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AI and Deep Learning with Python

Batch Price From £360 (approx. $464 USD) View Dates & Prices
Custom Price From £240 (approx. $309 USD) Price Calculator & Booking Short course on Data Science with Python
Total Duration: 12-Hour
Course level: Beginner to Intermediate
Delivery Method: Instructor Led Online Training
Certification: Certificate of Completion will be provided after completing the course

Course Overview

This course is designed to provide students with the knowledge and skills needed to perform Artificial Intelligence (AI) and Machine Learning using Python programming language. Students will learn to apply machine learning and deep learning algorithms using the libraries such as scikit-learn, Kears, and Tensorflow.

Learning Objectives:

  • Understand the fundamental concepts of Artificial Intelligence (AI) and machine learning
  • Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests
  • Understand the basics of deep learning and its applications in Python

Requirements

Basic programming knowledge in Python, familiarity with libraries like Numpy, Pandas, and Matplotlib.

We highly recommend you complete the following course(s) before attending the AI and Deep Learning with Python course:

You may also complete the following course(s) before attending the AI and Deep Learning with Python course but they are not mandatory:

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes
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There is no date for this course at this moment. Please complete the BOOKING REQUEST FORM below or come back to this page again later.

Course Content

  1. Introduction to Machine Learning in Python
    • Overview of machine learning
    • Introduction to Python for machine learning
    • Introduction to Google Colab Notebook
  2. Machine Learning Basics
    • Introduction of Overfitting and Underfitting
    • Class Imbalance and its solution
    • Download the dataset from Kaggle
    • Data splitting and data preparation for the Mchine learning model
    • Machine learning Basics and Types
    • Supervised, Unsupervised, Reinforcement learning with example
  3. Supervised Learning Algorithms
    • Linear regression
    • Logistic regression
    • Decision trees and random forests
    • Project: Wine Quality Predictions
  4. Unsupervised Learning Algorithms
    • K-means clustering
    • Hierarchical clustering
    • Dimensionality reduction
    • Project: Iris Classification
  5. Model Evaluation and Selection
    • Model evaluation metrics
    • Cross-validation
    • Model selection techniques
    • Ensemble Model
    • Model save and load
  6. Deep Learning with Keras and TensorFlow
    • Introduction to deep learning
    • Neural networks
    • Keras and TensorFlow
  7. Convolutional Neural Networks (CNN)
    • CNN basics
    • CNN implementation using Keras
    • Project: Cats and Dogs Classification
  8. Recurrent Neural Networks (RNN)
    • RNN basics
    • RNN implementation using Keras
    • Project: Sentiment analysis using text data
  9. Real-world Examples
    • Some real-life business applications of Machine learning
    • Q&A and course feedback

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