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

In the current data-driven environment, organizations depend heavily on data to inform decisions, identify innovative solutions to challenges, and foster innovation. Consequently, there is an increasing need for data science professionals. Bytecode Security offers a comprehensive six-month diploma in data science that is designed to equip you with the necessary skills and knowledge to thrive in this rapidly evolving field. This diploma encompasses three primary subjects: Python, artificial intelligence, and machine learning.

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

Through the world-class training faculties at Bytecode Security, learners who possess a strong comprehension of the application of numbers, insights, model building, and analysis can pursue a promising future in the rapidly expanding field of Data Science Diploma with AI. At Bytecode Security, a learning aspirant will genuinely experience the most optimal learning environment, enabling them to study in the most meticulously curated training environment.

The following are the subjects that will be covered in the 6-Months Diploma in Data Science, which is a remarkable discipline of study:

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

Let us examine the content of this course and the ways in which it will help you develop into a proficient data science professional.

Enquire Now




Python Programming for Data Science

Modern data science is constructed upon the foundation of Python. This course is designed to equip students with a strong foundation in Python programming, thereby facilitating a more direct understanding of the complexities associated with data analysis. You will acquire:

  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
      1. Why python created
      2. Fields of use
      3. Use of Python in Cybersecurity
      4. Reasons for using Python
      5. Syntax
      6. Installation of IDE
    2. Variables
      1. What is variable
      2. Declaration rules
      3. Multiple variable declarations
      4. Valid and invalid variables
      5. Type casting
    3. 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
    4. Operators
      1. Introduction
      2. Arithmetic operators
      3. Assignment operators
      4. Comparison operators
      5. Logical operators
      6. Identity operator
      7. Bitwise operator
      8. Membership operator
    5. 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
    6. Function
      1. Introduction function
      2. Declaration, calling of function
      3. Lambda function
      4. Filter
      5. Reduce function
      6. Map function
    7. File Handling
      1. Introduction
      2. Text file handling
      3. Binary file handling
    8. 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
    9. Web Scrapping
      1. Introduction
      2. Introduce basic HTML tags
      3. Introduction to Requests Library
      4. Introduction to bs4
      5. Scrapping through Beautiful Soup
    10. 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
    11. 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
    12. 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

The manner in which businesses analyze and respond to data is being transformed by machine learning, which is the primary force behind artificial intelligence. This lecture will guide you through the fundamentals of machine learning and the algorithms that support it, which include the following:

  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

Artificial intelligence (AI) is causing a revolution in a variety of sectors worldwide, including the financial and healthcare sectors. This course will introduce you to the field of artificial intelligence and the diverse applications of this discipline. The subsequent topics are addressed:

  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

Market Share of Data Science

In 2023, the global market for data science platforms was estimated to be worth USD 103.93 billion. USD 133.12 billion in 2024 is anticipated to increase at a compound annual growth rate (CAGR) of 24.7% over the duration of the forecast period, reaching USD 776.86 billion by 2032.

A data science platform is a software program that serves as a foundation for the entire life cycle of a data science endeavor. These platforms are essential instruments for data scientists, as they facilitate the development, dissemination, and investigation of models. In addition, it facilitates data preparation and visualization and offers a large-scale computing infrastructure. These systems provide a centralized platform that enables user collaboration.

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

Selecting Bytecode Security for comprehensive training in Data Science with AI from highly desired professionals with many years of quality experience can be very beneficial for significant career growth and reasonable growth in life. Before selecting Bytecode Security as your preferred companion in this field, you may want to take into account the following best factors:

  • Full Flexibility in choosing the learning mode, such as:
    • VILT (Virtual Instructor-Led Training) Sessions
    • Pre-recorded Video Sessions, and
    • Offline Classroom Sessions.
  • World-Class Experienced Training Faculties.
  • Study Materials are in both soft and hard copies.
  • Verified study materials from data scientists working in diverse organizations worldwide.
  • Certificate of Completion after finishing the course, followed by an internal exam(s).

Job Scope of Data Scientists: Exploring a Promising Career Path

The position of Data Scientist has emerged as one of the most desirable career paths in this technology-driven society. The demand for skilled data scientists is anticipated to continue to rise as a consequence of the increasing dependence of businesses on data to make decisions, innovate, and preserve their competitive advantage. This profession is potentially lucrative and diverse due to its broad scope, which includes a diverse array of industries.

A synopsis of the job scope of data scientists is provided below, which includes an outline of the primary industries with high demand for data scientists and career opportunities.

Technology

In technology companies, data science is a critical element in the development of user insights, the enhancement of products, and the acceleration of innovation. Companies like Google, Amazon, and Facebook depend on data scientists to enhance consumer experiences, personalize content, and optimize algorithms.

Finance

Data scientists contribute to the financial sector by assisting banks and other financial institutions in predicting market trends, evaluating risks, and identifying fraudulent activity. They are currently in the process of developing models for algorithmic trading, credit assessment, and risk management.

Healthcare

By utilizing insights derived from patient data, the healthcare industry is experiencing a revolution in the field of data science, which is facilitating predictive analytics for the prevention of disease, improving patient outcomes, and personalizing therapies.

Retail and E-commerce

In order to enhance pricing, inventory administration, and marketing strategies, data scientists are employed in the retail sector. Similar to the systems employed by Amazon and Netflix, data is employed to create recommendation systems that are intended to enhance the overall consumer experience.

Manufacturing

Data scientists are primarily focused on the improvement of production lines, the prediction of equipment breakdowns through predictive maintenance, and the reduction of operational costs through the analysis of supply chain data in the manufacturing industry.

Government and Public Policy

Governments employ data science to analyze data from the public sector, improve services, and advance smart city initiatives. It aids in the formulation of decisions that are supported by facts in the areas of urban planning, public health, and education.

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:

  • Programming Skills,
  • Statistical Analysis,
  • Machine Learning,
  • Data Visualization,
  • Big Data Tools,
  • Communication Skills, etc.

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:

Junior Data Scientist

Entry-level positions are primarily responsible for data collection, cleansing, and providing support for fundamental data analysis.

Data Analyst

The primary focus of data analysts is the interpretation and evaluation of data to provide business insights, and they often serve as intermediaries.

Senior Data Scientist

Data scientists are capable of assuming greater responsibility for projects, designing more complex machine learning models, and tackling increasingly challenging tasks as their experience expands.

Machine Learning Engineer

Once they have acquired experience in the field of machine learning, data scientists transition into professions that necessitate the development of scalable machine learning models for business applications.

Data Science Manager

As data scientists progress in their careers, they have the opportunity to assume leadership roles, which involve the management of teams of data professionals and the development of data strategy.

Chief Data Officer (CDO)

This individual is responsible for overseeing the data management strategy of the entire organization and ensuring that the organization's data assets are optimized to attain business objectives in a senior executive role.

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:

  • 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.

Course Duration

  • Course Duration: 720 Hours
  • Course Level: Intermediate
  • Include: Training Certificate
  • Language: English, Hindi
  • Course Delivery: Classroom Training
  • Course pdf: Click here to Download

Our Google Reviews