Six-Months Diploma in Artificial Intelligence (AI) and Machine Learning (ML)
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6-Months Diploma in Artificial Intelligence and Machine Learning | Data Science Diploma with AI
Are you interested in learning more about the methods employed in machine learning and artificial intelligence? If so, a 6-Months Diploma in AI and Machine Learning would be the best choice for you.
This diploma program will teach you how to work on artificial intelligence-enabled devices using a variety of methods and tools. Why do we wait? Now let’s get right to the point!
What Will You Learn in 6-Months Diploma in AI and Machine Learning?
You will learn about the following things in the 6-Months Diploma in AI and Machine Learning offered by Craw Security:
a) Python Programming: A solid foundation in Python, the language of choice for AI and machine learning.
b) Data Science Fundamentals: Learn how to clean, preprocess, and analyze large amounts of data.
c) Machine Learning Algorithms: Understand and apply a range of machine learning algorithms, including linear regression, decision trees, logistic regression, random forests, and others.
d) Deep Learning: Investigate neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for difficult tasks like picture and speech recognition.
e) Natural Language Processing (NLP): Analyze techniques for processing and decoding human language.
f) Computer Vision: Gain expertise in developing systems that can interpret and interpret visual data.
g) AI and ML Tools and Libraries: Gain hands-on experience with popular tools like TensorFlow, PyTorch, and Scikit-learn.
h) AI and ML Project Implementation: To apply your knowledge and build a strong portfolio, work on real-world projects.
Python Programming for Data Science
Python Programming for Data Science
Python serves as the cornerstone of contemporary data science. The goal of this course is to give students a strong foundation in Python programming so they can understand the complexities of data analysis more easily. You will obtain:
Introduction
a) Programming language introduction
b) Translators (Compiler, Interpreter)
c) Uses of computer programs
d) Algorithm
e) Flow chart
Python Introduction
a) History
b) Why python created
c) Fields of use
d) Use of Python in Cybersecurity
e) Reasons for using Python
f) Syntax
g) Installation of IDE
Variables
a) What is variable
b) Declaration rules
c) Multiple variable declarations
d) Valid and invalid variables
e) Type casting
Data Type
a) Introduction
b) Discuss all data types
c) Use type() to show dynamically typed language
d) String
e) List
f) List: List Comprehension
g) Tuple
h) Dictionary
i) Set
Operators
a) Introduction
b) Arithmetic operators
c) Assignment operators
d) Comparison operators
e) Logical operators
f) Identity operator
g) Bitwise operator
h) Membership operator
Control Flow
a) Introduction to Conditional Statement
b) Conditional Statement: if
c) Conditional Statement: elif
d) Conditional Statement: else
e) Conditional Statement: Nested if
f) Introduction to Looping
g) Looping: for loop
h) Looping: While loop
i) Looping: Nested loop
Function
a) Introduction function
b) Declaration, calling of function
c) Lambda function
d) Filter
e) Reduce function
f) Map function
File Handling
a) Introduction
b) Text file handling
c) Binary file handling
Object-Oriented Programming
a) Introduction
b) Difference between procedural programming and OOPS
c) Class
d) Object
e) Encapsulation
f) Inheritance
g) Abstraction
h) Polymorphism
Web Scrapping
a) Introduction
b) Introduce basic HTML tags
c) Introduction to Requests Library
d) Introduction to bs4
e) Scrapping through Beautiful Soup
Numpy
a) Creating NumPy arrays
b) Properties of Array
c) Indexing and Slicing
d) Aggregate Functions
e) Numpy Functions
f) Vectorization
g) Broadcasting
h) Boolean indexing
Pandas
a) Series
b) Data Frame
c) Data Frame Properties
d) Data Frame indexing and slicing
e) Reading data from various sources
f) Dataframe Functions
g) Pandas Functions
h) Filter Data
Visualization
a) Introduction to Matplolib and Seaborn
b) Properties of plots
c) Line plot
d) Histogram /Distplot
e) Bar plot/Count Plot
f) Pie Chart
g) Heat Map
h) Scatter Plot
i) Box Plot
Machine Learning
Artificial intelligence is powered by machine learning, which is changing how companies evaluate and react to data. The principles of machine learning and the algorithms that support it will be covered in this lesson, including the following:
Welcome to the ML experience
a) Importance of ML in your career
b) AI FAMILY TREE
c) System requirements
d) Prerequisites
Machine learning basics
a) What is machine learning?
b) Classification and regression
c) Supervised and Unsupervised
d) Preparing for your ML journey
EDA and Preprocessing
a) Reading/Writing Excel, CSV, and Other File Formats
b) Basic EDA (Info, Shape, Describe)
c) Handling Missing Values
d) Handling Outliers
e) Handling Skewness
f) Encoding Categorical Data (One-Hot, Label Encoding)
g) Data Normalization and Scaling (MinMax, Standard Scaler)
h) Feature Engineering
i) Correlation Analysis and Heatmaps
j) Train-Test Split & Cross-validation Strategy
Introduction to Regression
a) Simple Linear Regression
b) Multiple Linear Regression
c) Lost and Cost Function (Mean Squared Error)
d) Regression Evaluation Metrics
e) Assumptions of Linear Regression
f) Polynomial Regression
Regularization
a) Overfitting vs Underfitting
b) Bias Variance trade-off
c) Ridge and Lasso Regularization
d) Cross Validation
Introduction to Classification
a) Introduction to Logistic Regression
b) Model Evaluation: Accuracy, Precision & Recall
c) Model Evaluation: F1 Score, Confusion Matrix
d) SVM
e) Decision Tree
Ensemble Learning
a) What is Ensemble Learning?
b) Bagging
c) Random Forest
d) Introduction to Boosting
e) Boosting: Adaboost
f) Boosting: Gradient Boost
g) Boosting: XG Boost
Introduction to Hyperparameter Tuning
a) Hyperparameter Tuning: GridsearchCV
b) Hyperparameter Tuning: RandomizedSearchCV
c) Model Selection Guide
d) Selecting the Right Evaluation
Unsupervised ML
a) Introduction to Clustering
b) K-Means Clustering
c) Principal Component Analysis
Artificial Intelligence
Around the world, artificial intelligence (AI) is revolutionizing several industries, including the financial and healthcare sectors. An introduction to artificial intelligence and its diverse applications will be given to you in this course. The following topics are covered:
Artificial Neural Network and Regularization
- a) Single-layered ANN
b) Multiple Layered ANN
c) Vanishing Gradient problem
d) Dropout
Introduction to Deep Learning
- a) Difference between ML, DL, and AI
b) Activation functions
c) Gradient Descent
Computer Vision & OpenCV
- a) What is Computer Vision?
b) History of Computer Vision
c) Tools & Technology used in Computer Vision
d) Application of Computer Vision
e) What is OpenCV?
f) Installation of OpenCV
g) The first program with OpenCV
h) Reading & Writing Images
i) Capture Videos from Camera
j) Reading & Saving Videos
Image Classification
- a) Haar Cascade Classifier
b) Image Classification with CNN
Object Detection
- a) What is Object Detection
b) Object Detection using Haar Cascade
Introduction to NLP
- a) What is Natural Language Processing?
b) Uses of NLP
c) Application of NLP
d) Components of NLP
e) Stages of NLP
f) Chatbot
Text Preprocessing
- a) Tokenization
b) Non-Alphabets Removal
c) Bag of Words
d) Stemming & Lemmatization
Sentiment Analysis
- a) What is Sentiment Analysis?
b) Challenges in Sentiment Analysis
c) Handling Emotions
d) Sentiment Analysis with ANN
Sequence Model
- a) Sequential Data
b) Recurrent Neural Network
c) Architecture of RNN
d) Vanishing Gradient Problem in RNN
e) Long Short-Term Memory.
Market Share of Data Science
In 2023, the data science platform market was projected to be worth USD 103.93 billion worldwide. It is anticipated to rise from USD 133.12 billion in 2024 to USD 776.86 billion by 2032 at a compound annual growth rate (CAGR) of 24.7% during the forecast period.
A data science platform is a piece of software that offers a platform for the entire life cycle of a data science project. These platforms are essential resources for data scientists because they facilitate the creation, sharing, and analysis of models.
It also makes data preparation and visualization easier and offers a large-scale computing infrastructure. These systems provide a centralized platform that makes it easier for users to work together.
Why Choose Craw Security to Learn a 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?
.Selecting Craw Security for thorough training in Data Science with AI from highly qualified experts with years of excellent experience can be very helpful for both substantial career growth and respectable personal development.
Before deciding on Craw Security as your ideal partner in this field, you can take into account the following important factors:
Complete freedom to select the learning mode, including:
a) VILT (Virtual Instructor-Led Training) Sessions
b) Pre-recorded Video Sessions, and
c) Offline Classroom Sessions.
Top-notch, highly skilled training staff.
● Both soft and hard copies of the study materials are available.
● Verified research materials from data scientists employed by a variety of global organizations.
● After completing the course and passing an internal exam, students receive a Certificate of Completion.
Market Share of Data Science
In 2023, the data science platform market was projected to be worth USD 103.93 billion worldwide. It is anticipated to rise from USD 133.12 billion in 2024 to USD 776.86 billion by 2032 at a compound annual growth rate (CAGR) of 24.7% during the forecast period.
A data science platform is a piece of software that offers a platform for the entire life cycle of a data science project. These platforms are essential resources for data scientists because they facilitate the creation, sharing, and analysis of models.
It also makes data preparation and visualization easier and offers a large-scale computing infrastructure. These systems provide a centralized platform that makes it easier for users to work together.
Why Choose Craw Security to Learn a 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?
.Selecting Craw Security for thorough training in Data Science with AI from highly qualified experts with years of excellent experience can be very helpful for both substantial career growth and respectable personal development.
Before deciding on Craw Security as your ideal partner in this field, you can take into account the following important factors:
Complete freedom to select the learning mode, including:
a) VILT (Virtual Instructor-Led Training) Sessions
b) Pre-recorded Video Sessions, and
c) Offline Classroom Sessions.
Top-notch, highly skilled training staff.
● Both soft and hard copies of the study materials are available.
● Verified research materials from data scientists employed by a variety of global organizations.
● After completing the course and passing an internal exam, students receive a Certificate of Completion.
Market Share of Data Science
In 2023, the data science platform market was projected to be worth USD 103.93 billion worldwide. It is anticipated to rise from USD 133.12 billion in 2024 to USD 776.86 billion by 2032 at a compound annual growth rate (CAGR) of 24.7% during the forecast period.
A data science platform is a piece of software that offers a platform for the entire life cycle of a data science project. These platforms are essential resources for data scientists because they facilitate the creation, sharing, and analysis of models.
It also makes data preparation and visualization easier and offers a large-scale computing infrastructure. These systems provide a centralized platform that makes it easier for users to work together.
Why Choose Craw Security to Learn a 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?
.Selecting Craw Security for thorough training in Data Science with AI from highly qualified experts with years of excellent experience can be very helpful for both substantial career growth and respectable personal development.
Before deciding on Craw Security as your ideal partner in this field, you can take into account the following important factors:
● Complete freedom to select the learning mode, including:
a) VILT (Virtual Instructor-Led Training) Sessions
b) Pre-recorded Video Sessions, and
c) Offline Classroom Sessions.
● Top-notch, highly skilled training staff.
● Both soft and hard copies of the study materials are available.
● Verified research materials from data scientists employed by a variety of global organizations.
● After completing the course and passing an internal exam, students receive a Certificate of Completion.
Job Scope of Data Scientists: Exploring a Promising Career Path
|
S.No. |
Job Profiles |
What? |
|
1. |
Technology |
The development of user insights, product improvement, and the quickening of innovation in technology companies all depend on data science. Data scientists are used by businesses like Google, Amazon, and Facebook to optimize algorithms, personalize content, and enhance customer experiences. |
|
2. |
Finance |
Data scientists support the financial industry by helping banks and other financial institutions anticipate market trends, assess risks, and spot fraudulent activity. They are developing models for risk management, credit scoring, and algorithmic trading, among other things. |
|
3. |
Healthcare |
By enabling predictive analytics for disease prevention, enhancing patient outcomes, and customizing treatments through the use of insights obtained from patient data, data science is revolutionizing the healthcare sector. |
|
4. |
Retail and E-commerce |
To enhance pricing, inventory control, and marketing strategies, data scientists are used in the retail sector to collect information on consumer behavior. Recommendation systems, like those used by Amazon and Netflix, are developed using data to enhance the overall customer experience. |
|
5. |
Manufacturing |
Enhancing production lines, anticipating equipment failures through predictive maintenance, and lowering operating costs through supply chain data analysis are the main concerns of data scientists in the manufacturing sector. |
|
6. |
Government and Public Policy |
Governments use data science to analyze public sector data, improve services, and advance smart city initiatives. It supports the process of making fact-based decisions in the areas of public health, education, and urban planning. |
Skills Required for Data Scientists
To succeed in this field, a data scientist needs to possess both technical and non-technical skills. Among these skills are:
a) Programming Skills,
b) Statistical Analysis,
c) Machine Learning,
d) Data Visualization,
e) Big Data Tools, and
f) Communication Skills, etc.
Career Prospects and Growth Opportunities
Following are some of the job profiles students can go for after completing the 6-Months Diploma in AI and Machine Learning offered by Craw Security:
a) Junior Data Scientist: The main duties of entry-level positions include gathering data, cleaning it, and helping with simple data analysis.
b) Data Analyst: Data analysts, who often act as middlemen, are primarily focused on interpreting and evaluating data to provide business insights.
c) Senior Data Scientist: Data scientists can take on more challenging assignments, manage projects, and create increasingly complex machine-learning models as their expertise increases.
d) Machine Learning Engineer: Data scientists move into careers that require them to build scalable machine learning models for use in business applications after gaining experience in the field.
e) Data Science Manager: Data scientists have the chance to move into leadership roles as their careers progress, where they oversee teams of data experts and develop data management strategies.
f) Chief Data Officer (CDO): In a senior executive role, this person is responsible for overseeing the company’s entire data management strategy and making sure the data assets of the company are optimized for the accomplishment of business goals.
Benefits of Learning Artificial Intelligence (AI) and Machine Learning
|
S.No. |
Advantages |
How? |
|
1. |
Career Opportunities |
There is a significant demand for skilled workers in the rapidly growing fields of AI and ML. This leads to the creation of many job opportunities across various industries. |
|
2. |
High Salaries |
Data scientists and machine learning engineers are among the highest-paid professionals in the technology industry. |
|
3. |
Innovation and Problem-Solving |
AI and ML are driving advancements in many fields by empowering people to come up with innovative solutions to difficult problems. |
|
4. |
Automation of Tasks |
AI and ML can automate repetitive tasks, freeing up time for more strategic and creative work. |
|
5. |
Data-Driven Decision Making |
AI and ML enable data-driven decision-making by analyzing large datasets and uncovering valuable information. |
|
6. |
Personal Growth |
Learning about AI and ML can significantly enhance your problem-solving and critical-thinking skills. |
|
7. |
Contributing to Social Impact |
AI and ML can be used to address global issues like healthcare, education, and climate change. |
|
8. |
Continuous Learning and Evolution |
The domains of AI and ML are constantly evolving to stay ahead of the curve and offer chances for lifelong learning. |
Who Should Do a 6 Months Diploma in Learning Artificial Intelligence (AI) and Machine Learning?
Following individuals can join the 6-Month Diploma in Artificial Intelligence (AI) and Machine Learning offered by Craw Security:
a) Software Engineers,
b) Data Analysts,
c) Data Scientists,
d) IT Professionals,
e) Computer Science, Engineering, Statistics, or Mathematics Graduates,
f) Students Pursuing STEM Degrees,
g) Business Analysts,
h) Market Researchers,
i) Financial Analysts, and
j) Healthcare Professionals.
To put it another way, machine learning is a technique that is utilized to accomplish artificial intelligence.
a)tSupervised Learning: We train models using data that has been labeled.
b)tUnsupervised Learning: Unlabeled data can be analyzed by models to discover trends.
c)tReinforcement Learning: Models acquire knowledge through the process of trial and error when they are rewarded or punished.
a)tCompetence in programming languages such as Python, R, Java, and others.
b)tThe ability to comprehend mathematical concepts such as linear algebra, probability, and calculus.
c)tData structures and algorithmic knowledge are both required.
d)tKnowledge of several machine learning tools, such as TensorFlow, PyTorch, or scikit-learn.
a)tAutonomous vehicles,
b)tChatbots and virtual assistants,
c)tFraud detection,
d)tPersonalized recommendations,
e)tMedical diagnosis,
f)tPredictive analytics, and
g)tRobotics, etc.
a)tAutomation of repetitive tasks,
b)tEnhanced decision-making through data insights,
c)tImproved efficiency and productivity,
d)tPersonalization of user experiences, and
e)tAbility to process and analyze massive amounts of data, etc.
a)tHealthcare,
b)tFinance,
c)tRetail,
d)tManufacturing,
e)tEducation,
f)tEntertainment, and
g)tAgriculture, etc.
a)tCosts of early investment that are high,
b)tInsufficient number of experts with the necessary skills,
c)tConcerns of an ethical nature surrounding the privacy of data and bias, and
d)tThere is a challenge in comprehending complicated models, etc.
a)tLanguages used for programming: Python and R
b)tExamples of frameworks and libraries include TensorFlow, PyTorch, and Scikit-learn.
c)tAWS, Google Cloud, and Microsoft Azure are examples of cloud platforms.
d)tTools for the representation of data include Tableau and Power BI.
Practical applications frequently rely on libraries and frameworks that have already been constructed
a)tGoogle AI Certification,
b)tMicrosoft Certified: Azure AI Engineer Associate,
c)tIBM AI Engineering Professional Certificate, and
d)tCoursera Machine Learning by Andrew Ng, etc.
a)tLearning programming (Python is a good start),
b)tStudying ML basics through online courses (e.g., Coursera, edX),
c)tPracticing with projects and datasets, and
d)tExploring AI frameworks like TensorFlow and PyTorch,
a)tAbuse of artificial intelligence for potentially harmful objectives,
b)tEthical conundrums that arise during the decision-making process and
c)tThe loss of jobs and the disparity in economic conditions, etc.
