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Easy Learning with Data Science & AI Masters 2025 - From Python To Gen AI
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Become a Data Science & AI Master in 2025: Python to Generative AI

What you will learn:

  • Master Python programming for AI applications.
  • Understand and apply Machine Learning and Deep Learning algorithms.
  • Build sophisticated NLP applications using transformer models.
  • Develop advanced AI applications with RAG and Langchain.
  • Utilize vector databases for efficient embedding management.
  • Master the complete NLP pipeline, from preprocessing to deployment.
  • Explore Large Language Models (LLMs) and generative tasks.
  • Become proficient in prompt engineering for optimal model performance.

Description

Embark on a transformative journey with our Data Science & AI Masters 2025 bootcamp! Designed for both beginners and experienced professionals, this comprehensive program provides a complete education, covering everything from foundational Python programming to the cutting-edge realm of generative AI. Learn in-demand skills and build a robust portfolio.

Master the Fundamentals:

You'll gain a strong foundation in Python, data analysis, statistics, SQL, and core machine learning algorithms. This solid base ensures you are ready to tackle advanced topics with confidence.

Become Proficient in Advanced AI:

Explore the world of deep learning, understand the inner workings of neural networks, and delve into natural language processing (NLP). Master advanced NLP techniques, and harness the power of transformer models like BERT and GPT to build sophisticated generative AI applications. Our curriculum covers practical applications including time series analysis and forecasting, crucial for real-world data science scenarios.

Build Real-World Projects:

This bootcamp isn't just theory – it's hands-on practice. You'll work on stimulating real-world projects throughout the curriculum, culminating in a portfolio that demonstrates your expertise to potential employers.

Generative AI Focus:

The course doesn't stop at deep learning. It delves into Generative AI, including an in-depth exploration of Large Language Models (LLMs), prompt engineering, and Retrieval Augmented Generation (RAG). Learn to use powerful tools like Langchain and understand how vector databases improve your AI projects.

Course Structure:

Our structured approach ensures a seamless learning experience. Each module includes insightful video lectures, interactive exercises, and quizzes, reinforcing your understanding at every stage. By the end, you'll be ready to pursue a successful career in data science and artificial intelligence.

Enroll Now: Take the next step towards a rewarding career in data science. Master the essential skills of the future, and stand out in a highly competitive job market. Start your data science and AI journey today!

Curriculum

Introduction

This introductory section sets the stage for the course. The 'Welcome Page' orients you, 'Introduction to the Course' provides an overview of the curriculum's scope and objectives, and 'Course Resources' guides you to additional learning materials.

Python for Data Science

This module covers essential Python programming concepts for data science, starting with fundamental syntax (variables, data types, operators, control structures, functions, OOPs) and progressing to core libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. You'll also learn about efficient coding practices like map, reduce, and filter operations, as well as file handling.

Business Statistics

This section builds a strong foundation in business statistics. It covers data types, descriptive and inferential statistics, sampling techniques, probability (including Bayes Theorem), probability distributions (uniform, binomial, Poisson, normal), measures of central tendency and dispersion, and hypothesis testing using t-tests, z-tests, chi-square tests, and ANOVA, concluding with practical applications of correlation tests.

Exploratory Data Analysis

This module teaches practical data exploration, from data sourcing and cleaning (handling missing values, feature scaling, outlier treatment) to various analysis types (univariate, bivariate, multivariate). It covers numerical analysis techniques and creating derived metrics and features using binning and encoding, culminating in a comprehensive case study that applies these skills.

SQL for Data Science

This module provides a complete guide to SQL, including database architecture, DDLs, DQLs, DMLs, joins, data import/export, aggregation and string functions, date/time functions, regular expressions, nested queries, views, stored procedures, window functions, and finally, connecting SQL with Python for efficient data management.

Machine Learning

This comprehensive section covers a wide range of machine learning concepts and techniques, from foundational topics like the different types of ML and data preprocessing (train-test split, feature scaling, encoding) to various regression models (simple linear, multiple linear, polynomial, Ridge, Lasso), and classification models (kNN, Decision Tree, Random Forest, Naive Bayes, SVM, Logistic Regression). Furthermore, it covers ensemble learning, including bagging, boosting (AdaBoost, Gradient Boost, XGBoost), clustering (kMeans, hierarchical, mean shift), hyperparameter optimization (manual, GridSearchCV, RandomizedSearchCV), and feature engineering using RFE, SFS, chi-square, PCA, and LDA.

Time Series Analysis & Forecasting

This module focuses on time series analysis and forecasting, covering concepts like stationarity testing, transformations, handling missing values and outliers, different models (ARIMA, Facebook Prophet), and multi-variate time series analysis. The practical application is emphasized through several comprehensive projects, including energy forecasting, stock market prediction, and demand forecasting, using techniques such as additive and multiplicative decomposition and ACF/PACF plots. Metrics for forecasting evaluation are also covered.

Deep Learning & Neural Networks

This module explores deep learning, starting with foundational concepts like neurons, activation functions (step, linear, sigmoid, tanh, ReLU), backpropagation, and gradient descent. It covers artificial neural networks, convolutional neural networks (CNNs – including image augmentation, batch size, iterations, epochs), recurrent neural networks (RNNs, LSTMs, GRUs), and pre-trained models (VGG16, MobileNet) and transfer learning. Practical projects like X-ray detection and pneumonia detection using CNNs are included.

Natural Language Processing

This module offers a comprehensive exploration of Natural Language Processing (NLP), covering basics like case folding, stemming, lemmatization, handling contractions, tokenization, stop word removal, n-grams, vectorization, and word embeddings (Word2Vec, GloVe, FastText). Advanced topics include part-of-speech tagging, named entity recognition, and the use of LSTMs and GRUs for NLP tasks.

Transformers & Generative AI

This section delves into the world of transformers and generative AI, explaining different transformer types, self-attention mechanisms, encoder and decoder architectures, and popular models like BERT, RoBERTa, DistilBERT, and ALBERT. It covers GPT architecture, LLM basics (tokens, context window, prompting, prompt engineering, prompt tuning), RAG (Retrieval Augmented Generation), Langchain, and vector databases, with several practical project examples.

ML/DL Deployment

This module demonstrates the deployment of machine learning and deep learning models. Using Flask, it shows how to create a basic web application and deploy a breast cancer prediction model. It also includes a brief overview of deploying models on AWS.

Data Engineering Basics

This section introduces fundamental data engineering concepts, including ETL processes, ETL tools, data warehouses, data marts, and data lakes. It explains the distinctions and benefits of these architectures and their components.

Generative AI Projects

This final section showcases real-world generative AI applications through several case studies: building a ChatScholar (EdTech project), a research RAG chatbot, an automated AI claims processing system, a multi-PDF RAG chatbot (using web-scraped data), and a comprehensive AI Career Coach project.

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