Name: Machine Learning Essentials with Python (TTML5506-P)
Availability: InStock
Rating: 4.6 (5878 reviews)

Duration:

24.00 hours

19.5 CPD hours

Overview:

Dive into the fascinating world of AI and Machine Learning with our three-day, comprehensive course, "Machine Learning Essentials with Python". This course, perfect for basic Python developers, equips you with the skills to leverage Python for intelligent applications like data analysis, predictive modeling, automation, and chatbots, transforming your project capabilities. Participants will get hands-on experience with popular machine learning algorithms, exploring their potential applications and limitations.

Description:

Dive into the fascinating world of AI and Machine Learning with our three-day, comprehensive course, "Machine Learning Essentials with Python". This course, perfect for basic Python developers, equips you with the skills to leverage Python for intelligent applications like data analysis, predictive modeling, automation, and chatbots, transforming your project capabilities. Participants will get hands-on experience with popular machine learning algorithms, exploring their potential applications and limitations.
Our highly-experienced instructors will share their practical expertise, guiding you through learning these new skills and
empowering you to confidently apply them in your job or role. Throughout the course you'll explore learning and using
Supervised and Unsupervised Learning techniques, Data Wrangling and Preprocessing, Ensemble Learning, and Model
Evaluation and Validation. Hands-on labs replicating real-world scenarios form a core part of the learning experience, ensuring you acquire practical, applicable skills. Each hands-on lab will provide you with practical experience using innovative skills with cutting edge tools, applied in a practical and meaningful way.
If time permits, you'll also explore innovative technologies such as Generative AI with GPT-4, as well as practical AI integration into applications, highlighting the tools and technologies transforming the AI landscape. By the end of the course, you will not only have gained a deep understanding of AI and Machine Learning concepts but also the ability to apply these in your work context, leading to more complex and impactful projects.

Course 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)

Bonus Chapter: 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
Bonus Chapter: 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
Lab: Integrating a GPT-4 powered feature into a basic Python application
Bonus Chapter: 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

What You'll Learn:

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll learn how to:
- Master the Python Programming for Data Science: Gain an in-depth understanding of Python's role in data science and AI, including proficiency in using key Python data science libraries like Pandas, NumPy, and Matplotlib.
- Understand the Fundamentals of AI and Machine Learning: Develop a strong grasp of AI and Machine Learning concepts, their applications, and how to differentiate between AI, Machine Learning, and Deep Learning.
- Dive into Supervised and Unsupervised Learning Techniques: Acquire hands-on skills to conduct Regression Analysis, Binary Classification, and k-means Clustering - key methods in Supervised and Unsupervised Learning.
- Apply Data Wrangling and Preprocessing Techniques: Learn to handle missing data, outliers, and categorical data
effectively and perform feature scaling and normalization - crucial steps in Machine Learning projects.
- Create and Evaluate Machine Learning Models: Get a grip on the lifecycle of AI projects, including model creation,
evaluation, validation, and the application of Ensemble Learning techniques.
- Understand and implement crucial data preprocessing techniques in Python: Attendees will acquire the ability to handle missing data, outliers, and categorical data, essential for creating reliable machine learning models.
- Develop competency in creating and interpreting data visualizations: Students will learn how to leverage Python's
powerful libraries such as Matplotlib and Seaborn to create compelling visualizations and extract meaningful insights from data.
- Construct a machine learning pipeline for real-world applications: Participants will gain the practical know-how to carry a machine learning project from initial data collection through to final model deployment, using Python.
- (Optional / Bonus Topics): Implement AI into Real-World Applications: By the end of the course, you'll be able to build applications that integrate AI functionalities, using popular Python frameworks and modern AI technologies, like GPT-4.

Available Delivery Options for Machine Learning Essentials with Python (TTML5506-P)

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Machine Learning Essentials with Python (TTML5506-P) provide?

The 3 day Machine Learning Essentials with Python (TTML5506-P) course gives you up to 19.5 CPD hours/structured learning hours.

Who is the correct audience for Machine Learning Essentials with Python (TTML5506-P)?

This intermediate level course is geared for experienced programmers, data analysts, and aspiring data scientists new to AI and machine learning. Basic python experience is required.

Do you provide training for Machine Learning Essentials with Python (TTML5506-P)?

Yes, we provide corporate training, dedicated training and closed classes for Machine Learning Essentials with Python (TTML5506-P). This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Machine Learning Essentials with Python (TTML5506-P)?

The Machine Learning Essentials with Python (TTML5506-P) training takes place over 3 day(s), with each day lasting approximately 24.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Machine Learning

Why are Nexus Human the best provider for Machine Learning Essentials with Python (TTML5506-P)?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Machine Learning Essentials with Python (TTML5506-P)?

Yes, the discount code PENPAL5 is currently available for Machine Learning Essentials with Python (TTML5506-P) training. Only one discount code can be used per booking.

Machine Learning Essentials with Python (TTML5506-P)