Six-Months Diploma in Artificial Intelligence (AI) and Machine Learning
Batch Starting Soon
01/02/2026
Master AI fundamentals with Python, Machine Learning, and hands-on projects
Contact
+91 9513805401
training@craw.in
Languages
Hindi & English
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What Will You Learn in AI and Machine Learning?
Through the top-notch training faculty at Craw Security, students who have a solid grasp of how to accomplish something amazing with statistics, insights, model development, and analysis can pursue their bright future in this rapidly expanding field of Data Science Diploma with AI. At Craw Security, learning aspirants will genuinely have the best learning environment, allowing them to study in the most carefully selected training setting.
Master these essential disciplines in your 6-Month Diploma:
Artificial Intelligence
Machine Learning
Python Programming for ML & AI
Course Curriculum
Explore our comprehensive curriculum covering Python, Machine Learning, and Artificial Intelligence with hands-on projects and real-world applications.
Python is the foundation upon which modern data science is built. In order to provide a more straightforward comprehension of the intricacies involved in data analysis, this course is intended to provide students with a solid foundation in Python programming. You are going to acquire:
1. Introduction
- Programming language introduction
- Translators (Compiler, Interpreter)
- Uses of computer programs
- Algorithm
- Flow chart
2. Python Introduction
- History
- Why python created
- Fields of use
- Reasons for using Python
- Syntax
- Installation of IDE
3. Variables
- What is variable
- Declaration rules
- Multiple variable declarations
- Valid and invalid variables
- Type casting
4. Data Type
- Introduction
- Discuss all data types
- Use type() to show dynamically typed language
- String
- List
- List: List Comprehension
- Tuple
- Dictionary
- Set
5. Operators
- Introduction
- Arithmetic operators
- Assignment operators
- Comparison operators
- Logical operators
- Identity operator
- Bitwise operator
- Membership operator
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
7. Function
- Introduction function
- Declaration, calling of function
- Lambda function
- Filter
- Reduce function
- Map function
8. File Handling
- Introduction
- Text file handling
- Binary file handling
9. Object Oriented Programming
- Introduction
- Difference b/w procedural programming and OOPS
- Class
- Object
- Encapsulation
- Inheritance
- Abstraction
- Polymorphism
10. Web Scrapping
- Introduction
- Introduce basic HTML tags
- Introduction to Requests Library
- Introduction to bs4
- Scrapping through Beautiful Soup
11. Numpy
- Creating NumPy arrays
- Properties of Array
- Indexing and Slicing
- Aggregate Functions
- Numpy Functions
- Vectorization
- Broadcasting
- Boolean indexing
12. Pandas
- Series
- Data Frame
- Data Frame Properties
- Data Frame indexing and slicing
- Reading data from various sources
- Dataframe Functions
- Pandas Functions
- Filter Data
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 is the driving force behind artificial intelligence, and it is causing a shift in the way that businesses analyze and respond to data. This lesson will walk you through the fundamentals of machine learning as well as the algorithms that underpin it, including the following:
1. SQL BASICS
- Introduction to MySQL
- Databases, tables, and rows
- Data types and expressions
2. DDL & DML
- Table creation, modification, and deletion
- Constraints
- Data manipulation: INSERT, UPDATE, DELETE
3. DQL (QUERYING DATA)
- SELECT statement
- WHERE conditions
- Operators: arithmetic, comparison, logical, range, list, LIKE
- Result filtering & sorting: ORDER BY, DISTINCT, TOP
- NULL handling: IS NULL, IS NOT NULL
- Conditional logic: CASE statement
4. SQL FUNCTIONS
- String functions
- Math functions
- Date functions
- Comparison functions
- Aggregate functions
- GROUP BY and HAVING clauses
5. ADVANCED SQL
- Joins
- Subqueries
- Views
Machine learning is the driving force behind artificial intelligence, and it is causing a shift in the way that businesses analyze and respond to data. This lesson will walk you through the fundamentals of machine learning as well as the algorithms that underpin it, including the following:
1. Welcome to the ML experience
- Importance of ML in your career
- AI FAMILY TREE
- System requirements
- Prerequisites
2. 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
3. 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
4. Regularization
- Overfitting vs Underfitting
- Bias Variance trade-off
- Ridge and Lasso Regularization
- Cross Validation
5. Introduction to Classification
- Introduction to Logistic Regression
- Model Evaluation: Accuracy, Precision & Recall
- Model Evaluation: F1 Score, Confusion Matrix
- SVM
- Decision Tree
6. Ensemble Learning
- What is Ensemble Learning
- Bagging
- Random Forest
- Introduction to Boosting
- Boosting: Adaboost
- Boosting: Gradient Boost
- Boosting: XG Boost
7. Introduction to Hyperparameter Tuning
- Hyperparameter Tuning: GridsearchCV
- Hyperparameter Tuning: RandomizedSearchCV
- Model Selection Guide
- Selecting the Right Evaluation
8. Unsupervised ML
- Introduction to Clustering
- K-Means Clustering
- Principal Component Analysis
Artificial intelligence (AI) is causing a revolution in a variety of sectors all over the world, including the healthcare and financial sectors. This course will provide you with an introduction to the field of artificial intelligence and the various applications of this field. The following subjects are discussed:
1. Artificial Neural Network and Regularization
- Single layered ANN
- Multiple Layered ANN
- Vanishing Gradient problem
- Dropout
2. Introduction to Deep Learning
- Difference between ML, DL, and AI
- Activation functions
- Gradient Descent
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
4. Image Classification
- Haar Cascade Classifier
- Image Classification with CNN
5. Object Detection
- What is Object Detection
- Object Detection using Haar Cascade
6. Introduction to NLP
- What is Natural Language Processing
- Uses of NLP
- Application of NLP
- Components of NLP
- Stages of NLP
- Chatbot
7. Text Preprocessing
- Tokenization
- Non-Alphabets Removal
- Bag of Words
- Stemming & Lemmatization
8. Sentiment Analysis
- What is Sentiment Analysis
- Challenges in Sentiment Analysis
- Handling Emotions
- Sentiment Analysis with ANN
9. Sequence Model
- Sequential Data
- Recurrent Neural Network
- Architecture of RNN
- Vanishing Gradient Problem in RNN
- Long Short-Term Memory
Market Share of AI + ML
The size of the global market for data science platforms was estimated at USD ~$391.7 billion in 2025. Over the course of the forecast period, it is expected to increase at a compound annual growth rate (CAGR) of 36–38%, from USD 133.12 billion in 2025 to USD $1,836.8 billion by 2032.
A software program that provides a platform for a data science project's whole life cycle is called a data science platform. These platforms enable model building, distribution, and investigation, making them indispensable tools for data scientists. Furthermore, it provides a large-scale computing infrastructure and facilitates data preparation and visualization. These systems offer a centralized platform that facilitates user collaboration.
Why Choose Craw Security to Learn 6-Months Diploma in Artificial Intelligence (AI) and Machine Learning?
Selecting Craw Security for thorough training in Data Science with AI from highly sought-after experts with years of excellent experience can be incredibly advantageous for both substantial career advancement and respectable personal development. When selecting Craw Security as your chosen partner in this domain, you should take into account the following ideal factors:
Full Flexibility in choosing the learning mode, such as:
- VILT (Virtual Instructor-Led Training)
- Pre-recorded Video Sessions
- Offline Classroom Sessions
You can choose to opt for a demo session in order to make up your mind whether to start or not the primetime 6-Month Diploma in Artificial Intelligence (AI) and Machine Learning under the career-promising guidance of highly qualified training mentors with many years of quality work experience. To book a demo slot for the same course, you can give us a call at our hotline mobile number, +91-9513805401, and have a word with our superb group of study consultants.
Benefits of Learning AI & ML
These days, data is often referred to as the "new oil,"since it fuels innovation and technological growth. With organizations becoming more data-driven, AI and ML professionals are in extremely high demand.
Craw Security, India's leading AI Training Institute, offers a 6-Month Diploma in AI & ML focusing on real-world projects to build practical and industry-ready skills.
Below are the top benefits of learning AI & ML:
High Demand and Lucrative Career Opportunities
Diverse Career Paths and Flexibility
Solving Real-World Problems
Enhanced Problem-Solving and Analytical Thinking
Empowerment through Data Literacy
Opportunities for Innovation and Creativity
Mastering Cutting-Edge Tools and Technologies
Continuous Learning and Adaptation
Impactful Career with Global Reach
Job Scope of Data Scientists: Exploring a Promising Career Path
In this technology-driven society, the role of Data Scientist has become one of the most desirable job options available to individuals. The demand for talented data scientists is expected to continue to increase as a result of the growing reliance that businesses have on data in order to make choices, innovate, and maintain their competitive edge. The breadth of this profession encompasses a wide range of industries, which makes it a potentially lucrative and varied field of work.
An overview of the employment scope of data scientists is provided here, along with a description of the main industries with high demand for data scientists and career opportunities.
| Industry | Description |
|---|---|
| Technology | Data science is an essential component for the development of user insights, the enhancement of products, and the acceleration of innovation in technology companies. Businesses like Google, Amazon, and Facebook depend on data scientists to optimize algorithms, personalize content, and enhance customer experiences. |
| Finance | Data scientists support the financial industry by helping banks and other financial organizations forecast market trends, assess risks, and spot fraudulent activities. They are building models for risk management, algorithmic trading, and credit scoring, among other things. |
| Healthcare | By enabling predictive analytics for disease prevention, improving patient outcomes, and personalizing medications through the use of insights obtained from patient data, data science is revolutionizing the healthcare sector. |
| Retail and E-commerce | In order to improve pricing, inventory control, and marketing strategies, data scientists are used in the retail sector to collect data on consumer behavior. Recommendation systems, like those used by Amazon and Netflix, are developed using data in order to enhance the entire customer experience. |
| Manufacturing | Improving production lines, predicting equipment failures through predictive maintenance, and lowering operational costs through supply chain data analysis are the key concerns of data scientists in the manufacturing sector. |
| 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 for public health, education, and urban planning. |
Technology
Data science is an essential component for the development of user insights, the enhancement of products, and the acceleration of innovation in technology companies. Businesses like Google, Amazon, and Facebook depend on data scientists to optimize algorithms, personalize content, and enhance customer experiences.
Finance
Data scientists support the financial industry by helping banks and other financial organizations forecast market trends, assess risks, and spot fraudulent activities. They are building models for risk management, algorithmic trading, and credit scoring, among other things.
Healthcare
By enabling predictive analytics for disease prevention, improving patient outcomes, and personalizing medications through the use of insights obtained from patient data, data science is revolutionizing the healthcare sector.
Career Prospects and Growth Opportunities
A data scientist should anticipate a dynamic career path with a range of responsibilities and specialty choices. Some of the most common job titles in this sector are as follows:
| Position | Description |
|---|---|
| Junior Data Scientist | The main duties of entry-level positions include gathering data, cleaning it, and helping with basic data analysis. |
| Data Analyst | The main focus of data analysts, who often act as intermediates, is the interpretation and evaluation of data to provide business insights. |
| Senior Data Scientist | Data scientists can take on more challenging tasks, lead projects, and create increasingly complex machine learning models as their experience improves. |
| Machine Learning Engineer | Data scientists go into careers requiring them to build scalable machine learning models for use in business applications after getting experience in the area. |
| Data Science Manager | As data scientists advance in their careers, they have the opportunity to advance into leadership positions, where they are responsible for managing teams of data professionals and driving data strategy. |
| Chief Data Officer (CDO) | As a senior executive, this person is responsible for overseeing the company s overall data management strategy and making sure that the organization s data assets are ideal for achieving business goals. |
Junior Data Scientist
The main duties of entry-level positions include gathering data, cleaning it, and helping with basic data analysis.
Data Analyst
The main focus of data analysts, who often act as intermediates, is the interpretation and evaluation of data to provide business insights.
Senior Data Scientist
Data scientists can take on more challenging tasks, lead projects, and create increasingly complex machine learning models as their experience improves.
Skills Required for Data Scientists
To succeed in this field, a data scientist must possess both technical and non-technical skills. Among these skills are:
Programming Skills (Python, R)
Statistical Analysis
Machine Learning
Data Visualization
Big Data Tools
Communication Skills
Who Should Do 6 Months Diploma in Learning Artificial Intelligence (AI) and Machine Learning?
Here's a look at who would benefit the most from enrolling in this diploma course:
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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Frequently Asked Questions
About 6-Months Diploma in AI and Machine Learning
AI, or artificial intelligence, is the process of imitating human intellect in machines by programming them to think, learn, and make decisions in the same way that humans do. Natural language processing, computer vision, robotics, and expert systems are just some of the subfields that fall under the umbrella of artificial intelligence.
A subfield of artificial intelligence, machine learning (ML) focuses on the development of systems that can learn and improve from data without being explicitly programmed. For the purpose of recognizing patterns and making predictions, it requires the utilization of algorithms.
Artificial intelligence (AI) refers to the overarching notion of developing intelligent systems, whereas machine learning (ML) is a subset of AI that enables computers to acquire knowledge from data. To put it another way, machine learning is a technique that is utilized to accomplish artificial intelligence.
Three primary categories of ML are as follows:
Supervised Learning: We train models using data that has been labeled.
Unsupervised Learning: Unlabeled data can be analyzed by models to discover trends.
Reinforcement Learning: Models acquire knowledge through the process of trial and error when they are rewarded or punished.
The prime skills that are needed to learn AI and ML fundamentals are as follows:
Competence in programming languages such as Python, R, Java, and others
The ability to comprehend mathematical concepts such as linear algebra, probability, and calculus
Data structures and algorithmic knowledge are both required.
Knowledge of several machine learning tools, such as TensorFlow, PyTorch, or scikit-learn.
AI and ML are widely used in:
Autonomous vehicles,
Chatbots and virtual assistants,
Fraud detection,
Personalized recommendations,
Medical diagnosis,
Predictive analytics,
Robotics, etc.
The mainstream benefits of AI and ML technologies are as follows:
Automation of repetitive tasks,
Enhanced decision-making through data insights,
Improved efficiency and productivity,
Personalization of user experiences,
Ability to process and analyze massive amounts of data, etc.
AI and ML are transforming industries such as:
Healthcare,
Finance,
Retail,
Manufacturing,
Education,
Entertainment,
Agriculture, etc.
Costs of early investment that are high,
Insufficient number of experts with the necessary skills,
Concerns of an ethical nature surrounding the privacy of data and bias,
There is a challenge in comprehending complicated models, etc.
Some of the popular tools duly utilized in AI and ML technologies are jotted down:
Languages used for programming: Python and R
Examples of frameworks and libraries include TensorFlow, PyTorch, and Scikit-learn.
AWS, Google Cloud, and Microsoft Azure are examples of cloud platforms.
Tools for the representation of data include Tableau and Power BI.
Although having a fundamental understanding of linear algebra, calculus, and statistics is beneficial, there are numerous resources that simplify these ideas for those who are just starting out. Practical applications frequently rely on libraries and frameworks that have already been constructed.
AI has the potential to automate certain jobs, which could result in employment displacement in certain fields. Nevertheless, it also provides new roles in the development of artificial intelligence, data analysis, and the management of technology.
How long it takes to learn something is determined by your existing knowledge and your ambitions. As opposed to advanced expertise, which may require years of study and practice, basic abilities can be acquired by beginners in as little as six to twelve months.
Artificial intelligence brings ethical problems around accountability, privacy, and bias. It is essential to design artificial intelligence systems that are open to scrutiny, impartial, and in line with the values of society.
Some popular 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 programming (Python is a good start),
Studying ML basics through online courses (e.g., Coursera, edX),
Practicing with projects and datasets,
Exploring AI frameworks like TensorFlow and PyTorch,
There will be developments in autonomous systems, natural language processing, edge computing, and the incorporation of artificial intelligence into daily technology in the future of artificial intelligence and machine learning.
In fact, the hazards include:
Abuse of artificial intelligence for potentially harmful objectives,
Ethical conundrums that arise during the decision-making process,
The loss of jobs and the disparity in economic conditions, etc.