200+ Days: The Complete Pro AIMLDL & Data Science Bootcamp™ | Udemy
English | Size: 42.95 GB
Genre: eLearning[/center]
Mastering Data Science, Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) from beginner to expert
What you'll learn
Understand the fundamentals of Artificial Intelligence, Machine Learning, Data Science Deep Learning and Becoming Pro.
Gain deep knowledge of supervised, unsupervised, and reinforcement learning techniques.
Learn how to clean, explore, and visualize data using Python and powerful libraries like Pandas and Matplotlib.
Master statistical analysis, hypothesis testing, and inferential statistics for data-driven decision making.
Build strong foundations in Linear and Logistic Regression, Decision Trees, and Random Forests.
Learn to evaluate model accuracy using metrics such as precision, recall, F1-score, and ROC-AUC.
Perform hyperparameter tuning and model optimization using Grid Search and Random Search.
Understand dimensionality reduction techniques including PCA and t-SNE.
Implement real-world clustering algorithms like K-Means and DBSCAN.
Develop expertise in Support Vector Machines (SVM) and Kernel Functions.
Apply K-Nearest Neighbors and Gradient Boosting models to classification and regression tasks.
Understand the complete data science lifecycle from problem definition to business impact.
Master end-to-end feature engineering and feature selection techniques for model improvement.
Learn how to handle imbalanced datasets and apply advanced sampling strategies like SMOTE.
Gain hands-on experience with model deployment using Flask, Docker, and cloud services.
Develop working knowledge of MLOps tools and CI/CD workflows in machine learning projects.
Analyze case studies across healthcare, finance, retail, marketing, and energy domains.
Understand the ethical implications and responsibilities of AI and data-driven decision making.
Master Generative AI tools including ChatGPT, Gemini, Claude, and open-source models.
Create and fine-tune LLMs for specific business use cases using Hugging Face Transformers.
Understand the architecture and capabilities of transformer-based models like BERT and GPT.
Build interactive AI agents, chatbots, and autonomous workflows using LangChain and vector databases.
Apply deep learning frameworks such as TensorFlow and PyTorch for computer vision and NLP.
Work on real-world capstone projects across industries to demonstrate your applied expertise.
Receive global certification and mentorship to become an ethical and innovative AI leader.
This 200+ day globally recognized, industry-focused bootcamp is your all-in-one training for mastering Artificial Intelligence, Data Science, Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) from beginner to expert level with simplicity and depth. Designed for aspiring data scientists, software engineers, AI professionals, and innovation leaders, this course offers a blend of foundational theory, programming practice, machine learning applications, and real-world project. The curriculum aligns with current global AI trends and industry hiring standards.
Whether you're targeting top roles in global tech firms, launching an AI-powered startup, or aiming to build a strong data science portfolio this bootcamp ensures you stay ahead in the global AI race.
Core Modules (Search engine optimization SEO Keywords: Data Science, Python, AI, Machine Learning, Generative AI)
Data Science Fundamentals
Data Science Sessions Part 1 & 2 - Foundation of modern data science methodologies and approaches.
Data Science vs Traditional Analysis - Comparing data science techniques with conventional statistical methods.
Data Scientist Journey Parts 1 & 2 - Skills, roles, and global career pathways.
Data Science Process Overview - End-to-end project lifecycle and workflows.
Programming Essentials (Python & R for Data Science)
Introduction to Python for Data Science - Syntax, structures, and data analysis workflows.
Python Libraries: Numpy, Pandas, Matplotlib, Seaborn - Building blocks for data processing and visualization.
Introduction to R - Fundamentals of R programming for statistics and machine learning.
Data Structures and Functions - Hands-on practice in Python & R for real-world data operations.
Data Collection & Preprocessing
Methods of Data Collection - Surveys, APIs, sensors, web scraping.
Data Preprocessing - Cleaning and transforming datasets (Parts 1 & 2).
Exploratory Data Analysis (EDA) - Visual insights and initial hypothesis building.
Data Wrangling - Reshaping, merging, and preparing data.
Handling Missing Data & Outliers - Imputation and anomaly handling techniques.
Visualization & Inferential Statistics
Data Visualization Techniques - Choosing optimal charts and visuals.
Tableau for Data Analytics - Interactive dashboards and storytelling.
Inferential Statistics - Hypothesis testing, confidence intervals, and real-world decisions.
Machine Learning Mastery (Supervised, Unsupervised, Reinforcement)
Introduction to ML - Learning types and business applications.
Supervised Learning - Regression, classification, decision trees, random forest, SVM, KNN.
Unsupervised Learning - Clustering (K-Means, DBSCAN), PCA, anomaly detection.
Reinforcement Learning - Agents, rewards, exploration vs exploitation.
Evaluation & Tuning - Confusion matrix, ROC AUC, cross-validation, hyperparameter optimization.
Applied Machine Learning (Projects & Industry Use-Cases)
Regression & Classification Project Workflows - Linear, multiple linear, logistic regression.
Decision Tree & Random Forest Implementation - Real-world dataset applications.
SVM, KNN, Gradient Boosting Projects - Comparative performance tuning.
Advanced Topics in Data Science & ML
Dimensionality Reduction - PCA, LDA, t-SNE for visualization and modeling.
Feature Engineering - Variable transformation, encoding, feature selection.
SQL for Data Science - Querying relational databases and joining data.
AI Ethics & Governance - Fairness, accountability, transparency.
Deep Learning Specialization
Artificial Neural Networks (ANNs) - Perceptron, activation functions, loss functions.
CNNs for Computer Vision - Image classification, object detection.
RNN, LSTM, GRU - Sequential data modeling for time series and NLP.
Applications - Healthcare, finance, autonomous systems, sentiment analysis.
Generative AI & Foundation Models (LLMs, GANs, Transformers)
What is Generative AI? - GenAI vs Traditional AI.
GANs - Generator-discriminator architecture, synthetic data, image generation.
Transformers & LLMs - GPT, BERT, prompt engineering, attention mechanisms.
Text-to-Image Models - DALL·E, Midjourney, Stable Diffusion.
Voice, Video & Music Generation - Tools for creative AI use-cases.
Transfer Learning & Fine-tuning - Using pre-trained models like VGG, ResNet, Inception.
Model Evaluation & Deployment
Metrics for Classification & Regression - Accuracy, AUC, MSE, R2.
Loss Functions - Cross-entropy, MSE.
Model Deployment - APIs, Flask, Streamlit, cloud hosting.
Capstone Projects & Real-World Implementation
End-to-End Projects - Healthcare analytics, fraud detection, retail forecasting.
Industry Case Studies - Banking, e-commerce, NLP, and logistics.
AIML DL and Data Science Certification™
Who Should Enroll
Aspiring Data Scientists, AI Engineers, and ML Practitioners
Software Developers & Engineers aiming to transition to AI roles
Business Analysts & BI Professionals seeking advanced analytics skills
Founders, Innovators & Product Leaders building AI-first solutions
Working Professional from any Industry aiming to transition to AI roles
Top Executives. CXO's and Business Owners
Global Career Outcomes & Skills Gained
Master tools like Python, R, SQL, Tableau, TensorFlow, PyTorch, Hugging Face
Build a strong AI/ML portfolio with industry-level projects
Apply for global roles such as: Data Scientist, Machine Learning Engineer, Generative AI Developer, AI Product Manager, and more
Understand and deploy models that power cutting-edge innovations globally
Join the future of AI today and become a certified, globally competitive AI & Data Science expert.
Who this course is for:
For anyone aspiring to future skills - from Deep Learning Engineer to AI Scientist - ready to build a career in AIML and Data Science across industries.
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