Machine Learning & AI Bootcamp
Advance your career with a certification in Machine Learning, Deep Learning, and Artificial Intelligence. Build the expertise to solve real-world business problems from scratch using Machine Learning and AI techniques with Python.
- Batch Starting Date:- 28 Feb to 1st March
- Price : USD $899
- Discounted Price: USD $549
- Valid till 31st Jan
- 15+ Batches conducted in Dubai for Data Science and Machine Learning.
- 200+ Participants trained from multi-national companies across the world
- $13T The potential reach of the AI market by 2030
With Machine Learning & AI
- Classroom + Online
- Mix of in-person and live online sessions supported by self-paced content
- 2 months of Post-Bootcamp Program with live online sessions
- One-to-one mentorship from industry experts
- Become a part of our Machine Learning & AI community
- Hands-on training with 3 real-life case studies with 3 Capstone projects
What will you get
- 30 Hours of Bootcamp (classroom training) in your city
- 2 live online sessions for Q&A, case studies, assignments and capstone projects
- One-to-one session on mentorship and career design with industry experts
What will you learn
- Basics of Python for Machine Learning & AI
- AI techniques like Artificial Neural Networks, Natural Language Processing, and Computer Vision
- Machine Learning Algorithms like kNN, Support Vector Machines, Decision Trees & Random Forest
What skills you will build
Chapter 1: Introduction to Machine Learning
- What is Machine Learning?
- Machine Learning in action
- Applications and industry case studies
- Understanding of statistical and Machine Learning techniques
- Comparison of popular Machine Learning tools
- A glimpse on career opportunities
Chapter 2: Python Programming
- Fundamentals: Python overview. Installation. Packages and walkthrough. Operators, Data Types, Conditional Statements.
- Control Flow Basics: For loops. While loops. Nested loops. The disadvantage of loops. Alternatives to loops.
- Functions: Understand the structure of functions. Build your own function. Usage of parameters and default values. Anonymous functions.
- NumPy: Scientific computing with Python. Importance of NumPy. Array Creation. Data Types. Unitary Operations. Shape Manipulation. Reshape, Transpose, - Ravel Array Indexing. Boolean Indexing. Broadcasting. Universal Functions. Matrix Multiplication. Statistical Methods. Stacking. Splitting Copies and Views.
- Pandas: Pandas Data Structures - Series and Data Frame. Basic functions on Data Frame. Indexing and Selecting Data. Group By: Split-Apply-Combine. Handling Missing Data. Merging Multiple Datasets. Data Analysis Scenarios. Data Manipulation / Data Cleaning with different types of data inputs like CSV, JSON, etc.
Chapter 3: Predictive Modelling and Data Visualisation
- Time series forecasting: Converting Series to Time Series Handling Invalid Data. Epoch/Date-Time Index. Indexing. Time / Date Components. Period and Period Index. Handling Time Zones. Parsing and Manipulating Dates.
- Regression: Simple Linear Regression. Multiple Regression. Logistic Regression. Naive Bayes, Understanding of kNN concepts.
- Data Visualization: Matplotlib Package. Simple Plot with X and Y Axis. Line styles and Color. Multiple Lines on Same Plot Controlling Line Properties. Adding Labels, Gridlines, Annotations X and Y Ticks and Rotations. Splines. Legends. Working with Multiple Figures and Axes Share X and Y Axis. Adding Subplots. Creating Different Types of Plots Line Graphs - Bar Plots, Histograms, Box Plot, Stacked Plot, Scatter Plot, Pie Chart.
Chapter 4: Machine Learning
- Introduction to Machine Learning: How do machines learn? Choosing a Machine Learning algorithm. Using R for Machine Learning.
- Decision Trees and Random Forest: Understanding decision trees. Understanding classification rules. Understanding Random Forest. Modeling using Random Forest.
- Classification using Nearest Neighbours: Understanding classification using nearest neighbors. The kNN algorithm - Calculating distance, Choosing an appropriate k, Preparing data for use with kNN.
- Neural Networks: Understanding neural networks. Activation functions. Network topology. Training neural networks with backpropagation.
- Text Mining and Sentiment Analysis: Concepts and components of text mining. Text mining tasks and approaches. Access twitter data. Build a frequent term network. Topic Modeling. Analysis of followers and retweets. Understand sentiment analysis and its key concepts. Sentiment polarity. Opinion summarization. Feature extraction.
Case study on Loan Default
We all know that banks make money by lending. There are cases where the borrower defaults on loan i.e. they do not repay the loan. There is an enormous pressure on banks to recover the dues and bring Non Performing Assets under control. It would be really helpful for the bank if they know up front, who will default on the loan.
In this case study, you will help CNN Amro, a leading bank in south-east Asia to build a system that can predict loan defaulters in advance.
Case Study on customer analytics
In the present era of cut-throat competition, the customer expects personalized services. Also, not all customers are the same, customers can vary basis several parameters like demographics, price sensitivity, etc. These parameters should be considered while designing marketing campaigns.
In this case study, you will help KT insurance to segment customers, who have bought vehicle insurance, basis features like demographics, vehicle details, driving experience, etc.
Sentiment analysis on Twitter
Sentiment analysis helps allows us to gain an overview of the wider public opinion behind certain topics of interest. The applications of sentiment analysis are broad and powerful. The ability to extract insights from Twitter data is a practice that is being widely adopted by companies around the world.
In this case study, you will perform sentiment analysis on live twitter data based on your area of interest
Diagnose breast cancer
Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018.
Humanity internationals, an advanced breast cancer research institute has collected medical records of patients suffering from breast cancer. Using the data it wants to build a breast cancer prediction system that can diagnose breast cancer at an early stage.
Churn is a very common problem in the telecom industry. Customers have an option to choose from a variety of plans from multiple Telecommunication service providers (TSP). Any point of time, they believe the other TSP is providing better services and plans than their existing TSP, they prefer to opt for the other TSP.
In this case study, you will help Botaphone, a leading TSP of United Arab Emirates in designing a churn prediction system that provides lists of potential churners.
Human Face Detector
FCICT bank has several ATMs installed at various location. Currently, it is facing several security issues at many of its locations. To solve the problem, it wants to detect how many individuals are present near the ATM machine. To help FCICT bank, you have to buildp a face detection and counter system that can detect the faces and also can count the number of faces so as to help FCICT to know the number of people near the ATM machine.
In this case study, you will built a face detector and counter system for FCICT bank