Introduction to the Course
Our AI and Applied Data Science with Python – Level 4 training course is designed to provide learners with the skills and knowledge necessary to build, deploy, and scale real-world AI solutions using Python. This course is suitable for anyone interested in AI and Data Science, as it starts from basic Python programming and progresses step by step to advanced Machine Learning, Deep Learning, and Generative AI concepts. Learners can choose from three specialisation options and also opt for an internship involving four live projects mapped to their chosen specialisation stream.
What is the Course About?
This course is designed to provide you with a comprehensive and end-to-end understanding of Artificial Intelligence (AI) and Applied Data Science using Python. The course covers a wide range of topics, starting from Python fundamentals, data analysis, statistics, and machine learning, and advancing to deep learning, natural language processing, computer vision, generative AI, large language models (LLMs), and AI system deployment.
You will learn how to use Python to manipulate and analyze data, understand the mathematical foundations behind machine learning algorithms, build predictive and generative models, and deploy machine learning solutions through web APIs and modern AI frameworks.
What Will You Learn?
You will learn how to use Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Plotly, and Scikit-Learn to perform data analysis, visualization, and build predictive models. You will also learn how to apply a wide range of machine learning algorithms, including K-Nearest Neighbors, Linear Regression (Least Squares, Ridge, Lasso, Polynomial Regression), Logistic Regression, Support Vector Machines, Decision Trees, Naive Bayes, Random Forests, and Gradient Boosted Models to solve real-world problems.
In addition, you will learn Deep Learning using TensorFlow/Keras and PyTorch, working with architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Autoencoders, and Transformers. You will also gain hands-on experience in Natural Language Processing (NLP), Computer Vision, Generative AI, Large Language Models (LLMs), LangChain, Vector Databases, and Retrieval-Augmented Generation (RAG).
You will learn how to evaluate the performance of your models, optimize them for better accuracy, and deploy machine learning models using Flask, FastAPI, Docker, and cloud platforms.
Skills You Will Gain
After completing this course, you will have gained the following skills:
- Proficiency in Python programming from basic to advanced levels
- Data analysis, manipulation, and visualization using Python libraries
- Strong understanding of statistics, linear algebra, and machine learning fundamentals
- Ability to build, evaluate, and optimize machine learning models
- Hands-on experience with Deep Learning, NLP, Computer Vision, and Generative AI
- Ability to deploy machine learning models and AI systems through web APIs
- Practical experience through live, industry-oriented projects
Overall, this course will provide you with a strong, industry-ready foundation in AI and Applied Data Science with Python, giving you the necessary skills to tackle real-world problems using data-driven and AI-powered solutions.
Why Should I Enroll in This Course?
Undertaking a training course in AI and Applied Data Science with Python – Level 4 can provide numerous benefits. Here are some of them:
High demand for AI and Data Science skills:
AI and Data Science are among the fastest-growing domains in today’s job market. Organizations across industries are actively looking for professionals who can build, deploy, and manage AI systems. This course prepares you with skills that are highly valued in the industry.
Learn from basics to advanced AI:
The course is structured to start from fundamental Python programming, making it suitable for beginners, and gradually advance to complex AI, Machine Learning, Deep Learning, and Generative AI concepts.
Hands-on experience with Python and modern AI tools:
You will gain hands-on experience with Python and its ecosystem, including NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, PyTorch, Hugging Face Transformers, LangChain, Flask, FastAPI, Docker, and cloud platforms.
Learn essential and advanced Data Science concepts:
The course provides a comprehensive understanding of data cleaning, data analysis, data visualization, machine learning, deep learning, NLP, computer vision, and generative AI. You will also learn how to evaluate model performance, optimize parameters, and deploy AI solutions.
Enhance problem-solving skills:
AI and Data Science require strong analytical and problem-solving abilities. This course helps you learn how to identify problems, formulate hypotheses, design experiments, analyze data, interpret results, and build effective AI solutions.
Flexibility and specialisation options:
The course is available in weekday and weekend formats, allowing you to learn at your own pace. You can choose from three specialisation options and also opt for an internship with four live projects, mapped to your chosen specialisation stream, to gain real-world experience.
Career Pathways, Average Salary, and Hiring Companies
After undertaking a course in AI and Applied Data Science with Python – Level 4, there are several career pathways you can pursue, including:
Data Scientist:
A Data Scientist is responsible for collecting, analyzing, and interpreting large and complex datasets to extract insights and support data-driven decision-making. The average salary for a Data Scientist in India ranges from ₹3.8 LPA to ₹26 LPA, with an average around ₹10 LPA. Major hiring companies include Accenture, Deloitte, Fractal Analytics, Genpact, IBM, Infosys, KPMG, TCS, and Wipro.
Machine Learning Engineer:
A Machine Learning Engineer designs, develops, and deploys machine learning models and systems. The average salary for a Machine Learning Engineer in India is around ₹9,00,000 per annum. Major hiring companies include Amazon, Google, IBM, Microsoft, Nvidia, and Qualcomm.
AI Engineer:
An AI Engineer builds and deploys intelligent systems using machine learning, deep learning, and generative AI techniques. The average salary for an AI Engineer in India is around ₹9,00,000 per annum. Major hiring companies include Accenture, Deloitte, Genpact, IBM, Infosys, TCS, and Wipro.
Big Data Analyst:
A Big Data Analyst analyzes large and complex datasets to identify trends and insights. The average salary for a Big Data Analyst in India is around ₹6,00,000 per annum. Major hiring companies include Accenture, Capgemini, Cognizant, Deloitte, IBM, Infosys, TCS, and Wipro.
Business Intelligence Analyst:
A Business Intelligence Analyst analyzes business data to create reports and visualizations for decision-making. The average salary for a Business Intelligence Analyst in India is around ₹5,00,000 per annum. Major hiring companies include Accenture, Capgemini, Cognizant, Deloitte, IBM, Infosys, TCS, and Wipro.
Curriculum
- 23 Sections
- 109 Lessons
- Lifetime
- Fundamentals of Python8
- Additional Python Topics7
- Linux and Containerisation2
- Python Programming for Mathematical and Scientific Computation4
- Introduction to Linear Algebra5
- 5.1Basics: Scalars, vectors, matrices, notations
- 5.2Vector Operations: Addition, subtraction, scalar multiplication, dot & cross product, norms
- 5.3Matrix Operations: Addition, multiplication, transpose, inverse
- 5.4Special Matrices: Identity, diagonal, symmetric, orthogonal, determinants
- 5.5Solving Linear Equations: Row echelon forms, Gaussian elimination
- Introduction to Statistics5
- 6.1Descriptive Statistics: Types of data, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, kurtosis
- 6.2Probability Theory: Basic probability, conditional probability, Bayes’ theorem, discrete & continuous distributions
- 6.3Inferential Statistics: Population vs sample, sampling methods, central limit theorem, hypothesis testing (null/alternative, p-value, type I/II errors)
- 6.4Statistical Tests & Confidence Intervals: t-tests (one-sample, two-sample), chi-square test, ANOVA, confidence intervals for means & proportions
- 6.5Correlation & Regression: Correlation coefficient, simple & multiple linear regression, interpretation of results
- Exploratory Data Analysis1
- Introduction to Machine Learning5
- In depth mathematical explanation of the algorithms and its implementation in python from scratch2
- Building models with popular Supervised Learning algorithms7
- 10.1K-Nearest Neighbors: Classification and Regression
- 10.2Linear Regression: Least-Squares, Ridge
- 10.3Lasso, and Polynomial Regression
- 10.4Logistic Regression, Support Vector Machines, Decision Trees
- 10.5Neural Networks, Naive Bayes
- 10.6Ensemble Modelling, Random Forest, Gradient Boosted Decision Trees
- 10.7Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn
- Model Evaluation6
- Building models with popular Unsupervised Learning4
- Serving Machine Learning Models through web API2
- Introduction to Deep Learning5
- 14.1Programming with Tensorflow/Keras and Pytorch
- 14.2Deep Learning Architectures: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, Autoencoders
- 14.3Transfer Learning and Fine Tuning
- 14.4Generative AI algorithms: Generative Adversarial Networks (GANs), Transformers, Diffusion models,Large Language Models (LLMs)
- 14.5Tools Covered: Python, TensorFlow/Keras
- Natural Language Processing (NLP) and Applied Text Mining3
- Computer Vision4
- Generative AI and Large Language Models (LLMs)12
- 17.1Introduction to Generative AI and Popular LLMs: Definition, history, key characteristics, and applications; popular LLMs (GPT, BERT, T5, LLaMA, Phi)
- 17.2Fundamentals of LLMs: Transformer architecture and attention mechanism; pre-training and fine-tuning; ethics in Generative AI
- 17.3Working with LLMs: Hugging Face setup and usage; NLP tasks (text generation, classification, summarization); evaluation metrics (Perplexity, BLEU, Precision, Recall, F1 Score)
- 17.4LangChain & Application Development: Core components (Chains, Agents, Memory, Prompts); chatbot development and integration with LLMs
- 17.5Vector Databases & Retrieval-Augmented Generation (RAG): Vector databases for storing and searching embeddings; RAG for combining retrieved knowledge with LLM generation to produce accurate, context-aware responses
- 17.6Transfer Learning & Fine-Tuning: Transfer learning concepts; fine-tuning LLMs on domain-specific data; LoRA (Low-Rank Adaptation) for efficient fine-tuning
- 17.7Agentic AI & Model Context Protocol(MCP): Understanding agentic models; multi-step reasoning and planning
- 17.8Advanced Techniques: RLHF (Reinforcement Learning from Human Feedback); Chain-of-Thought (CoT) prompting; agentic models and decision-making
- 17.9Open-Source Models & Ollama: Setup and usage of Ollama; comparison with commercial LLMs; demo
- 17.10Chatbot Development & Best Practices: APIs, context management, personalization; evaluating chatbots using BLEU, Perplexity, F1 Score
- 17.11Future Directions: Multi-modal models (text + image + video); few-shot and zero-shot learning; scalability and real-time processing
- 17.12Tools Covered: Python, Hugging Face Transformers, LangChain, OpenAI API, Ollama, PyTorch, TensorFlow, Vector Databases, evaluation tools
- Prompt engineering1
- Database5
- Cloud4
- Reinforcement Learning5
- Specialisation OptionsOption 1 - Guided Project – AI & Data Science (Mentor-Led, Production-Style Projects) Option 2 - Cloud DevOps & Deployment (cloud infra, CI/CD, MLOps deployment practices) Option 3 - Introduction to Big Data with Databricks and Apache Spark12
- 22.1Option 1 – Guided Project – AI & Data Science (Mentor-Led, Production-Style Projects) Learners will apply their AI and Data Science skills to end-to-end, production-grade projects under mentor supervision, resulting in an industry-ready portfolio of deployable AI applications.
- 22.2Option 2 – Cloud DevOps & Deployment (cloud infra, CI/CD, MLOps deployment practices)
- 22.3Introduction & Theory
- 22.4Cloud Infrastructure
- 22.5CI/CD
- 22.6MLOps Deployment (End-to-End ML Lifecycle)
- 22.7Option 3 – Introduction to Big Data with Databricks and Apache Spark
- 22.8Introduction and Fundamentals
- 22.9Databricks Notebook
- 22.10Data Handling and SQL in Databricks
- 22.11Data Analysis and Visualization
- 22.12Machine Learning with Databricks
- Internship - 4 live projects (mapped to chosen specialisation stream), Internship duration - 6 to 8 months0
Courses you might be interested in
-
70 Lessons
-
51 Lessons
-
97 Lessons
-
135 Lessons