Fundamentals of AI, Machine Learning, and Python Programming

Embark on a transformative learning experience designed to equip you with a robust understanding of AI, machine learning, and Python programming. This course begins with a thorough introduction to artificial intelligence and machine learning, demystifying the core concepts and exploring how algorithms and data-driven techniques empower computers to learn and adapt. As you progress, you'll delve into the architecture of deep learning and neural networks, grasping how these advanced structures mimic human cognition to process complex data and make accurate predictions. Transitioning from theory to practical application, the course guides you through setting up your development environment with Anaconda, laying the groundwork for efficient coding and package management. You'll then immerse yourself in Python programming, mastering flow control mechanisms, data structures, and functions. The journey continues with an exploration of essential Python libraries such as NumPy, Matplotlib, and Pandas, providing you with the tools to handle data manipulation and visualization effectively. The latter part of the course focuses on advanced AI topics, including the installation and application of deep learning libraries like TensorFlow and PyTorch. You'll learn about the fundamental structures of artificial neurons and neural networks, and the crucial roles of activation functions, loss functions, and optimizers in training models. Through hands-on projects, such as building regression models for house price prediction and binary classification models for heart disease prediction, you'll apply your knowledge to real-world scenarios, reinforcing your learning and enhancing your practical skills. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers. It is ideal for beginners seeking a comprehensive introduction to AI and machine learning, as well as professionals looking to deepen their understanding of these technologies. Prerequisites include basic programming knowledge and a keen interest in artificial intelligence and data science.

• Introduction
• In this module, we will provide a comprehensive introduction to the course. We’ll outline the key topics covered, focusing on deep learning, neural networks, and Generative Adversarial Networks (GANs). This overview will set the stage for your learning journey, giving you a clear roadmap of what to expect.
• Introduction to AI and Machine Learning
• In this module, we will introduce you to the fundamental concepts of artificial intelligence and machine learning. You will learn how AI and machine learning algorithms empower computers to learn, adapt, and make informed decisions based on data.
• Introduction to Deep learning and Neural Networks
• In this module, we will delve into the basics of deep learning and neural networks. We’ll explore how these powerful models are structured and how they process complex data to make predictions, mimicking the way humans learn.
• Setting Up Computer - Installing Anaconda
• In this module, we will guide you through the process of setting up your computer by installing Anaconda. You will learn how to create isolated environments and manage packages, laying a solid foundation for your data science and machine learning projects.
• Python Basics - Flow Control
• In this module, we will cover the essentials of Python flow control mechanisms. You will learn how to manipulate the sequence of code execution, using conditional statements and loops to manage the flow of your programs effectively.
• Python Basics - Lists and Tuples
• In this module, we will explore the basics of Python lists and tuples. You will understand their properties and how they can be used to organize and manipulate data efficiently in your Python programs.
• Python Basics - Dictionaries and Functions
• In this module, we will delve into Python dictionaries and functions. You will learn how to use dictionaries for dynamic data storage and how to create and utilize functions to streamline your code and improve efficiency.
• NumPy Basics
• In this module, we will introduce you to NumPy, a critical library for numerical computations in Python. You will learn how to create and manipulate multidimensional arrays, gaining tools to perform efficient data analysis.
• Matplotlib Basics
• In this module, we will explore the Matplotlib library for data visualization. You will learn how to transform data into insightful visual representations, using plots and histograms to better understand data distributions and patterns.
• Pandas Basics
• In this module, we will dive into the Pandas library, focusing on its powerful data structures: series and data frames. You will learn how to leverage these tools for effective data analysis and manipulation.
• Installing Deep Learning Libraries
• In this module, we will guide you through installing essential deep learning libraries such as TensorFlow and PyTorch. You will learn how to set up these libraries, preparing you for your deep learning journey.
• Basic Structure of Artificial Neuron and Neural Network
• In this module, we will explore the basic structure of artificial neurons and neural networks. You will learn about the building blocks of these models and how they work together to perform complex computations and pattern recognition.
• Activation Functions Introduction
• In this module, we will introduce you to activation functions, which are crucial in shaping the outputs of neural networks. You will understand their role in the learning process and how they impact model performance.
• Popular Types of Activation Functions
• In this module, we will explore popular types of activation functions used in neural networks. You will learn how these functions drive information flow and affect the overall performance of your models.
• Popular Types of Loss Functions
• In this module, we will demystify popular loss functions used in training neural networks. You will learn about mean squared error, cross-entropy, and more, understanding how these functions help in refining model predictions.
• Popular Optimizers
• In this module, we will unravel the world of popular optimizers. You will learn how various algorithms optimize the training of neural networks, improving model accuracy and efficiency.
• Popular Neural Network Types
• In this module, we will explore popular types of neural networks. You will learn about feedforward, convolutional, recurrent networks, and more, understanding their unique architectures and applications in machine learning and AI.
• King County House Sales Regression Model - Step 1 Fetch and Load Dataset
• In this module, we will begin the process of building a regression model to predict house prices in King County, USA. You will learn how to fetch and load datasets, setting the stage for effective data analysis and model training.
• Steps 2 and 3 - EDA and Data Preparation
• In this module, we will dive into exploratory data analysis (EDA) and data preparation. You will learn how to clean and transform data, ensuring it is ready for building accurate and effective machine learning models.
• Step 4 - Defining the Keras Model
• In this module, we will define the Keras model for our regression task. You will learn how to architect the model, setting up the input, hidden, and output layers to create a robust neural network.
• Steps 5 and 6 - Compile and Fit Model
• In this module, we will compile and fit our Keras model. You will learn how to configure the model’s parameters and train it using the prepared dataset, optimizing its performance for accurate predictions.
• Step 7 Visualize Training and Metrics
• In this module, we will focus on visualizing the training progress and metrics of our model. You will learn how to use graphs and plots to gain insights into model performance and make necessary adjustments for improvement.
• Step 8 Prediction Using the Model
• In this module, we will use our trained regression model to predict house prices. You will see the model in action, applying machine learning principles to real-world data and making accurate predictions.
• Heart Disease Binary Classification Model - Introduction
• In this module, we will introduce the creation of a binary classification model for heart disease prediction. You will learn the importance of such models in healthcare and the steps involved in building one.
• Step 1 - Fetch and Load Data
• In this module, we will guide you through fetching and loading the necessary data for heart disease prediction. You will learn how to prepare the data, setting a solid foundation for building an effective classification model.
• Steps 2 and 3 - EDA and Data Preparation
• In this module, we will delve into exploratory data analysis (EDA) and data preparation for our heart disease classification model. You will learn how to clean and transform the data, ensuring it is ready for model training.
• Step 4 - Defining the Model
• In this module, we will define the architecture of our heart disease classification model. You will learn how to set up the neural network, configuring layers and activations for optimal performance.
• Step 5 - Compile, Fit, and Plot the Model
• In this module, we will compile, fit, and plot our heart disease classification model. You will learn how to train the model and visualize its performance using key metrics and plots.
• Step 5 - Predicting Heart Disease Using Model
• In this module, we will use our trained classification model to predict heart disease. You will see the model in action, applying machine learning principles to healthcare data and making accurate classifications.
• Step 6 - Testing and Evaluating Heart Disease Model
• In this module, we will test and evaluate our heart disease classification model using new data. You will learn how to assess the model’s accuracy and refine it for better performance in predicting heart disease.