وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

Machine Learning, Data Science and Generative AI with Python

سرفصل های دوره

Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks


01 - Getting Started
  • 001 Introduction
  • 002 Udemy 101 Getting the Most From This Course
  • 003 Important note.html
  • 004 Installation Getting Started.html
  • 005 [Activity] WINDOWS Installing and Using Anaconda & Course Materials
  • 006 [Activity] MAC Installing and Using Anaconda & Course Materials
  • 007 [Activity] LINUX Installing and Using Anaconda & Course Materials
  • 008 Python Basics, Part 1 [Optional]
  • 009 [Activity] Python Basics, Part 2 [Optional]
  • 010 [Activity] Python Basics, Part 3 [Optional]
  • 011 [Activity] Python Basics, Part 4 [Optional]
  • 012 Introducing the Pandas Library [Optional]

  • 02 - Statistics and Probability Refresher, and Python Practice
  • 001 Types of Data (Numerical, Categorical, Ordinal)
  • 002 Mean, Median, Mode
  • 003 [Activity] Using mean, median, and mode in Python
  • 004 [Activity] Variation and Standard Deviation
  • 005 Probability Density Function; Probability Mass Function
  • 006 Common Data Distributions (Normal, Binomial, Poisson, etc)
  • 007 [Activity] Percentiles and Moments
  • 008 [Activity] A Crash Course in matplotlib
  • 009 [Activity] Advanced Visualization with Seaborn
  • 010 [Activity] Covariance and Correlation
  • 011 [Exercise] Conditional Probability
  • 012 Exercise Solution Conditional Probability of Purchase by Age
  • 013 Bayes Theorem

  • 03 - Predictive Models
  • 001 [Activity] Linear Regression
  • 002 [Activity] Polynomial Regression
  • 003 [Activity] Multiple Regression, and Predicting Car Prices
  • 004 Multi-Level Models

  • 04 - Machine Learning with Python
  • 001 Supervised vs. Unsupervised Learning, and TrainTest
  • 002 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression
  • 003 Bayesian Methods Concepts
  • 004 [Activity] Implementing a Spam Classifier with Naive Bayes
  • 005 K-Means Clustering
  • 006 [Activity] Clustering people based on income and age
  • 007 Measuring Entropy
  • 008 [Activity] WINDOWS Installing Graphviz
  • 009 [Activity] MAC Installing Graphviz
  • 010 [Activity] LINUX Installing Graphviz
  • 011 Decision Trees Concepts
  • 012 [Activity] Decision Trees Predicting Hiring Decisions
  • 013 Ensemble Learning
  • 014 [Activity] XGBoost
  • 015 Support Vector Machines (SVM) Overview
  • 016 [Activity] Using SVM to cluster people using scikit-learn

  • 05 - Recommender Systems
  • 001 User-Based Collaborative Filtering
  • 002 Item-Based Collaborative Filtering
  • 003 [Activity] Finding Movie Similarities using Cosine Similarity
  • 004 [Activity] Improving the Results of Movie Similarities
  • 005 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
  • 006 [Exercise] Improve the recommenders results

  • 06 - More Data Mining and Machine Learning Techniques
  • 001 K-Nearest-Neighbors Concepts
  • 002 [Activity] Using KNN to predict a rating for a movie
  • 003 Dimensionality Reduction; Principal Component Analysis (PCA)
  • 004 [Activity] PCA Example with the Iris data set
  • 005 Data Warehousing Overview ETL and ELT
  • 006 Reinforcement Learning
  • 007 [Activity] Reinforcement Learning & Q-Learning with Gym
  • 008 Understanding a Confusion Matrix
  • 009 Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
  • external-links.txt

  • 07 - Dealing with Real-World Data
  • 001 BiasVariance Tradeoff
  • 002 [Activity] K-Fold Cross-Validation to avoid overfitting
  • 003 Data Cleaning and Normalization
  • 004 [Activity] Cleaning web log data
  • 005 Normalizing numerical data
  • 006 [Activity] Detecting outliers
  • 007 Feature Engineering and the Curse of Dimensionality
  • 008 Imputation Techniques for Missing Data
  • 009 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE
  • 010 Binning, Transforming, Encoding, Scaling, and Shuffling

  • 08 - Apache Spark Machine Learning on Big Data
  • 001 Warning about Java 21+ and Spark 3!.html
  • 002 Spark installation notes for MacOS and Linux users.html
  • 003 [Activity] Installing Spark
  • 004 Spark Introduction
  • 005 Spark and the Resilient Distributed Dataset (RDD)
  • 006 Introducing MLLib
  • 007 Introduction to Decision Trees in Spark
  • 008 [Activity] K-Means Clustering in Spark
  • 009 TF IDF
  • 010 [Activity] Searching Wikipedia with Spark
  • 011 [Activity] Using the Spark DataFrame API for MLLib

  • 09 - Experimental Design ML in the Real World
  • 001 Deploying Models to Real-Time Systems
  • 002 AB Testing Concepts
  • 003 T-Tests and P-Values
  • 004 [Activity] Hands-on With T-Tests
  • 005 Determining How Long to Run an Experiment
  • 006 AB Test Gotchas

  • 10 - Deep Learning and Neural Networks
  • 001 Deep Learning Pre-Requisites
  • 002 The History of Artificial Neural Networks
  • 003 [Activity] Deep Learning in the Tensorflow Playground
  • 004 Deep Learning Details
  • 005 Introducing Tensorflow
  • 006 [Activity] Using Tensorflow, Part 1
  • 007 [Activity] Using Tensorflow, Part 2
  • 008 [Activity] Introducing Keras
  • 009 [Activity] Using Keras to Predict Political Affiliations
  • 010 Convolutional Neural Networks (CNNs)
  • 011 [Activity] Using CNNs for handwriting recognition
  • 012 Recurrent Neural Networks (RNNs)
  • 013 [Activity] Using a RNN for sentiment analysis
  • 014 [Activity] Transfer Learning
  • 015 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters
  • 016 Deep Learning Regularization with Dropout and Early Stopping
  • 017 The Ethics of Deep Learning

  • 11 - Generative Models
  • 001 Variational Auto-Encoders (VAEs) - how they work
  • 002 Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST
  • 002 variationalautoencoders.zip
  • 003 Generative Adversarial Networks (GANs) - How they work
  • 004 Generative Adversarial Networks (GANs) - Playing with some demos
  • 005 Generative Adversarial Networks (GANs) - Hands-on with Fashion MNIST
  • 005 gan-on-fashion-mnist.zip
  • 006 Learning More about Deep Learning

  • 12 - Generative AI GPT, ChatGPT, Transformers, Self Attention Based Neural Networks
  • 001 The Transformer Architecture (encoders, decoders, and self-attention.)
  • 002 Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth
  • 003 Applications of Transformers (GPT)
  • 004 How GPT Works, Part 1 The GPT Transformer Architecture
  • 005 How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding
  • 006 Fine Tuning Transfer Learning with Transformers
  • 007 transformers-mlcourse.zip
  • 007 [Activity] Tokenization with Google CoLab and HuggingFace
  • 008 [Activity] Positional Encoding
  • 009 [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT
  • 010 [Activity] Using small and large GPT models within Google CoLab and HuggingFace
  • 011 [Activity] Fine Tuning GPT with the IMDb dataset
  • 012 From GPT to ChatGPT Deep Reinforcement Learning, Proximal Policy Gradients
  • 013 From GPT to ChatGPT Reinforcement Learning from Human Feedback and Moderation

  • 13 - The OpenAI API (Developing with GPT and ChatGPT)
  • 001 chat-completions.zip
  • 001 [Activity] The OpenAI Chat Completions API
  • 002 functions.zip
  • 002 [Activity] Using Tools and Functions in the OpenAI Chat Completion API
  • 003 image.zip
  • 003 [Activity] The Images (DALL-E) API in OpenAI
  • 004 embedding.zip
  • 004 [Activity] The Embeddings API in OpenAI Finding similarities between words
  • 005 The Legacy Fine-Tuning API for GPT Models in OpenAI
  • 006 extract-script.zip
  • 006 [Demo] Fine-Tuning OpenAIs Davinci Model to simulate Data from Star Trek
  • 007 The New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!
  • 007 makingdata.zip
  • 008 moderation.zip
  • 008 [Activity] The OpenAI Moderation API
  • 009 audio.zip
  • 009 [Activity] The OpenAI Audio API (speech to text)

  • 14 - Retrieval Augmented Generation (RAG)
  • 001 Retrieval Augmented Generation (RAG) How it works, with some examples
  • 002 Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek
  • 002 data-rag.zip

  • 15 - Final Project
  • 001 Your final project assignment Mammogram Classification
  • 002 Final project review

  • 16 - You made it!
  • 001 More to Explore
  • 002 Dont Forget to Leave a Rating!.html
  • 003 Bonus Lecture.html
  • 139,000 تومان
    بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
    خرید دانلودی فوری

    در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

    ایمیل شما:
    تولید کننده:
    شناسه: 38990
    حجم: 7379 مگابایت
    مدت زمان: 1126 دقیقه
    تاریخ انتشار: 30 تیر 1403
    طراحی سایت و خدمات سئو

    139,000 تومان
    افزودن به سبد خرید