AI & Machine Learning with Python

Our comprehensive course is meticulously crafted to furnish students with the essential knowledge and practical skills required to excel in the realm of Artificial Intelligence (AI) and Machine Learning, utilizing the Python programming language. Through a blend of theoretical insights and hands-on exercises, participants will delve into the intricacies of AI and Machine Learning algorithms, empowering them to tackle real-world challenges with confidence.

Throughout the duration of the course, students will embark on a journey to master the application of various machine learning and deep learning algorithms. With a focus on practical implementation, participants will gain proficiency in leveraging industry-standard libraries such as scikit-learn, Keras, and TensorFlow. These libraries are indispensable tools in the arsenal of any aspiring data scientist or AI practitioner, enabling the seamless development and deployment of cutting-edge AI solutions.

• Grasp the foundational principles of Artificial Intelligence (AI) and machine learning.
• Construct predictive models utilizing a variety of machine learning algorithms, including linear regression, logistic regression, decision trees, and random forests.
• Gain insight into the fundamentals of deep learning and its practical applications in Python.

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

Our aim is not just to get you the qualification but also guide you through all processes while you are applying for your SIA Licence. Following are some of the reasons why you should choose us as your training provider:

• Excellent Success Rate!
• Fully accredited and authorized training center in London!
• Most competitive prices with NO HIDDEN COSTS!
• Prices are all inclusive of tuition, handbook, notebook, exams and certificate costs!
• Nationally recognized and Ofqual accredited training course!
• Highly skilled and qualified trainers with a great wealth of practical knowledge & experience!
• Post-qualification email support while you are preparing to book your licence application!
• Free guidance session on how to apply and obtain a SIA Licence!