Ai In A Weekend: Unlock The Future [2024]

Learn AI, Machine Learning, Deep Learning, Neural Networks and more in a weekend! No coding or tech background needed!

What you'll learn

Comprehensive understanding of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) concepts

How Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) differ from one another

Natural Language Processing (NLP)

Deep Learning and Neural Networks

Large Language Models (LLM)

Artificial Neural Networks (ANN)

Transformer Architecture

Convolutional Neural Networks (CNN)

ChatGPT, BERT, CoPilot, Gemini

Recurrent Neural Networks (RNN)

Machine Learning (ML) Algorithms

Long Short-term Memory Networks (LSTM)

Classification, Regression, and Clustering

Reinforcement Learning Algorithms

Cloud Applications of AI

Association Rule Learning Algorithms

Real-life Applications of AI, ML, and DL

Risks of AI

Ethical AI

Requirements

No technical background needed

No coding experience needed.

No math knowledge needed.

Description

Welcome to our comprehensive course on Artificial Intelligence! You will dive into the world of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), Data Science, and cutting-edge AI technologies such as Generative AI, ChatGPT, LSTM, and Transformers. Designed for learners of all levels, this course covers the fundamental principles and advanced techniques that power modern AI applications.In this course, you will explore:Artificial Intelligence: Understand the basics of AI and its impact on industries, careers, and everyday life.Machine Learning: Learn about supervised, semi-supervised, unsupervised, and reinforcement learning techniques, including regression, classification, and clustering models, along with practical applications from data science such as computer vision, Deep Learning: Explore Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-term Memory Networks  (LSTM), mastering concepts like perceptron, backpropagation, gradient descent, and more.Natural Language Processing (NLP): Discover how machines understand and generate human language, from Bag of Words to advanced transformer models and NLP applications.Large Language Models (LLM): Gain insights into the powerful models behind AI text generation, prompt engineering, and fine-tuning for domain-specific applications.Emerging AI Technologies: Stay ahead with the latest developments in cloud AI, generative AI, ChatGPT, CoPilot, Gemini, Autoencoders, Self-Organizing Maps, Boltzmann Machines, and more.Whether you're a beginner looking to start your AI journey or an experienced professional aiming to deepen your knowledge, this course will equip you with the skills and tools to succeed in the ever-evolving field of artificial intelligence. By the end of the course, you’ll be able to understand AI/ML models, understand large-scale NLP systems, and leverage cloud computing to power your AI solutions!No technical, coding or math background is needed!Complex topics are broken into digestible lessons, helping people from all backgrounds understand AI core principles and applications.I can't wait to see you in the course!Omer

Overview

Section 1: Welcome to the AI in a Weekend Course!

Lecture 1 Welcome to "AI in A Weekend: Unlock the Future" Course!

Lecture 2 What to expect from this course?

Lecture 3 Course Structure

Section 2: Overview of Artificial intelligence, Machine Learning, and Deep Learning

Lecture 4 What You Will Learn in This Section

Lecture 5 A Quick Note - Overview of AI, Machine Learning (ML), Deep Learning (DL)

Lecture 6 What is Artificial Intelligence?

Lecture 7 History of Articial Intelligence

Lecture 8 What is Machine Learning?

Lecture 9 What is Deep Learning?

Section 3: Types of AI | ML vs Programming | Advantages & Disadvantages | Ethical AI

Lecture 10 What You Will Learn in This Section

Lecture 11 Types of AI

Lecture 12 Traditional Programming vs Machine Learning, Advantages and Disadvantages of AI

Lecture 13 Which Industries will be Impacted the Most?

Lecture 14 Responsbile AI

Section 4: Descriptive AI, Predictive AI, Prescriptive AI, Diagnostic AI,NLP, Generative AI

Lecture 15 What You Will Learn in This Section

Lecture 16 Descriptive AI & Predictive AI

Lecture 17 Prescriptive AI & Diagnostic AI

Lecture 18 Natural Language Processing (NLP) & Generative AI

Section 5: Overview of Machine Learning Methods

Lecture 19 What You Will Learn in This Section

Lecture 20 What is Machine Learning? What is Supervised Learning?

Lecture 21 What is Semi-Supervised Learning? What is Unsupervised Learning?

Lecture 22 What is Reinforcement Learning, Ensemble Learning, and Transfer Learning?

Lecture 23 Deep Learning vs Machine Learning

Lecture 24 Training a Machine Learning Model

Section 6: Regression - (Machine Learning Methods - Supervised Learning)

Lecture 25 What You will Learn in This Section

Lecture 26 Simple Linear Regression

Lecture 27 Multiple Linear Regression

Lecture 28 Polynomial Regression

Lecture 29 Support Vector Regression

Lecture 30 Decision Tree Regression

Lecture 31 Random Forest Regression

Lecture 32 Regression Model Performance

Lecture 33 Conclusion

Lecture 34 Business Use Cases

Section 7: Classification (Machine Learning Methods - Supervised Learning)

Lecture 35 What You will Learn in This Section

Lecture 36 Logistic Regression

Lecture 37 K-Nearest Neighbors (K-NN)

Lecture 38 Support Vector Machine

Lecture 39 Kernel SVM

Lecture 40 Naïve Bayes

Lecture 41 Decision Tree Classification

Lecture 42 Random Forest Classification

Lecture 43 Classification Model Performance Evaluation

Section 8: Clustering (Machine Learning Methods - Unsupervised Learning)

Lecture 44 What You will Learn in This Section

Lecture 45 K-Means Clustering

Lecture 46 Hierarchical Clustering

Lecture 47 Conclusion

Section 9: Association Rule Learning (Machine Learning Methods - Unsupervised Learning)

Lecture 48 What You will Learn in This Section

Lecture 49 What is Association Rule Learning? What is Apriori?

Lecture 50 Eclat and FP-Growth

Lecture 51 Real-life Applications and Conclusion

Section 10: Reinforcement Learning (Neither Supervised nor Unsupervised)

Lecture 52 What You Will Learn in This Section

Lecture 53 What is Reinforcement Learning?

Lecture 54 Terminology

Lecture 55 Bellman Equation

Lecture 56 Markov Decision Process

Lecture 57 Q-Learning

Lecture 58 Deep Q-Learning and Deep Convolutional Q-Learning

Lecture 59 A3C

Lecture 60 Multi-armed Bandit Problem

Lecture 61 Upper Confidence Bound

Lecture 62 Thompson Sampling

Lecture 63 Real-life Applications

Section 11: Dimentionality Reduction (Machine Learning Methods - Unsupervised Learning)

Lecture 64 What You Will Learn in This Section

Lecture 65 What is Dimentionality Reduction?

Lecture 66 Principal Component Analysis

Lecture 67 Linear Discriminant Analysis

Section 12: Deep Learning - Artificial Neural Networks (ANN)

Lecture 68 What You Will Learn in This Section

Lecture 69 Deep Learning vs Machine Learning

Lecture 70 What are Neural Networks? + Let's Compare Deep Learning & Neural Networks

Lecture 71 What is a Neuron? What is a Perceptron?

Lecture 72 Inputs, Weights, and Biases

Lecture 73 Net Input Function

Lecture 74 Activation Functions and Outputs

Lecture 75 Single-Layer Perceptron & Multi-Layer Perceptron

Lecture 76 Hidden Layers

Lecture 77 How do Artificial Netural Networks Learn?

Lecture 78 Forward Propagation vs Backpropagation

Lecture 79 Gradient Descent

Lecture 80 Batch, Stochastic, and Mini-batch Gradient Descent

Lecture 81 Terminologies Used in Neural Networks

Section 13: Deep Learning - Generative Adversarial Networks (GAN)

Lecture 82 What You Will Learn in This Section

Lecture 83 Generative Adversarial Networks (GANs)

Section 14: Deep Learning - Convolutional Neural Networks (CNN)

Lecture 84 What You Will Learn in This Section

Lecture 85 What are Convolutional Neural Networks?

Lecture 86 Convolutional Network Layers

Lecture 87 Cross Entropy and CNN Visualizations

Section 15: Deep Learning - Recurrent Neural Networks (RNN)

Lecture 88 What You Will Learn in This Section

Lecture 89 What are Recurrent Neural Networks?

Lecture 90 How do Recurrent Neural Networks work?

Lecture 91 The Vanishing-Exploding Gradient Problem

Lecture 92 Long Short-Term Memory (LSTM)

Section 16: Deep Learning - Self-Organizing Maps

Lecture 93 What You Will Learn in This Section

Lecture 94 What is a Self-Organizing Map?

Lecture 95 How do Self-Organizing Maps Work?

Lecture 96 Self-Organizing Map Example from US Congress

Lecture 97 Self-Organizing Map Applications

Lecture 98 SOM Visualizations

Section 17: Deep Learning - Boltzmann Machine

Lecture 99 What You Will Learn in This Section

Lecture 100 What is a Boltzmann Machine?

Lecture 101 Restricted Boltzmann Machine

Lecture 102 How does Restricted Boltzmann Machine work?

Lecture 103 Restricted Boltzmann Machine Applications

Lecture 104 Deep Belief Networks & Deep Boltzmann Machine

Lecture 105 Deep Belief Networks vs Deep Boltzmann Machines

Section 18: Deep Learning - Autoencoders

Lecture 106 What You Will Learn in This Section

Lecture 107 What is an Autoencoder? - Structure and Working Mechanism

Lecture 108 Types of Autoencoders

Lecture 109 Autoencoder Applications

Section 19: Natural Language Processing (NLP)

Lecture 110 What You Will Learn in This Section

Lecture 111 What is Natural Language Processing?

Lecture 112 NLP Use Cases

Lecture 113 How does NLP work?

Lecture 114 Bag of Words (BOW)

Lecture 115 TF-IDF (Term Frequency-Inverse Document Frequency)

Lecture 116 Word Embeddings

Lecture 117 Seq2Seq

Lecture 118 Transformers

Lecture 119 Evaluation and Deployment of NLPs

Section 20: Large Language Models (LLM)

Lecture 120 What You Will Learn in This Section

Lecture 121 NLP vs Large Language Models (LLMs) vs Small Language Models (SLMs)

Lecture 122 Training a Large Language Model

Lecture 123 "Attention is All You Need" and Transformer Model

Lecture 124 How do LLMs work?

Lecture 125 Internal Workings of LLM

Lecture 126 LLM Parameters Fine Tuning Prompt Engineering

Lecture 127 RAG - Retrieval Augmented Generation

Section 21: Cloud Applications of AI and ML

Lecture 128 What You Will Learn in This Section

Lecture 129 What is Cloud? What is Cloud Computing? Types of Cloud Computing?

Lecture 130 Cloud Providers, Pre-built AI Services in Cloud

Lecture 131 Integrating AI with Cloud, Cloud Applications

Section 22: Congratulations!

Lecture 132 Congratulations!

Anyone from any background.,Anyone interested in learning Artificial Intelligence, Machine Learning and Deep Learning concepts.,Any professional who wants to add value to themselves, their job and their business.,Any leader, manager and c-suite executive looking to upskill in emerging technologies like AI, ML and DL.,Anyone who is not comfortable with coding but is interested in emerging technologies like AI, ML and DL.,Anyone who wants to be valuable in job market with AI, ML and DL knowledge,Anyone looking for a new career in Artificial Intelligence and Machine Learning field.