The Introduction of AI and Machine Learning with Python

Learn Data Science, Machine Learning (Artificial Intelligence), Deep Learning & more from the absolute basics!

Dive into the concept of Artificial Intelligence and Machine Learning (ML) and learn how to implement advanced algorithms to solve real-world problems. This course will teach you the workflow of ML projects from data pre-processing to advanced model design and testing.

What you’ll learn

  • Define and understand the meaning of AI and machine learning and explore their applications.
  • Handling Data Frames by learning various tasks including (data exploration, visualization and cleaning).
  • Understand and create various Supervised Learning algorithms.
  • Understand and create various Unsupervised Learning algorithms.
  • Understand and build recommendation systems.
  • Understand and create NLP (Natural Language Processing) systems.
  • Define and understand Deep Learning in computer vision.

Course Content

  • Course introduction –> 1 lecture • 1min.
  • Introduction to AI –> 7 lectures • 21min.
  • Understanding AI –> 7 lectures • 25min.
  • Introduction to Data –> 7 lectures • 23min.
  • Machine Learning –> 10 lectures • 29min.
  • Supervised Learning – Regression –> 7 lectures • 28min.
  • Supervised Learning – Binary Classification –> 7 lectures • 32min.
  • Supervised Learning – Multi-class Classification –> 8 lectures • 23min.
  • Unsupervised Learning – Clustering –> 7 lectures • 24min.
  • Unsupervised Learning – Customer Segmentation –> 6 lectures • 14min.
  • Unsupervised Learning – Association Rule Mining –> 7 lectures • 20min.
  • Recommendation System – Content Based –> 7 lectures • 17min.
  • Recommendation System – Collaborative Filtering –> 7 lectures • 19min.
  • Natural Language Processing – Sentiment Analysis –> 7 lectures • 16min.
  • Deep Learning – Computer Vision –> 7 lectures • 28min.
  • Object Recognition –> 4 lectures • 6min.
  • Final Assessment –> 0 lectures • 0min.

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  • Previous programming knowledge. Python recommended..

Dive into the concept of Artificial Intelligence and Machine Learning (ML) and learn how to implement advanced algorithms to solve real-world problems. This course will teach you the workflow of ML projects from data pre-processing to advanced model design and testing.


By the end of the course the students will be able to:

– Build a variety of AI systems and models.

– Determine the framework in which AI may function, including interactions with users and environments.

– Extract information from text automatically using concepts and methods from natural language processing (NLP).

– Implement deep learning models in Python using TensorFlow and Keras and train them with real-world datasets.


Detailed course outline:

Introduction to AI

. Introduction to AI and Machine Learning.

. Overview on Fields of AI:

. Computer Vision.

. Natural Language Processing (NLP).

. Recommendation Systems.

. Robotics.

. Project: Creation of Chatbot using traditional programming (Python revision).


Understanding AI

· Understanding how AI works.

· Overview of Machine Learning and Deep Learning.

· Workflow of AI Projects.

· Differentiating arguments vs parameters.

· Project: Implementing functions using python programming (Python revision).


Introduction to Data Science

· Introduction to Data Science.

· Types of Data.

· Overview of DataFrame.

· Project: Handling DataFrame using python programming by learning various tasks including:

. Importing Dataset

. Data Exploration

. Data Visualization

. Data Cleaning


Machine Learning

· Overview on Machine Learning Algorithms with examples.

· Types of Machine Learning:

. Supervised

. Unsupervised

. Reinforcement

· Types of Supervised Learning:

. Classification

. Regression

· Project: Training and deploying machine learning model to predict salary of future candidates using python programming.


Supervised Learning – Regression

· Understanding Boxplot and features of Boxplot function.

· Understanding Training and Testing Data with train_test_split function.

· Project: Creating a machine learning model to solve a regression problem of predicting weight by training and testing data using python programming.


Supervised Learning – Binary Classification

· Understanding Binary Classification problems.

· Overview on Decision tree Algorithm.

· Overview on Random Forest Algorithm.

· Use of Confusion Matrix to check performance of the classification model.

· Project: Implementing Decision tree and Random forest algorithm using python programming to train a classification model to predict diabetic patients, and using confusion matrix to check performance of both algorithms.


Supervised Learning – Multi-class Classification

· Understanding Multi-class Classification problems.

· One-vs-One method.

· One-vs-Many method.

· Project: Implementing Logistic Regression algorithm with both One-vs-One and One-vs-Rest approach to solve a multi-class classification problem of Iris flower prediction. Also, evaluating performance of both approaches using confusion matrix.


Unsupervised Learning – Clustering

· Understanding Unsupervised Learning.

· Use of Unsupervised learning.

· Types of Unsupervised learning:

. Clustering

. Association

· Working of KMeans Algorithm.

· Use of Elbow method to determine K value.

· Project: Standardising the data and implementing KMeans algorithm to form clusters in the dataset using python programming.


Unsupervised Learning – Customer Segmentation

· Understanding Customer Segmentation.

· Types of characteristics used for segmentation.

· Concept of Targeting.

· Project: Implementing KMeans algorithm to segment customers into different clusters and analysing the clusters to find the appropriate target customers.


Unsupervised Learning – Association Rule Mining.

· Understanding Association problems.

· Market Basket Analysis.

· Working of Apriori Algorithm.

· Key metrics to evaluate association rules:

. Support

. Confidence

. Lift

· Steps involved in finding Association Rules.

· Project: Implement Apriori algorithm to generate association rules for Market Basket Analysis using python programming.


Recommendation System – Content-Based

· Understanding Recommendation Systems.

· Working of Recommendation Systems.

· Types of Recommendation Systems:

. Content-based

. Collaborative

· Project: Building a content-based recommendation system using K Nearest Neighbour(KNN) algorithm to recommend a car to the customer based on their input of preferred car features.


Recommendation System – Collaborative Filtering

· Understanding Collaborative filtering technique.

· Types of approaches in collaborative filtering:

. User-based

. Item-based

· Project: Building a movie recommendation system using item-based collaborative filtering based on data from a movie rating matrix.


Natural Language Processing – Sentiment Analysis

· Natural Language Processing (NLP)

· Applications of NLP

· Fundamental NLP tasks.

· Tokenization

· Project: Creating a machine learning model that can predict the sentiment in a sentence (Application of NLP).


Deep Learning – Computer Vision

· Understanding Deep Learning.

· Neural Networks and Deep Neural Networks.

· Image Processing

· Project: A neural network model is created for image recognition purposes to predict the digit written in images of hand-written digits.


Image Classification- Bonus Class

· Learn about pre-trained models.

· ResNet50 model trained using ImageNet data.

· Project: Use ResNet50 model to classify images (predicting what the image represents).

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