AI Agents for Everyone and Artificial Intelligence Bootcamp
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The course “AI Agents for Everyone and Artificial Intelligence Bootcamp” is designed to demystify the world of intelligent systems, making it accessible to learners of all levels. Whether you’re a curious beginner or an aspiring AI developer, this course provides a comprehensive foundation in the development, deployment, and application of AI agents across various domains. With a strong emphasis on hands-on learning, participants will explore state-of-the-art technologies such as machine learning, natural language processing (NLP), and advanced frameworks like AutoGPT, IBM Bee, LangGraph, and CrewAI.
Throughout the course, learners will gain a deep understanding of how AI agents function, from basic reflex agents to advanced collaborative systems. You’ll learn about the core principles that govern intelligent agents, including decision-making, adaptability, and autonomy. By understanding these foundations, you will be equipped to create AI agents that can perceive their environment, make informed decisions, and perform complex tasks. The course also delves into the critical technologies that power AI agents, such as machine learning algorithms for predictive insights, NLP techniques for conversational AI, and robotics integration for automation.
One of the course’s unique aspects is its focus on practical application. You will work on hands-on projects to develop and deploy AI agents in real-world scenarios. From creating collaborative systems with CrewAI to implementing stateful interactions using LangGraph, you’ll get valuable experience with cutting-edge tools and frameworks. Additionally, the course explores the transformative potential of AI agents in industries such as healthcare, finance, business operations, entertainment, and IoT, providing actionable insights into their role in shaping the future.
Ethics and societal impact are integral to this learning experience. The course examines the ethical considerations and regulatory challenges surrounding AI agents, empowering you to approach development with responsibility and foresight. You’ll explore the implications of deploying AI agents in various contexts, understanding how to address bias, ensure fairness, and adhere to legal and ethical standards. By the end of this course, you’ll have a nuanced perspective on the role of AI in modern society, recognizing its potential to foster innovation while navigating its challenges.
The course culminates in an exploration of future trends, showcasing how AI agents are set to redefine collaboration, enhance public safety, and accelerate scientific research. With insights into emerging technologies and methodologies, you’ll leave equipped to stay ahead in the rapidly evolving AI landscape. By the end of the bootcamp, you’ll have a solid foundation in AI agent development, a portfolio of completed projects, and the confidence to apply your skills to real-world challenges. Whether your goal is to advance your career, innovate within your organization, or simply gain a deeper understanding of AI, this course is your gateway to mastering the exciting and impactful field of intelligent agents.
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30Introduction to Week 1 Python Programming BasicsVideo lesson
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31Day 1: Introduction to Python and Development SetupVideo lesson
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32Day 2: Control Flow in PythonVideo lesson
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33Day 3: Functions and ModulesVideo lesson
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34Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)Video lesson
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35Day 5: Working with StringsVideo lesson
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36Day 6: File HandlingVideo lesson
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37Day 7: Pythonic Code and Project WorkVideo lesson
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38Introduction to Week 2 Data Science EssentialsVideo lesson
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39Day 1: Introduction to NumPy for Numerical ComputingVideo lesson
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40Day 2: Advanced NumPy OperationsVideo lesson
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41Day 3: Introduction to Pandas for Data ManipulationVideo lesson
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42Day 4: Data Cleaning and Preparation with PandasVideo lesson
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43Day 5: Data Aggregation and Grouping in PandasVideo lesson
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44Day 6: Data Visualization with Matplotlib and SeabornVideo lesson
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45Day 7: Exploratory Data Analysis (EDA) ProjectVideo lesson
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46Introduction to Week 3 Mathematics for Machine LearningVideo lesson
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47Day 1: Linear Algebra FundamentalsVideo lesson
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48Day 2: Advanced Linear Algebra ConceptsVideo lesson
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49Day 3: Calculus for Machine Learning (Derivatives)Video lesson
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50Day 4: Calculus for Machine Learning (Integrals and Optimization)Video lesson
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51Day 5: Probability Theory and DistributionsVideo lesson
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52Day 6: Statistics FundamentalsVideo lesson
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53Day 7: Math-Driven Mini Project – Linear Regression from ScratchVideo lesson
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54Introduction to Week 4 Probability and Statistics for Machine LearningVideo lesson
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55Day 1: Probability Theory and Random VariablesVideo lesson
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56Day 2: Probability Distributions in Machine LearningVideo lesson
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57Day 3: Statistical Inference - Estimation and Confidence IntervalsVideo lesson
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58Day 4: Hypothesis Testing and P-ValuesVideo lesson
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59Day 5: Types of Hypothesis TestsVideo lesson
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60Day 6: Correlation and Regression AnalysisVideo lesson
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61Day 7: Statistical Analysis Project – Analyzing Real-World DataVideo lesson
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62Introduction to Week 5 Introduction to Machine LearningVideo lesson
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63Day 1: Machine Learning Basics and TerminologyVideo lesson
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64Day 2: Introduction to Supervised Learning and Regression ModelsVideo lesson
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65Day 3: Advanced Regression Models – Polynomial Regression and RegularizationVideo lesson
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66Day 4: Introduction to Classification and Logistic RegressionVideo lesson
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67Day 5: Model Evaluation and Cross-ValidationVideo lesson
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68Day 6: k-Nearest Neighbors (k-NN) AlgorithmVideo lesson
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69Day 7: Supervised Learning Mini ProjectVideo lesson
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70Introduction to Week 6 Feature Engineering and Model EvaluationVideo lesson
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71Day 1: Introduction to Feature EngineeringVideo lesson
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72Day 2: Data Scaling and NormalizationVideo lesson
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73Day 3: Encoding Categorical VariablesVideo lesson
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74Day 4: Feature Selection TechniquesVideo lesson
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75Day 5: Creating and Transforming FeaturesVideo lesson
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76Day 6: Model Evaluation TechniquesVideo lesson
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77Day 7: Cross-Validation and Hyperparameter TuningVideo lesson
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78Introduction to Week 7 Advanced Machine Learning AlgorithmsVideo lesson
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79Day 1: Introduction to Ensemble LearningVideo lesson
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80Day 2: Bagging and Random ForestsVideo lesson
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81Day 3: Boosting and Gradient BoostingVideo lesson
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82Day 4: Introduction to XGBoostVideo lesson
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83Day 5: LightGBM and CatBoostVideo lesson
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84Day 6: Handling Imbalanced DataVideo lesson
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85Day 7: Ensemble Learning Project – Comparing Models on a Real DatasetVideo lesson

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