Data Science, AI & ML

Data Science, AI & ML

Data science uses math and computer science to extract insights from data. AI is about creating machines that can do tasks that require human intelligence. ML is a subset of AI that involves teaching machines to learn from data. Together, these fields are transforming businesses by automating tasks, gaining insights, and making better decisions.
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Data Science, AI & ML Course Roadmap

AI and ML

Applied AI

Just the ML you need

  • Course Duration : 14 days
  • Schedule : 2 Hours per day

About

AI and its related technologies have entered the mainstream of technological innovation. Thanks to the increased access to data, the increasing computational power and sophisticated sensors and algorithms. These technologies include machine learning, natural language processing, robotics and image processing.

This course is an introduction to AI from an applied perspective and has 30 hours of complete industry focused cutting-edge content covering Data Science, AI, Python for ML, Data Analytics and Machine Learning. Live sessions covering topics designed based current industry requirements to prepare students better for real-world problem-solving.

Fundaments of scalable Data Science – Foundational Course

  • Online : 2
  • In-Person : 2
  • Quiz : 0.15
  • The History and Evolution of Data.
  • How Data and Science are connected.
  • The Life Cycle of Data Science.
  • Data Analytics, Data Science & AI – The link..
  • Types of Data Analytics by Relevance.
  • Introduction to Big Data and the 4 V’s.

Introduction to Artificial Intelligence

  • Online : 2
  • In-Person : 2
  • Quiz : 0.15
  • Terminologies and differences between them – Artificial Intelligence, Machine Learning, Deep Learning.
  • History of AI (evolution of AI and its pace).
  • Proposing and evaluating real world AI applications
  • AI in the enterprise and AI in the future of work.

Fundamentals of Programming – Python for ML

  • Online : 8
  • In-Person : 8
  • Lab: 4
  • Introduction to python
  • Working with Data.
  • The Life Cycle of Data Science.
  • Data Types & Variables
  • Making Decisions.
    • Logical Expression
    • Conditional statements
    • Logical operators
    • More complex expressions
  • Finding and Fixing problems
    • Types of Errors
    • Troubleshooting Tools
    • Using the Python Debugger
  • Lists and Loops
    • Lists and Tuples
    • List Functions
    • “For” & “While” Loops
  • Numeric and Date Functions
    • Dates and Times
    • Advanced Data Time Management
    • Random Numbers
    • The Math Library
  • Functions
    • Writing and
    • Calling Functions
    • Function Inputs and Outputs
    • Local and Global Scope

Machine Learning Basics

  • Online : 4
  • In-Person : 4
  • Lab: 2
  • Types of Machine Learning – Supervised, Unsupervised and Reinforcement
  • Basic ML process
  • Classification and Regression Models.
  • Feature Engineering and deployment of ML Models.

Foundations of ML (Algorithms)

  • Online : 3
  • In-Person : 3
  • Lab: 3
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • K-Nearest Neighbours
  • Decision Tree
  • Random Forest

ML Libraries – NumPy, Pandas, Matplotlib, Seaborn and Scikit-learn

  • Online : 2
  • In-Person : 2
  • Pracrical/Quiz : 2
  • NumPy (key operations)
  • Pandas (Series, DataFrame, key operations)
  • Matplotlib (basic plotting)
  • Seaborn (key plots)
  • Scikit-learn (key algorithms and operations)
Case Study 1 (end-to-end process) (2 hours)
  • ML Real World Case Studies.
  • Regression problem
Case Study 2 (end-to-end process) (2 hours)
  • Classification problem

Key Notes:

  1.  The entire course will be taught from an Industry stand point. Every session will have many practical examples and exercises that would help prepare our students better for real-world problem solving and interviews.
  2. The validity of the Applied Machine Learning course is 90 days from the date of Enrollment.
  3. Every live session will have many facts and figures to enable the latest happening in the industry.
  4. More than 5 real world case studies (includes 2 Self Case studies).
  5. 12+ machine learning algorithms will be taught in this course.
  6. After every learning milestones, there will be assignments and exercises to measure learners understanding of the subject and concepts
  7. We will also be conducting 10+ hours of live content based on industry trends along with the research support.
  8. Mentorship will be assigned to each candidate after the completion of 50% of the course assignments whose sole concentration would be on building the specific learner’s portfolio/resume and in interview preparation, mock interviews