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Machine Learning Essentials with Python
Description
Machine Learning Essentials with Python Introduction
Embark on a transformative three-day journey with our “Machine Learning Essentials with Python” course, designed to elevate your proficiency in artificial intelligence and machine learning using Python. This course is ideal for individuals with a foundational knowledge of Python who aspire to delve into applications such as data analysis, predictive modeling, and the creation of advanced AI-driven systems, including sophisticated chatbots.
Our curriculum includes Data Wrangling and Preprocessing to structure raw data effectively, Ensemble Learning for model accuracy enhancement, and a special segment on Generative AI using GPT-4. Through hands-on labs that mimic real-world scenarios, you will gain both theoretical knowledge and practical skills, enabling you to apply these cutting-edge techniques in your professional roles.
Throughout this comprehensive course, you will explore the nuances of AI and machine learning, developing both the technical skills and the creative approaches necessary for high-impact projects. From Model Evaluation and Validation to practical AI integration strategies, our expert instructors will guide you through each step, ensuring you not only understand the concepts but are also ready to implement them.
This course will not only broaden your skill set but also position you at the forefront of technological innovation, ready to tackle more challenging projects in the tech industry.
Machine Learning Essentials With Python Course Objectives
Our course structure combines engaging instructor-led presentations, insightful demonstrations, valuable hands-on labs, and interactive group activities. Here’s what you’ll achieve:
- Master Python for Data Science: Gain in-depth knowledge of Python’s data science capabilities using libraries like Pandas, NumPy, and Matplotlib.
- Foundational AI and Machine Learning Concepts: Understand the core principles of AI and machine learning, their applications, and how they differ from deep learning.
- Techniques in Supervised and Unsupervised Learning: Develop skills in Regression Analysis, Binary Classification, and k-means Clustering.
- Advanced Data Handling Techniques: Learn essential preprocessing tasks such as handling missing data, outliers, and feature normalization.
- Model Development and Evaluation: Create, evaluate, and validate machine learning models; apply Ensemble Learning for better accuracy.
- Data Visualization Skills: Use Python libraries to create visualizations that help interpret data effectively.
- Real-World Machine Learning Pipeline: Construct end-to-end machine learning workflows from data collection to model deployment.
- AI Integration into Applications: Explore how to integrate AI functionalities into real-world applications, including the use of GPT-4.
Prerequisites
To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:
- Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and Numpy, etc.)
- Basic Math and Problem-Solving Skills
- Understanding of Basic Data Structures
Audience
- This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into AI and Machine Learning.
- It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on understanding of AI’s impact on product development and business strategy.
Machine Learning Essentials with Python Outline
Python for Data Science Quick Refresher
- Review and application of Python basics
- Relevance of Python in Data Science
- Exploring Python data science libraries: Pandas, NumPy, Matplotlib
- Introduction to Jupyter Notebook, Anaconda
- Lab: Solving basic data science problems using Python
Introduction to AI and Machine Learning
- Understanding the foundations and significance of AI and Machine Learning
- Differentiating between AI, Machine Learning, and Deep Learning
- Overview of the business applications of AI and Machine Learning
- Exploring types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Deep dive into common Machine Learning algorithms
- Introduction to TensorFlow and PyTorch
- Lab: Exploring Python libraries for Machine Learning
Supervised Learning: Regression and Classification
- Understanding Simple Linear, Multiple Regression, and Binary Classification
- Understanding the business context in Binary Classification
- Lab: Conducting Regression Analysis and Classification using Python
Unsupervised Learning: Introduction to Clustering
- Understanding the concept of Clustering in Unsupervised Learning
- Diving deep into k-means clustering algorithm
- Lab: Implementing k-means Clustering
Data Wrangling and Preprocessing Techniques
- Understanding the importance of data wrangling and preprocessing in Machine Learning
- Techniques for handling missing data, outliers, and categorical data
- Feature scaling and normalization techniques
- Lab: Applying data preprocessing techniques on a dataset
Practical Machine Learning Project Walkthrough
- Gaining insights into the lifecycle of AI projects in the industry
- Common challenges in implementing AI projects and solutions
- Step-by-step walkthrough of a real-life AI project from end-to-end
- Lab: Implementing a small-scale machine learning project
Model Evaluation and Validation
- Understanding model assessment metrics for both Regression and Classification
- Learning to split data for model training and testing
- Lab: Evaluating model performance on test data
Introduction to Ensemble Learning
- Learning the concept of Ensemble Learning and its importance
- Understanding simple methods for Ensemble Learning
- Lab: Implementing simple Ensemble Learning techniques
Explainable AI and Ethical Considerations in AI
- Understanding the importance of interpretability in Machine Learning
- Exploring techniques for making AI transparent
- Discussing ethical considerations in AI and ML
- Lab: Visualizing Feature Importance in a model
Introduction to Neural Networks
- Grasping the basics of Neural Networks
- Learning about Feedforward and Backpropagation processes
- Lab: Building a basic Neural Network with Python
Data Visualization Techniques with Python
- Understanding the importance of data visualization in Machine Learning
- Exploring Python libraries for data visualization: Matplotlib, Seaborn
- Lab: Visualizing datasets using various plots
Machine Learning Pipeline and Model Deployment
- Understanding the concept of ML pipeline: Data collection, Preprocessing, Modeling, Evaluation, Deployment
- Lab: Creating a simple Machine Learning pipeline
Bonus Chapters / Time Permitting (or Day Four)
Exploring Generative AI with GPT-4
- Understand Generative AI and how it powers GPT-4, using Python for interacting with these models
- Learn about the evolution of GPT models, and the specific advancements of GPT-4 in handling complex Python programming tasks
- Understand the potential applications of GPT-4 and how to implement them using Python
- Discuss the ethical considerations and Python coding practices for using powerful models like GPT-4 responsibly
- Lab: Creating a conversational bot using GPT-4 with Python
Basics of Integrating AI into Applications
- Understand the concept of AI integration into simple applications
- Learn about the role of APIs in leveraging AI capabilities in applications
- Explore how Python can be used to connect applications to AI functionalities
- Discuss various simple AI plugins and extensions that can be integrated using Python
- Lab: Building a basic application integrating a pre-trained AI model
Integrating AI into Web Applications
- Understand the concept of AI integration into web applications
- Learn about the Flask and Django frameworks for Python web development
- Discuss the role of APIs in leveraging AI capabilities in web applications
- Explore various AI plugins and extensions for web development
- Lab: Integrating a GPT-4 powered chatbot into a web application
$2195.00
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3 Days Course |