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).
