Machine Learning & AI Hybrid Program
ML and AI are transforming industries because of their ability to perform roles that traditionally required human intervention. These are not just limited to repetitive low-skill jobs but also extend to knowledge industries.
- Batch Starting Date:- 28 Feb to 1st March
- Price : USD $899
- Discounted Price: USD $549
- Valid till 31st Jan
- 50+ Batches conducted in Dubai, Riyadh, Jeddah, Dammam, Singapore & India
- 1000+ Data Science participants from multinational companies across the world
- 29% CAGR of Data science & Analytics professional services in the Middle East and the Asia Pacific by 2020
With Machine Learning & AI
- Classroom + Online
- Learning with real-world business problems
- A thoughtful mix of self-paced and live online sessions
- One-to-one mentorship from industry experts
- Leverage our community of AI enthusiasts and experts to solve your business problems
What will you get
- 22 hours of self-paced video-based learning
- 12 hours live online sessions over 3 months
- 6-month access to Learning Environment for Analytics Professional (LEAP)
What will you learn
- Basics of Python & Python for Machine Learning & AI
- Predictive Modeling: ANN, NLP, & Computer Vision
- Machine Learning Algorithms like kNN, SVM, & Decision Trees & Random Forest
What skills you will build
Chapter 1: Introduction to Python Programming
- Introduction to Python: This module gives a gentle introduction to Python. It also introduces basic features of Anaconda and helps with installation
- Data types: This module covers the most common built-in data types e.g. dictionaries, lists, etc
- Variables: This module introduces variables and how to handle them in Python
- Conditional Statements: In order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. Conditional statements give us this ability This module introduces conditional statements like if-else
- Loops: Introduction to loops, different types of loops, and how to iterate over values in loops — all these topics are covered in this module
- Functions: This module will enable students to automate a sequence of steps using functions
- An object-oriented state of mind: This module introduces the fundamentals of object-oriented programming (OOP) in Python and how to work with classes, objects, etc.
- Our first series of the program: This is an interesting module as it gets the learner started to code on how to download a webpage and how to handle the errors that occur while executing a program.
- Files: This module will enable students to handle different types of files like CSV etc. using Python.
- Database: This module starts with an interesting example of implementing a Bank ATM through code then walks through the database management system and how you can put it to use.
- Introduction to Python Libraries: This module will enable the student to perform data analysis in python by taking them through the most important libraries like Pandas and NumPy
Chapter 2: Introduction to Machine Learning
- This module takes the learner through the overview of Machine Learning program and provides a brief introduction to the course.
- Classification Problems: The different algorithms that can be used for classification problems are being covered in this module, for e.g. Naive Bayes, KNN, SVM, ANN, etc. It covers a case study of survival prediction in titanic case study covering different algorithms for classification problems.
- Clustering: The module describes clustering and its methods. But it mainly focuses on k-Means clustering and walks through a data clustering algorithm-DBSCAN.
- Association Rule Mining: This module gives an introduction of Association Rule learning and how it is being used in many industries especially e-commerce.
- Dimensionality Reduction: Machine learning problems often involve tens of thousands of features, this makes the computation really slow. This module will introduce the dimensionality reduction technique that will enable students to overcome the above-mentioned problem.
- Regression as a form of supervised learning: The module walks the learner through an example of demand forecasting through linear and logistic regression. Also, it explains the tradeoff that a learner needs to keep in mind while training the dataset in supervised learning. It also covers an interesting Melbourne pricing case study.
- Decision Trees: This module explains the decision tree using a case study on Titanic.
- Random Forests: The module explains random forests. How they are different from Decision Trees? Describing Bagging and Bootstrap sampling and cross-validating random forest and decision trees in the Titanic case study.
- Problems of overfitting: This module covers the problems of overfitting and techniques for resolution.
Chapter 3: NLP with Python
- Natural Language Processing: This module will make use of our basic Machine Learning knowledge put to use. It will explain Natural Language Process starting with a rule-based approach. It will also cover the case study of auto summarization text using Natural Language Toolkit, web scraping news article through different algorithms.
- Sentiment Analysis: The module explains what is Sentiment Analysis and why it is important. You will learn about the different approaches to Sentiment Analysis and define sentiment lexicons to develop features to use.
Chapter 4: Recommender Systems
- Recommendation Systems: While you surf Netflix or you are shopping on Amazon, you must have noticed an amazing feature showing you some recommended picks which may turn out to be of your use. Now how do they know what to recommend to whom? This is called as Recommendation Systems. This module will cover the challenges and intricacies behind these systems and implement a movie recommendation system case study.
- Recommendation Techniques: Collaborative Filtering, Content-Based Filtering, Latent Factor Collaborative Filtering.
Chapter 5: Deep Learning
Deep Learning and Computer Vision: This module provides a brief overview of some of the most significant deep learning techniques used in computer vision problem such as handwriting recognition, face detection, etc. It also covers a case study of handwritten digit recognition from the MNIST database.
- A Case Study to implement a Bank ATM
- A Case Study on Survival Prediction
- A Case Study for Auto summarization of Text
- A Case Study to build Movie Recommendation System
- A Case Study for Spam Detection
- A Case Study to build Hand-Digit Recognition System
- A Case Study for Twitter Sentiment Analysis
- A Case Study to build Melbourne House Pricing Prediction System
- A Case Study to build a Database of Stock Movement