

. Uses of computer programs

. Algorithm

. Flow chart

Module 2 : Python Introduction

. History

. Why python created

. Fields of use

. Use of Python in Cybersecurity

. Reasons for using Python

. Syntax

. Installation of IDE

Module 3 : Variables

. What is variable

. Declaration rules

. Multiple variable declarations

. Valid and invalid variables

. Type casting

Module 4 : Data Type

. Introduction

. Discuss all data types

. Use type() to show dynamically typed language

. String

. List

. List: List Comprehension

. Tuple

. Dictionary

. Set

Module 5 : Operators

. Introduction

. Arithmetic operators

. Assignment operators

. Comparison operators

. Logical operators

. Identity operator

. Bitwise operator

. Membership operator

Module 6 : Control Flow

. Introduction to Conditional Statement

. Conditional Statement: if

. Conditional Statement: elif

. Conditional Statement: else

. Conditional Statement: Nested if

. Introduction to Looping

. Looping: for loop

. Looping: While loop

. Looping: Nested loop

Module 7 : Function

. Introduction function

. Declaration, calling of function

. Lambda function

. Filter

. Reduce function

. Map function

Module 8 : File Handling

. Introduction

. Text file handling

. Binary file handling

Module 9 : Object Oriented Programming

. Introduction

. Difference b/w procedural programming and OOPS

. Class

. Object

. Encapsulation

. Inheritance

. Abstraction

. Polymorphism

Module 10 : Web Scrapping

. Introduction

. Introduce basic HTML tags

. Introduction to Requests Library

. Introduction to bs4

. Scrapping through Beautiful Soup

Module 11 : Numpy

. Creating NumPy arrays

. Properties of Array

. Indexing and Slicing

. Aggregate Functions

. Numpy Functions

. Vectorization

. Broadcasting

. Boolean indexing

Module 12 : Pandas

. Series

. Data Frame

. Data Frame Properties

. Data Frame indexing and slicing

. Reading data from various sources

. Dataframe Functions

. Pandas Functions

. Filter Data

Module 13 : Visualization

. Introduction to Matplolib and Seaborn

. Properties of plots

. Line plot

. Histogram / Distplot

. Bar plot/ Count Plot

. Pie Chart

. Heat Map

. Scatter Plot

. Box Plot

Machine Learning

The transformation that is taking place in the manner in which businesses study and react to data is being brought about by machine learning, which is the driving force behind artificial intelligence. You will be guided through the principles of machine learning as well as the algorithms that underpin it in this course. Some of the algorithms that will be covered include the following:

Module 1 : Welcome to the ML experience

. Importance of ML in your career

. AI FAMILY TREE

. System requirements

. Prerequisites

Module 2 : Machine learning basics

. What is machine learning

. Classification and regression

. Supervised and Unsupervised

. Preparing for your ML journey

Module 3 : EDA and Preprocessing

. Reading/Writing Excel, CSV, and Other File Formats

. Basic EDA (Info, Shape, Describe)

. Handling Missing Values

. Handling Outliers

. Handling Skewness

. Encoding Categorical Data (One-Hot, Label Encoding)

. Data Normalization and Scaling (MinMax, Standard Scaler)

. Feature Engineering

. Correlation Analysis and Heatmaps

. Train-Test Split & Cross-validation Strategy

Module 4 : Introduction to Regression

. Simple Linear Regression

. Multiple Linear Regression

. Lost and Cost Function (Mean Squared Error)

. Regression Evaluation Metrics

. Assumptions of Linear Regression

. Polynomial Regression

Module 5 : Regularization

. Overfitting vs Underfitting

. Bias Variance trade-off

. Ridge and Lasso Regularization

. Cross Validation

Module 6 : Introduction to Classification

. Introduction to Logistic Regression

. Model Evaluation: Accuracy, Precision & Recall

. Model Evaluation: F1 Score, Confusion Matrix

. SVM

. Decision Tree

Module 7 : Ensemble Learning

. What is Ensemble Learning

. Bagging

. Random Forest

. Introduction to Boosting

. Boosting: Adaboost

. Boosting: Gradient Boost

. Boosting: XG Boost

Module 8 : Introduction to Hyperparameter Tuning

. Hyperparameter Tuning: GridsearchCV

. Hyperparameter Tuning: RandomizedSearchCV

. Model Selection Guide

. Selecting the Right Evaluation

Module 9 : Unsupervised ML

. Introduction to Clustering

. K-Means Clustering

. Principal Component Analysis

Artificial Intelligence

In many different industries all around the world, including the healthcare and financial sectors, artificial intelligence (AI) is producing a revolution. This revolution is causing a revolution. Through the completion of this course, you will be given an introduction to the field of artificial intelligence as well as the different applications that fall under this field. The following topics are brought forward for discussion:

Module 1 : Artificial Neural Network and Regularization

. Single layered ANN

. Multiple Layered ANN

. Vanishing Gradient problem

. Dropout

Module 2 : Introduction to Deep Learning

. Difference between ML, DL, and AI

. Activation functions

. Gradient Descent

Module 3 : Computer Vision & OpenCV

. What is Computer Vision

. History of Computer Vision

. Tools & Technology used in Computer Vision

. Application of Computer Vision

. What is OpenCV

. Installation of OpenCV

. The first program with OpenCV

. Reading & Writing Images

. Capture Videos from Camera

. Reading & Saving Videos

Module 4 : Image Classification

. Haar Cascade Classifier

. Image Classification with CNN

Module 5 : Object Detection

. What is Object Detection

. Object Detection using Haar Cascade

Module 6 : Introduction to NLP

. What is Natural Language Processing

. Uses of NLP

. Application of NLP

. Components of NLP

. Stages of NLP

. Chatbot

Module 7 : Text Preprocessing

. Tokenization

. Non-Alphabets Removal

. Bag of Words

. Stemming & Lemmatization

Module 8 : Sentiment Analysis

. What is Sentiment Analysis

. Challenges in Sentiment Analysis

. Handling Emotions

. Sentiment Analysis with ANN

Module 9 : Sequence Model

. Sequential Data

. Recurrent Neural Network

. Architecture of RNN

. Vanishing Gradient Problem in RNN

. Long Short-Term Memory

. and Career Path

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Benefits of Learning Artificial Intelligence (AI) and Machine Learning

In the present day, data is frequently referred to as the “new oil” due to its role in the advancement of science, the growth of enterprises, and innovation. As the implementation of plans by a growing number of firms in all sectors becomes more data-driven, the demand for experienced individuals in the field of data science is on the rise. Learning data science is one of the most rewarding talents to acquire, as it offers numerous advantages, such as the capacity to solve problems and advance in one’s career.

The most significant benefit of studying data science is that it is a worthwhile investment for your future.

1. High Demand and Lucrative Career Opportunities

2. Diverse Career Paths and Flexibility

3. Solving Real-World Problems

4. Enhanced Problem-Solving and Analytical Thinking

5. Empowerment through Data Literacy

6. Opportunities for Innovation and Creativity

7. Mastering Cutting-Edge Tools and Technologies

8. Continuous Learning and Adaptation

9. Impactful Career with Global Reach

Who Should Do 6 Months Diploma in Learning Artificial Intelligence (AI) and Machine Learning?

Enrolling in this diploma program would be advantageous for the following individuals:

1. Fresh Graduates and Students,

2. Professionals Looking for a Career Change,

3. IT Professionals Looking to Upskill,

4. Business Professionals and Managers,

5. Entrepreneurs and Startups,

6. Researchers and Academics,

7. Anyone Interested in Artificial Intelligence and Machine Learning,

8. People Looking for Remote Work Opportunities, etc.

Market Share of Data Science

In 2023, it was anticipated that the total value of the global market for data science platforms would amount to 103.93 billion US dollars. It is anticipated that it will increase at a compound annual growth rate (CAGR) of 24.7% over the course of the projected period, going from 133.12 billion USD in 2024 to 776.86 billion USD by 2032.

The term “data science platform” refers to a piece of software that serves as a foundation for the entirety of the life cycle of a data science development project. For data scientists, these platforms are crucial tools since they allow for the construction of models, the distribution of models, and the investigation of models. Additionally, it offers a computer infrastructure that is capable of handling enormous amounts of data and makes data processing and visualization much simpler. Users are able to collaborate more easily thanks to the consolidated platform that these solutions provide.