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

Organizations heavily rely on data to make decisions, find creative solutions to issues, and spur innovation in today’s data-driven environment. Consequently, there is a growing need for data scientists as professionals. Craw Security offers a comprehensive data science program that is intended to give you the knowledge and skills you need to succeed in this rapidly evolving industry. For a duration of 6 months, the diploma is available. In this certificate, the three main topics covered are artificial intelligence, machine learning, and Python.

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

Through the top-notch training faculty at Craw Security, learners who possess a strong grasp of how to accomplish something amazing with statistics, insights, model building, and analysis can pursue their bright future in the rapidly expanding field of Data Science Diploma with AI.  To be honest, everyone who is eager to learn will love Craw Security’s best learning environment, which gives them the chance to learn in the most meticulously designed training setting.

After completing six months of education, one can acquire a diploma in this amazing field of data science, where they will gain knowledge of the following topics:

  • Artificial Intelligence
  • Machine Learning
  • Python Programming for Data Science

Let’s take a closer look at the subjects you will be studying in this course and how mastering them will enable you to become a successful data science specialist.

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

Choosing Craw Security, the Best AI Training Institute in Singapore, as the supplier of thorough training in Data Science with AI from highly sought-after professionals with many years of high-quality expertise, can be quite beneficial when it comes to attaining positive growth in life and significant advancement in one’s career. Before choosing Craw Security as your ideal partner in this industry, you should think about the following important factors:
● Total autonomy in choosing the teaching method, including:
○ VILT (Virtual Instructor-Led Training) Sessions
○ Pre-recorded Video Sessions, and
○ Offline Classroom Sessions.
● World-Class Experienced Training Faculties.
● The study materials are available in hard copy and soft copy.
● Examine materials that have been validated by data scientists working for various companies worldwide.
● A certificate of completion will be given to the student upon completion of the course and successful completion of the internal exam or exams.

Python is the foundation upon which modern data science is built. The goal of this course is to give students a solid foundation in Python programming so they may gain a basic understanding of the complexity involved in data analysis. What you will receive is as follows:

  1. Introduction
    1. Programming language introduction
    2. Translators (Compiler, Interpreter)
    3. Uses of computer programs
    4. Algorithm
    5. Flow chart
  2. Python Introduction
    1. History
    2. Why python created
    3. Fields of use
    4. Use of Python in Cybersecurity
    5. Reasons for using Python
    6. Syntax
    7. Installation of IDE
  3. Variables
    1. What is variable
    2. Declaration rules
    3. Multiple variable declarations
    4. Valid and invalid variables
    5. Type casting
  4. Data Type
    1. Introduction
    2. Discuss all data types
    3. Use type() to show dynamically typed language
    4. String
    5. List
    6. List: List Comprehension
    7. Tuple
    8. Dictionary
    9. Set
  5. Operators
    1. Introduction
    2. Arithmetic operators
    3. Assignment operators
    4. Comparison operators
    5. Logical operators
    6. Identity operator
    7. Bitwise operator
    8. Membership operator
  6. Control Flow
    1. Introduction to Conditional Statement
    2. Conditional Statement: if
    3. Conditional Statement: elif
    4. Conditional Statement: else
    5. Conditional Statement: Nested if
    6. Introduction to Looping
    7. Looping: for loop
    8. Looping: While loop
    9. Looping: Nested loop
  7. Function
    1. Introduction function
    2. Declaration, calling of function
    3. Lambda function
    4. Filter
    5. Reduce function
    6. Map function
  8. File Handling
    1. Introduction
    2. Text file handling
    3. Binary file handling
  9. Object Oriented Programming
    1. Introduction
    2. Difference b/w procedural programming and OOPS
    3. Class
    4. Object
    5. Encapsulation
    6. Inheritance
    7. Abstraction
    8. Polymorphism
  10. Web Scrapping
    1. Introduction
    2. Introduce basic HTML tags
    3. Introduction to Requests Library
    4. Introduction to bs4
    5. Scrapping through Beautiful Soup
  11. Numpy
    1. Creating NumPy arrays
    2. Properties of Array
    3. Indexing and Slicing
    4. Aggregate Functions
    5. Numpy Functions
    6. Vectorization
    7. Broadcasting
    8. Boolean indexing
  12. Pandas
    1. Series
    2. Data Frame
    3. Data Frame Properties
    4. Data Frame indexing and slicing
    5. Reading data from various sources
    6. Dataframe Functions
    7. Pandas Functions
    8. Filter Data
  13. Visualization
    1. Introduction to Matplolib and Seaborn
    2. Properties of plots
    3. Line plot
    4. Histogram / Distplot
    5. Bar plot/ Count Plot
    6. Pie Chart
    7. Heat Map
    8. Scatter Plot
    9. Box Plot

Machine learning, which is the engine underlying artificial intelligence, is causing a revolution in the way businesses analyze and respond to data. This course will walk you through the fundamentals of machine learning and the algorithms that support it. The following are a few of the algorithms that will be discussed:

  1. Welcome to the ML experience
    1. Importance of ML in your career
    2. AI FAMILY TREE
    3. System requirements
    4. Prerequisites
  2. Machine learning basics
    1. What is machine learning
    2. Classification and regression
    3. Supervised and Unsupervised
    4. Preparing for your ML journey
  3. EDA and Preprocessing
    1. Reading/Writing Excel, CSV, and Other File Formats
    2. Basic EDA (Info, Shape, Describe)
    3. Handling Missing Values
    4. Handling Outliers
    5. Handling Skewness
    6. Encoding Categorical Data (One-Hot, Label Encoding)
    7. Data Normalization and Scaling (MinMax, Standard Scaler)
    8. Feature Engineering
    9. Correlation Analysis and Heatmaps
    10. Train-Test Split & Cross-validation Strategy
  4. Introduction to Regression
    1. Simple Linear Regression
    2. Multiple Linear Regression
    3. Lost and Cost Function (Mean Squared Error)
    4. Regression Evaluation Metrics
    5. Assumptions of Linear Regression
    6. Polynomial Regression
  5. Regularization
    1. Overfitting vs Underfitting
    2. Bias Variance trade-off
    3. Ridge and Lasso Regularization
    4. Cross Validation
  6. Introduction to Classification
    1. Introduction to Logistic Regression
    2. Model Evaluation: Accuracy, Precision & Recall
    3. Model Evaluation: F1 Score, Confusion Matrix
    4. SVM
    5. Decision Tree
  7. Ensemble Learning
    1. What is Ensemble Learning
    2. Bagging
    3. Random Forest
    4. Introduction to Boosting
    5. Boosting: Adaboost
    6. Boosting: Gradient Boost
    7. Boosting: XG Boost
  8. Introduction to Hyperparameter Tuning
    1. Hyperparameter Tuning: GridsearchCV
    2. Hyperparameter Tuning: RandomizedSearchCV
    3. Model Selection Guide
    4. Selecting the Right Evaluation
  9. Unsupervised ML
    1. Introduction to Clustering
    2. K-Means Clustering
    3. Principal Component Analysis

Artificial intelligence (AI) is causing a revolution in a wide range of businesses worldwide, including the financial and healthcare sectors. Another revolution is being sparked by this one. You will receive an introduction to the field of artificial intelligence and its various applications upon completion of this course. The following subjects are presented for conversation:

  1. Artificial Neural Network and Regularization
    1. Single layered ANN
    2. Multiple Layered ANN
    3. Vanishing Gradient problem
    4. Dropout
  2. Introduction to Deep Learning
    1. Difference between ML, DL, and AI
    2. Activation functions
    3. Gradient Descent
  3. Computer Vision & OpenCV
    1. What is Computer Vision
    2. History of Computer Vision
    3. Tools & Technology used in Computer Vision
    4. Application of Computer Vision
    5. What is OpenCV
    6. Installation of OpenCV
    7. The first program with OpenCV
    8. Reading & Writing Images
    9. Capture Videos from Camera
    10. Reading & Saving Videos
  4. Image Classification
    1. Haar Cascade Classifier
    2. Image Classification with CNN
  5. Object Detection
    1. What is Object Detection
    2. Object Detection using Haar Cascade
  6. Introduction to NLP
    1. What is Natural Language Processing
    2. Uses of NLP
    3. Application of NLP
    4. Components of NLP
    5. Stages of NLP
    6. Chatbot
  7. Text Preprocessing
    1. Tokenization
    2. Non-Alphabets Removal
    3. Bag of Words
    4. Stemming & Lemmatization
  8. Sentiment Analysis
    1. What is Sentiment Analysis
    2. Challenges in Sentiment Analysis
    3. Handling Emotions
    4. Sentiment Analysis with ANN
  9. Sequence Model
    1. Sequential Data
    2. Recurrent Neural Network
    3. Architecture of RNN
    4. Vanishing Gradient Problem in RNN
    5. Long Short-Term Memory

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Market Share of Data Science

It was projected that the global market for data science platforms would be worth 103.93 billion US dollars in 2023. Over the predicted period, it is expected to rise at a compound annual growth rate (CAGR) of 24.7%, from 133.12 billion USD in 2024 to 776.86 billion USD by 2032. A software program that acts as the basis for a data science development project's whole life cycle is referred to as a “data science platform.” Because they enable the creation, distribution, and analysis of models, these platforms are essential resources for data scientists. It also provides a computer architecture that can handle massive volumes of data and greatly simplifies data processing and visualization. Because these solutions offer a centralized platform, users may cooperate more readily.

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Job Scope of Data Scientists: Exploring a Promising Career Path

In today’s technologically sophisticated society, becoming a data scientist has become one of the most sought-after career paths available to prospective employees. Because businesses are depending more and more on data to make choices, innovate, and maintain an advantage over rivals, there will likely be a further increase in the need for qualified data scientists. This is a career that has the potential to be both profitable and highly varied because it encompasses a wide range of various areas.

The important industries with a strong demand for data scientists and the career options open to them are summarized here as part of an overview of the employment scope of data scientists.

Technology

Data science plays a crucial role in technology businesses’ efforts to improve products, produce user insights, and spur innovation. Data scientists are essential to businesses like Google, Amazon, and Facebook because they improve user experiences, customize content, and optimize algorithms.

Finance

By helping banks and other financial institutions anticipate market trends, assess risks, and spot fraudulent conduct, data scientists support the financial industry. Models for risk management, credit evaluation, and algorithmic trading are presently being developed.

Healthcare

The healthcare sector is undergoing a data science revolution by employing insights from patient data, which is enabling predictive analytics for illness prevention, bettering patient outcomes, and customizing treatments.

Retail and E-commerce

Data scientists work in the retail industry to improve pricing, inventory control, and marketing tactics. Data is used to develop recommendation systems that aim to improve the entire customer experience, much to the systems used by Amazon and Netflix.

Manufacturing

In the manufacturing sector, data scientists are mostly concerned with enhancing production lines, anticipating equipment failures through predictive maintenance, and lowering operating expenses through supply chain data analysis.

Government and Public Policy

Data science is used by governments to evaluate public sector data, enhance services, and further smart city projects. It facilitates the creation of fact-based judgments in the fields of education, public health, and urban planning.

Career Prospects and Growth Opportunities

For data scientists, a dynamic career path with a wide variety of duties and specialized chances is what they should expect. Some of the most common job titles in this industry are as follows:

Junior Data Scientist

Data collection, cleaning, and support for basic data analysis are the main responsibilities of entry-level roles.

Data Analyst

Data analysts frequently act as middlemen and are primarily concerned with interpreting and evaluating data to produce business insights.

Senior Data Scientist

As their experience grows, data scientists can take on more responsibility for projects, create more intricate machine-learning models, and take on more difficult tasks.

Machine Learning Engineer

When data scientists gain experience in machine learning, they go on to work in fields that require them to create scalable machine learning models for business applications.

Data Science Manager

Data scientists have the chance to take on leadership responsibilities as their careers evolve, which include managing groups of data experts and creating data strategies.

Chief Data Officer (CDO)

In a senior executive position, this person is in charge of managing the organization’s data strategy and ensuring that the information assets of the firm are maximized to meet business goals.

Benefits of Learning Artificial Intelligence (AI) and Machine Learning

These days, data is sometimes referred to as the “new oil” because of its contribution to innovation, business expansion, and scientific advancement. The need for skilled workers in the data science profession is increasing as more and more businesses across all industries implement plans that are more data-driven. One of the most fulfilling skills to learn is data science, which has several benefits, including the ability to solve problems and grow in one’s job.
Studying data science is a wise investment for your future, which is its biggest advantage:
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

Skills Required for Data Scientists

To succeed in this field, a data scientist must possess both technical and non-technical abilities. These skills include:
● Programming Skills,
● Statistical Analysis,
● Machine Learning,
● Data Visualization,
● Big Data Tools,
● Communication Skills, etc.

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

The following people would benefit from enrolling in this diploma program:
● Fresh Graduates and Students,
● Professionals Looking for a Career Change,
● IT Professionals Looking to Upskill,
● Business Professionals and Managers,
● Entrepreneurs and Startups,
● Researchers and Academics,
● Anyone Interested in Artificial Intelligence and Machine Learning,
● People Looking for Remote Work Opportunities, etc.

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One of the finest institutions in the area regarding cybersecurity. I've learnt a lot of knowledgeable topics and am still learning. So far I've done Networking was taught to me by prayas sir who really knows his stuff, CEH was taught to me by Mohit sir who is very helpful and polite, Python was taught by Ajay sir. Trainers and management staff are very helpful and polite.

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I have joined craw in the month of december and i have completed networking module ….right now i am learning linux module…it also going to complete soon…trainer which is anees sir is also soo supportive he taught us about new things in linux and solve our doubt . Craw academy is a only platform in india which gives opportunity to learning students ..…also those students who dont belong to IT background.

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Basic Networking Training Course FAQs

The process of teaching robots to think, learn, and make decisions similarly to humans in order to mimic human ​perception is known as artificial intelligence or AI. Computer vision, robotics, expert systems, and natural language processing are just a few of the subfields that fall under the umbrella of artificial intelligence.

A branch of artificial intelligence called machine learning (ML) focuses on developing systems that can learn and improve from data without explicit programming. In order to find patterns and make predictions, algorithms are required.

While artificial intelligence (AI) is the broad idea of creating intelligent systems, machine learning (ML) is a subset of AI that enables computers to learn from data.  To put it another way, machine learning is a technique used to create artificial intelligence.  Hence, we can say that by employing varied ML tactics, a professional AI expert can nicely obtain many AI-based solutions that can ease the workload of any operation that used to take many hours beforehand.

The following are the three main categories of machine learning:

  • Supervised Learning: Models are trained using labeled data.
  • Unsupervised Learning: To find trends in unlabeled data, models can be used for analysis.
  • Reinforcement Learning: Models go through a process of trial and error when they are rewarded or punished, which ultimately results in knowledge acquisition.

The following fundamental abilities are necessary to understand the concepts of machine learning and artificial intelligence:

  • Competent in several programming languages, such as Java, R, and Python.
  • The ability to comprehend mathematical ideas like probability, calculus, and linear algebra
  • Data structures and algorithmic understanding are both crucial.
  • Proficiency with a range of machine learning tools, such as TensorFlow, PyTorch, and scikit-learn.

ML and AI are extensively utilized in:

  • Autonomous vehicles,
  • Chatbots and virtual assistants,
  • Fraud detection,
  • Personalized recommendations,
  • Medical diagnosis,
  • Predictive analytics,
  • Robotics, etc.

The following are the main benefits of AI and ML technologies:

  • Automating monotonous tasks,
  • Improved decision-making by using insights from data,
  • Increased efficiency and productivity,
  • Personalization of user experiences,
  • Among other things, the ability to process and analyze large amounts of data.

AI and ML are transforming industries like the following:

  • Healthcare,
  • Finance,
  • Retail,
  • Manufacturing,
  • Education,
  • Entertainment,
  • Agriculture, etc.
  • High costs associated with the initial investment,
  • There are not enough professionals with the necessary skills.
  • Ethical issues with bias and data privacy,
  • Understanding complex models, etc., is difficult.
  • The following is a list of some of the most popular tools in AI and ML technologies:

    • Python and R are programming languages.
    • TensorFlow, PyTorch, and Scikit-learn are some examples of frameworks and libraries.
    • AWS, Google Cloud, and Microsoft Azure are examples of cloud platforms.
    • Two tools for representing data are Tableau and Power BI.

Although it is beneficial to have a basic understanding of linear algebra, calculus, and statistics, there are many resources that make these subjects easier for starting students to understand. In real-world applications, pre-existing libraries and frameworks are increasingly used.

Although it is beneficial to have a basic understanding of linear algebra, calculus, and statistics, there are many resources that make these subjects easier for starting students to understand. In real-world applications, pre-existing libraries and frameworks are increasingly used.

AI has the ability to automate certain operations, which may result in job displacement in particular industries. But it also brings with it new duties in data analysis, technological management, and the advancement of artificial intelligence.

Hence, the answer can be in a mixed tone where AI can replace certain human jobs while it generates some jobs that were not present previously, especially those dedicated to AI or Artificial Intelligence.

The amount of time needed to learn a new skill depends on your goals and prior expertise. In contrast to advanced expertise, which may need years of study and practice, beginners can learn basic skills in as such as 6 to 12 months.

Artificial intelligence raises ethical questions about prejudice, privacy, and accountability. The development of artificial intelligence systems that are objective, transparent, and in line with societal norms is essential.

Some of the most well-known AI and ML certifications are as follows:

  • Google AI Certification,
  • Microsoft Certified: Azure AI Engineer Associate,
  • IBM AI Engineering Professional Certificate,
  • Coursera Machine Learning by Andrew Ng, etc.
  • Learning to program (Python is a great place to start)
  • Taking online courses to understand the basics of machine learning (e.g., Craw Security, edX, etc.),
  • Working with initiatives and datasets,
  • Examining frameworks for artificial intelligence like PyTorch and TensorFlow,
  • Future developments in machine learning and artificial intelligence will include improvements in autonomous systems, natural language processing, peripheral computing, and the incorporation of AI into everyday technologies.

  • In actuality, the dangers include:

    • The improper application of artificial intelligence to accomplish potentially harmful objectives,
    • Ethical issues that develop during the decision-making process,
    • Among other things, the loss of jobs and economic inequality.