Become an AI Engineer: Master ML, DL & AI Agents
What you will learn:
- Master advanced feature engineering techniques for machine learning.
- Evaluate model performance using precision, recall, F1-score, and AUC.
- Implement and fine-tune decision trees, random forests, and gradient boosting algorithms.
- Understand and apply deep learning concepts, including activation functions and backpropagation.
- Build neural networks from scratch using Python and popular libraries.
- Train and deploy models effectively using TensorFlow and Keras.
- Leverage PyTorch to build, optimize, and evaluate deep learning models.
- Grasp the fundamental principles of AI agents and explore their practical applications.
Description
Transform your career with our comprehensive AI Engineer training program. This intermediate-level course provides a robust foundation in machine learning, deep learning, and AI agent development, equipping you with the practical skills and in-demand tools needed to excel in the field. We go beyond theory, diving deep into real-world applications using TensorFlow and PyTorch.
You'll master essential techniques, starting with data preprocessing and feature engineering, ensuring your models are ready for production. Learn to implement and fine-tune advanced algorithms like decision trees, random forests, gradient boosting, and XGBoost. We then unravel the mysteries of neural networks, covering perceptrons, activation functions, backpropagation, and various network architectures. You'll build and train your own deep learning models from scratch using both TensorFlow/Keras and PyTorch, gaining experience with tensor operations, custom training loops, autograd, and optimizers. Finally, you'll explore the fascinating world of AI agents, understanding their role in decision-making, planning, and task automation through practical examples.
This program isn't just about theory; it's designed for hands-on learning. You'll complete numerous projects and exercises to solidify your understanding and build a portfolio. Prepare to deploy advanced ML models, grasp the mathematical principles behind neural networks, master TensorFlow and PyTorch, and apply AI agent concepts to real-world problems. Upon completion, you'll have the expertise to pursue roles as a Machine Learning Engineer, AI Developer, or other specialized AI positions.
Enroll now and take the first step towards your successful AI engineering career!
Curriculum
Introduction to Course and Instructor
This introductory section sets the stage for the course. The "What You’ll Learn" lecture (6:32) provides a concise overview of the skills and knowledge you'll gain throughout the program, helping you understand the course's scope and structure.
Feature Engineering and Model Evaluation
This module focuses on preparing data for machine learning and evaluating model performance. Lectures cover various techniques like data scaling and normalization, encoding categorical variables, feature selection, and creating new features. It also explores model evaluation metrics such as precision, recall, F1-score, ROC-AUC and cross-validation. Hands-on projects are included, such as preparing features and evaluating models for customer churn.
Advanced Machine Learning Algorithms
Here, you'll delve into the world of advanced machine learning algorithms. The module covers ensemble learning, exploring techniques like bagging, random forests, boosting, XGBoost, LightGBM, and CatBoost. You'll learn how to handle imbalanced datasets and compare models using a real-world dataset. A focus on selecting appropriate models for specific problems, like a fraud detection case study, is included.
Neural Networks and Deep Learning Fundamentals
This section establishes a strong foundation in neural networks and deep learning. It covers fundamental concepts including perceptrons, activation functions, loss functions, backpropagation, and gradient descent. You'll build neural networks with TensorFlow/Keras and PyTorch, culminating in an image classification project using CIFAR-10.
Machine Learning Algorithms and Implementations
This comprehensive section provides practical implementations of various machine learning algorithms in Python, covering linear regression, logistic regression, SVM, decision trees, random forests, clustering techniques (K-means, hierarchical, DBSCAN, GMM), dimensionality reduction (PCA, t-SNE), autoencoders, reinforcement learning algorithms (Q-learning, DQN, policy gradients), anomaly detection (One-Class SVM, Isolation Forest), and deep learning models (CNNs, RNNs, LSTMs, Transformers). Each algorithm is explained conceptually and implemented practically.
Introduction to Machine Learning and TensorFlow
This module is a deep dive into TensorFlow, covering its installation, tensor operations, computational graphs, Keras API, building CNNs and RNNs, model deployment using TensorFlow Serving and Lite, distributed computing, and building end-to-end ML pipelines with TFX. It also includes many examples using different techniques for image classification, NLP, recommender systems, object detection, sentiment analysis and more.
Introduction to Learning PyTorch
This module introduces PyTorch, covering its basics, tensor manipulation, autograd, building neural networks, data loading, model evaluation, advanced architectures, transfer learning, model deployment, debugging, distributed training, and more. It emphasizes practical implementation and research-oriented techniques.
AI Agents for Dummies
This section provides a beginner-friendly introduction to AI agents, covering their functionalities, types, underlying technologies (NLP, Machine Learning), architectures (AutoGPT, IBM Bee, LangGraph, CrewAI), applications across various industries, future trends, and ethical considerations. It's a high-level overview which provides students with insight into the topic.
Congratulations
A brief concluding section offering congratulations and well wishes to the students.
