Course Details

Building AI Models with Python and TensorFlow Training Course

Introduction

The field of Artificial Intelligence (AI) and Machine Learning (ML) has become a cornerstone of modern technological innovation, driving advancements across virtually every industry, from healthcare and finance to automotive and entertainment. At the heart of this revolution lies the ability to build sophisticated AI models that can learn from data, make predictions, and automate complex tasks. Python, with its extensive ecosystem of scientific libraries, has emerged as the de facto language for AI development, while TensorFlow, developed by Google, stands as one of the most powerful and widely adopted open-source frameworks for building and deploying machine learning and deep learning models. For aspiring data scientists, machine learning engineers, and developers, mastering Python and TensorFlow is crucial for translating theoretical AI concepts into practical, impactful solutions. Without hands-on expertise in these tools, professionals risk being left behind in a rapidly evolving job market and facing significant barriers to contributing meaningfully to AI projects. Many individuals struggle with the steep learning curve of AI development, including understanding complex algorithms, setting up development environments, managing data pipelines, and effectively training and evaluating models. Conversely, a strong command of Python and TensorFlow empowers practitioners to design, implement, and optimize a wide array of AI models, enabling them to tackle real-world problems and drive significant innovation. Ignoring the foundational importance of these tools means limiting one's ability to participate in and lead the AI transformation. Our intensive 5-day "Building AI Models with Python and TensorFlow" training course is meticulously designed to equip data scientists, machine learning engineers, software developers, and researchers with the essential knowledge and practical skills required to confidently build, train, and deploy various AI models using Python and the TensorFlow framework.

This comprehensive program will delve into Python fundamentals for data science, the core concepts of TensorFlow, and its high-level API Keras. Participants will gain hands-on experience with practical applications in areas such as predictive analytics, image recognition, and natural language processing, covering data preparation, model architecture design, training optimization, and model evaluation. By the end of this course, you will be proficient in conceptualizing, planning, and executing the development of AI models, empowering you to contribute effectively to advanced AI projects and drive data-driven innovation within your organization.

Duration

5 Days

Target Audience

The "Building AI Models with Python and TensorFlow" training course is ideal for professionals who have some programming experience (preferably in Python) and a foundational understanding of mathematics (linear algebra, calculus, statistics) and are looking to specialize in AI and Machine Learning model development. This includes:

• Aspiring Data Scientists: To gain practical skills in building ML/DL models.
• Machine Learning Engineers: To deepen their expertise in TensorFlow for scalable solutions.
• Software Developers: Looking to transition into AI/ML roles or integrate AI into applications.
• Researchers: Applying AI models to analyze complex datasets and solve scientific problems.
• Data Analysts with Python experience: Wanting to move from descriptive to predictive modeling.
• Academics and Students: Pursuing a career in AI and requiring hands-on framework experience.
• Anyone with Python coding knowledge eager to build AI models from the ground up.
• Individuals preparing for TensorFlow Developer Certifications.

Course Objectives

Upon successful completion of the "Building AI Models with Python and TensorFlow" training course, participants will be able to:

• Understand the fundamental concepts of Machine Learning, Deep Learning, and Neural Networks.
• Master essential Python libraries for data manipulation and scientific computing (NumPy, Pandas).
• Utilize TensorFlow and Keras to build, train, and evaluate various types of AI models.
• Implement predictive models for regression and classification tasks.
• Develop and train Convolutional Neural Networks (CNNs) for image recognition.
• Construct and train Recurrent Neural Networks (RNNs) for sequential data, including basic NLP tasks.
• Apply best practices for model optimization, hyperparameter tuning, and regularization.
• Deploy and integrate trained TensorFlow models for practical applications.

 Course Modules

Module 1: Python and Data Fundamentals for AI

• Python refresher for data science: Data types, control flow, functions, object-oriented concepts.
• NumPy: Efficient array manipulation for numerical computing.
• Pandas: Data loading, cleaning, transformation, and analysis with DataFrames.
• Data visualization essentials with Matplotlib or Seaborn.
• Introduction to Machine Learning concepts: Supervised vs. Unsupervised Learning, training, validation, testing.

Module 2: Introduction to TensorFlow and Keras

• Understanding the TensorFlow ecosystem: Core concepts, computation graphs (conceptual).
• Introduction to Keras: High-level API for building and training neural networks easily.
• TensorFlow basics: Tensors, variables, operations.
• Setting up your TensorFlow development environment (Jupyter, Google Colab).
• First steps with Keras: Defining a simple neural network architecture.

Module 3: Building & Training Feedforward Neural Networks

• Neural Network fundamentals: Neurons, layers, activation functions.
• Designing and implementing Multi-Layer Perceptrons (MLPs) with Keras.
• Training process: Forward propagation, backpropagation, gradient descent.
• Loss functions and optimizers for neural networks.
• Hands-on: Building a neural network for a classification or regression problem.

Module 4: Predictive Modeling with TensorFlow (Regression & Classification)

• Implementing linear and logistic regression models with TensorFlow.
• Handling different data types and encoding categorical features.
• Model evaluation metrics for regression (e.g., MSE, RMSE) and classification (e.g., accuracy, precision, recall, F1-score).
• Cross-validation techniques for robust model evaluation.
• Practical case study: Building a predictive model for a real-world dataset.

Module 5: Convolutional Neural Networks (CNNs) for Image Recognition

• Introduction to Computer Vision problems in AI.
• CNN architecture: Convolutional layers, pooling layers, dense layers.
• Understanding feature extraction in CNNs.
• Building and training a CNN with Keras for image classification tasks.
• Techniques for improving CNN performance: Data augmentation, transfer learning (conceptual).

Module 6: Recurrent Neural Networks (RNNs) for Sequential Data & NLP Basics

• Introduction to sequential data (time series, text).
• Understanding Recurrent Neural Networks (RNNs) and their limitations.
• Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs).
• Basic Natural Language Processing (NLP) concepts: Word embeddings, tokenization.
• Hands-on: Building an RNN/LSTM model for text classification or sequence prediction.

Module 7: Model Optimization and Regularization

• Overfitting and Underfitting: Diagnosing and addressing common problems.
• Regularization techniques: L1/L2 regularization, Dropout.
• Hyperparameter tuning: Learning rate, batch size, number of layers, number of units.
• Callbacks in Keras: Early stopping, model checkpointing.
• Saving and loading trained models in TensorFlow.

Module 8: Model Deployment & Advanced Topics (Conceptual)

• Strategies for deploying TensorFlow models (e.g., TensorFlow Serving, Flask/ FastAPI integration).
• Introduction to TensorFlow Lite for mobile/edge deployment (conceptual).
• Introduction to Transfer Learning for leveraging pre-trained models.
• Brief overview of Generative AI concepts (e.g., GANs, Transformers) and TensorFlow's role.
• Best practices for developing and maintaining AI models in production environments.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com