Embark on an unforgettable journey with us, and get ready to unleash your inner machine learning and deep learning expert. Welcome to the Machine Learning Mastery community, where your future is crafted by your own hands!

Build Application with ChatGPT

Next Class : Tuesdays and Thursdays for 10 days from May 23rd

Duration : 6:30 PM to 8:30 PM (PST)

Location : Online using Google Meet

Cost : USD 499.99

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Overview of the course

Join us for an exciting and comprehensive course on harnessing the incredible potential of ChatGPT using OpenAI’s API and Python! Whether you’re an AI enthusiast, a software engineer, or a data scientist, our 10-session course is designed to equip you with the skills to construct robust applications with one of the world’s most advanced language models.

Our expert-led course offers step-by-step instructions on how to interact with the OpenAI API, integrate it with Python, and create practical, innovative applications using ChatGPT. From understanding the basics of ChatGPT and the OpenAI API to designing conversational agents for customer service or content generation, we’ve got you covered!

By the end of this course, you’ll be confidently building, testing, and deploying AI applications using ChatGPT and Python. Embark on this transformative learning journey and discover how to revolutionize your projects with the power of Conversational AI!

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Introduction to ChatGPT and OpenAI

Next Class : May 20th 9:30 AM PST

Duration : 2 hours

Cost : Free

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Overview of the course

By the end of this course, you will not only have a solid understanding of ChatGPT and its API, but also hands-on experience in working with it in Python, specifically within a Jupyter notebook environment. You will have the skills and knowledge necessary to begin building your own applications using the ChatGPT-API. We look forward to your active participation and are excited to see what you will build!

Introduction to Machine Learning with Scikit-Learn:

Overview of the course

Embark on a thrilling journey into the world of machine learning with our comprehensive course on Scikit-Learn, one of the most widely used and powerful libraries in the Python ecosystem. This course covers everything from basic concepts and techniques to advanced algorithms and ensemble learning methods. Along the way, you will learn how to preprocess data, engineer features, and evaluate models using Scikit-Learn’s versatile API. By the end of this course, you will possess the skills and knowledge to tackle real-world machine learning problems and harness the power of data to unlock valuable insights.

Module 1: Introduction to Machine Learning and Scikit-Learn

  1. Overview of Machine Learning
  2. Introduction to Scikit-Learn
  3. Installation and Setup
  4. Scikit-Learn’s API and Basic Usage

Module 2: Data Preprocessing and Feature Engineering

  1. Data Loading and Exploration
  2. Handling Missing Data
  3. Categorical Data Encoding
  4. Feature Scaling and Normalization
  5. Feature Selection Techniques

Module 3: Supervised Learning Algorithms

  1. Linear Regression
  2. Logistic Regression
  3. k-Nearest Neighbors (kNN)
  4. Decision Trees
  5. Random Forests
  6. Support Vector Machines (SVM)
  7. Naive Bayes

Module 4: Unsupervised Learning Algorithms

  1. k-Means Clustering
  2. Hierarchical Clustering
  3. Principal Component Analysis (PCA)
  5. Gaussian Mixture Models (GMM)

Module 5: Model Evaluation and Hyperparameter Tuning

  1. Train-Test Split and Cross-Validation
  2. Model Evaluation Metrics
  3. Hyperparameter Tuning Techniques
  4. Grid Search and Randomized Search

Module 6: Ensemble Learning and Advanced Techniques

  1. Bagging and Boosting
  2. Stacking
  3. Feature Importance
  4. Advanced Model Selection Techniques

Introduction to Deep Learning with PyTorch and TensorFlow:

Dive into the fascinating realm of deep learning with our extensive course focused on the two leading frameworks, PyTorch and TensorFlow. This curriculum encompasses the fundamentals of deep learning, including neural networks, activation functions, and backpropagation, as well as more advanced architectures like CNNs, RNNs, LSTMs, and GANs. You will also explore data preprocessing, model training and evaluation, and optimization techniques, all while working hands-on with real-world datasets. By the end of this course, you will have a deep understanding of deep learning concepts and be ready to build, train, and deploy state-of-the-art models to tackle complex challenges across various domains.

Module 1: Introduction to Deep Learning, PyTorch, and TensorFlow 1.1 Overview of Deep Learning 1.2 Introduction to PyTorch and TensorFlow 1.3 Installation and Setup 1.4 Basic Usage and Syntax

Module 2: Neural Networks and Their Building Blocks 2.1 Introduction to Neural Networks 2.2 Neurons, Activation Functions, and Layers 2.3 Feedforward Neural Networks (FNN) 2.4 Backpropagation and Gradient Descent

Module 3: Deep Learning Architectures and Models 3.1 Convolutional Neural Networks (CNN) 3.2 Recurrent Neural Networks (RNN) 3.3 Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) 3.4 Autoencoders (AE) and Variational Autoencoders (VAE) 3.5 Generative Adversarial Networks (GAN)

Module 4: Working with Data and Preprocessing 4.1 Loading and Preprocessing Data 4.2 Data Augmentation Techniques 4.3 Working with Images and Text Data 4.4 Pretrained Models and Transfer Learning

Module 5: Model Training, Evaluation, and Optimization 5.1 Loss Functions and Optimizers 5.2 Training and Validation Splits 5.3 Model Evaluation Metrics 5.4 Hyperparameter Tuning 5.5 Regularization Techniques (Dropout, L1/L2 regularization)

Module 6: Advanced Deep Learning Concepts and Techniques 6.1 Reinforcement Learning (RL) 6.2 Attention Mechanisms 6.3 Transformer Models and BERT 6.4 Deployment and Production Considerations 6.5 Model Interpretability and Explainability