Course Overview

This comprehensive 4-month course provides a deep dive into Artificial Intelligence, Machine Learning, and Data Science, equipping students with the knowledge and tools needed to become proficient in these fields. You’ll master Python programming, work with key libraries such as Pandas, NumPy, Matplotlib, and Seaborn, and get hands-on experience with cutting-edge technologies like TensorFlow, Keras, and PySpark.

Throughout the course, you will gain practical knowledge and skills in data analysis, machine learning, deep learning, natural language processing (NLP), and big data technologies. By the end of the course, you will have the ability to build real-world AI applications and be ready for a career in data science or machine learning.

Who is it for

This course is ideal for:

1. Aspiring Data Scientists & Machine Learning Engineers: If you’re looking to launch a career in data science, machine learning, or artificial intelligence, this course will provide you with the foundational knowledge and practical skills required to work with real-world data, build machine learning models, and implement AI solutions.
2. Beginner Programmers & Developers: If you have a basic understanding of programming (especially Python), but you’re new to data science or machine learning, this course will take you from beginner to advanced levels, enabling you to work confidently with key libraries like Pandas, NumPy, and TensorFlow.
3. Students in Computer Science or Engineering: If you’re a student in computer science, engineering, or any related field looking to gain hands-on experience with AI, data science, and machine learning techniques, this course is tailored to complement your academic learning with real-world projects and skills.
4. Professionals Looking to Transition to Data Science: If you are a software developer, business analyst, or work in any other technical field and want to pivot to data science or AI, this course will equip you with the necessary tools, frameworks, and methodologies to start a successful career in this high-demand field.
5. Business Analysts & Data Analysts: If you’re already working in data analytics and want to transition into advanced data science or machine learning, this course will broaden your skillset, introducing you to the tools and techniques necessary to move from basic analytics to predictive modeling and AI.
6. Entrepreneurs and Startups: If you are working on launching a tech startup or building AI-driven products, this course will help you understand how to leverage AI and machine learning techniques to build smart applications, products, and services that can disrupt industries.
7. Researchers and Academics: If you’re in academia or research and want to explore AI, machine learning, or data science as part of your projects, this course provides the foundational knowledge and advanced techniques to take your research to the next level.
8. Anyone Interested in AI and Data Science: If you’re simply curious about artificial intelligence and data science, and you want to learn more about how data and algorithms can be used to solve real-world problems, this course will introduce you to cutting-edge technologies like generative AI, deep learning, and big data.

Prerequisites

1. Basic knowledge of Python programming.
2. Understanding of mathematics and statistics (basic algebra and probability).
3. Familiarity with basic programming concepts (loops, functions, conditionals).
4. Interest in learning data science and AI concepts.

Software Used

1. Python (Programming Language)
2. Jupyter Notebooks / Google Colab (for coding and project work)
3. Anaconda (Python distribution for data science)
4. Pandas (Data manipulation)
5. NumPy (Numerical computing)
6. Matplotlib & Seaborn (Data visualization)
7. Scikit-Learn (Machine Learning library)
8. TensorFlow & Keras (Deep Learning frameworks)
9. PySpark (Big Data processing)
10. NLTK (Natural Language Processing)
11. Hadoop (Big Data framework)
12. Tableau (Data visualization)

Git (Version control)