Choose Artificial Intelligence & Machine Learning Course in Malaysia
Completely project-based Artificial Intelligence & Machine Learning Course.
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Artificial Intelligence
& Machine Learning Course
Mdec Digital Up Initiative 2023
30.09.2023
RM 4200
RM 3000
(After MDEC subsidy: RM 500)
The participants must pay to Nexperts Academy Sdn Bhd RM 3000 first. Once training completed, MDEC will reimburse to participant’s account RM 2500 within 30.09.2023.
How to Apply
Be Trained & Claim 95% Fee before 30.09.2023
- Open to Malaysians, Employed, Unemployed, Fresh Graduates, OR Gig workers aged 21-55 years old.
- Be trained in any of our courses listed in the directory from 1 March 2023 – 30 September 2023.
- You can get reimbursement of up to RM2,500.00 on the training fee. You are required to submit documentation for the claims.
- Subject to availability and on a first come first served basis.
How to apply for Incentive
Step 1
Complimentary 30 hours on fundamentals of machine learning full course video
Step 2
Register for the course
Step 3
Make payment of the Training Fee
RM 4200- RM 3000
Step 4
Attend training (Live Online / Classroom)
Step 5
Submit Application to obtain reimbursement of training fee of RM2,500 from
https://platform.mdec.com.my/auth/signup
Step 6
Upon receipt of approval by Mdec you will receive the incentive amount within 30 days.
Let’s dive in about the most interesting part of this course!
Artificial Intelligence and Machine Learning Overview
✔ AI is projected to become the most sought-after job in the near future, presenting promising career prospects for IT professionals.
✔ According to IDC, spending on products and services incorporating Augmented Reality (AR) and Virtual Reality (VR) concepts is expected to grow significantly, reaching nearly 215 billion by 2021.
✔ The rise in demand for AI professionals is a positive sign for individuals aspiring to build a career in this field.
✔ The course offered covers the foundational aspects of modern AI and explores various applications of AI.
✔ Students will gain in-depth knowledge of essential AI concepts, including heuristic search and genetic programming.
✔ The curriculum focuses on practical aspects like game development and building intelligent applications to address real-world challenges in organizations and businesses.
✔ The course aims to ignite enthusiasm among students about the vast array of applications and opportunities in the AI field, pushing the boundaries of human capabilities.
✔ The rapid expansion of AI in various industries continues to open up new possibilities and avenues for innovation.
✔ By completing this course, students can equip themselves with the skills and expertise necessary to tap into the growing AI job market and contribute to the advancement of AI technology.
✔ Overall, the course prepares students to explore and embrace the limitless potential of AI and its impact on the future.
Here are the best part of what will you get from this course
✔ We will build AI Product as part of our project
✔ We will solve real world problems with AI
✔ Build machine learning models that can do prediction
✔ Master AI models
✔ Complimentary 30 hours on fundamentals of deep learning full course video
✔ Complimentary 30 hours on fundamentals of machine learning full course video
Course Module’s Outline
Foundations of AI
Learning Objectives
Learn how to build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Develop expertise in popular AI & ML technologies and problem-solving methodologies. Also develop the ability to independently solve business problems using Artificial Intelligence & Machine Learning.
Topics Covered:
• Write Python code to analyze, manipulate and visualize data
• Learn to implement statistical techniques with Microsoft Excel
• Write Python code using Python library – matplotlib, seaborn to visualize data and represent it graphically
• Conduct exploratory data analysis in python to identify potential revenue maximization opportunities and also visualize data
Machine Learning – Supervised Learning
Learning Objectives:
Learn about supervised learning techniques – regression and classification. Also understand various techniques to build Decision Trees.
Topics Covered:
• Regression (Linear, Multiple and Logistic)
• Classification (K-NN, Naive Bayes) Techniques
• Decision Trees
• Case Study
Hands On:
✔ This dataset classifies people described by a set of attributes as good or bad credit risks. Using classification techniques, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
Machine Learning – Unsupervised Learning
Learning Objectives:
Learn about unsupervised learning technique – K-Means Clustering and Hierarchical Clustering. Also understand the Elbow method and Silhouette method.
Topics Covered:
• K-means Clustering
• Hierarchical Clustering
• High-dimensional Clustering
• Case Study
Hands On:
✔ In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Machine Learning – Ensemble Techniques
Learning Objectives:
Learn about bootstrap sampling and its advantages followed by bagging. Boost model performance with Boosting. Through a real-life case study, learn Random Forest and how it helps avoid overfitting compared to decision trees.
Topics Covered:
• Boosting
• Bagging
• Random Forest
• Case Study
Hands On:
✔ In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform better than a single model.
Machine Learning – Reinforcement Learning
Learning Objectives:
Understand the basics of RL and its applications in AI. Get an understanding of Markov Decision Processes: Model processes as Markov chains, and learn algorithms for solving optimisation problems. Write Q-learning algorithms to solve complex RL problems.
Topics Covered:
• Value based methods
• Q-learning
• Policy-based methods
Hands On:
✔ In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Deep Learning
Learning Objectives:
• Learn advanced machine learning techniques using the Neural Networks algorithms. Neural Networks can enable pattern recognition based on a large amount of inputs. Learn how NN algorithms work, and end up with an introduction to deep learning.
• This module covers various activation functions like sigmoid, hyperbolic-tangent, Rectified Linear Units, Leaky Rectified Linear Units.
Topics Covered:
• Neural Network Basics
• Deep Neural Networks
• TensorFlow using Neural Networks & Deep Learning
• Case Study
Hands On:
✔ A research study was aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients.
Natura Language Processing
Learning Objectives:
Get started with the Natural language toolkit, and learn the basics of text processing in Python. Learn how to extract features from unstructured text and build machine learning models on text data. Conduct sentiment analysis, learn to parse English sentences and extract meaning from them. Explore the applications of text analytics in new areas and various business domains.
Topics Covered:
• Statistical NLP and text similarity
• Text Summarization
• Syntax and Parsing techniques
• Semantics and Generation
• Case Study
Hands On:
✔ Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.
Computer Visions
Learning Objectives:
Learn to use the power of computer vision and play with what you see, detect faces, eyes and other attributes using Haar cascades.
Topics Covered:
• Convolutional Neural Networks
• Keras Library for Deep Learning in Python
• Pre-processing Image Data
• Object and face recognition using OpenCV
• Case Study
Hands On:
✔ While we drive on a highway, we tend to feel sleepy. In this project, using OpenCV and implementing object detection and feature extraction we detect fatigue in real-time and report an alarm which will not only keep a driver attentive while driving but also reduce the number of accidents.
Intelligent Agents
Learning Objectives:
Learn the AI search technique that employs heuristic for its moves. Understand the fundamental concepts of genetic algorithms and visualize the evolution.
Topics Covered:
• Uniform and heuristic-based search techniques
• Planning and constraint satisfaction techniques
• Adversarial search and its uses
• Case Study
Hands On:
✔ Use cutting edge AI techniques to teach a computer to play a computer game.
What you will learn
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Flexible schedule
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Check out what our students say
“Honestly i really enjoyed learning in this Center. and i will always suggest this Training center to everyone in this world. thank your for sharing your knowledge to us Mr. Vaheed sir and Good luck to all”
Alphadio kouyatè
Mr Yash provide good materials that are very easy to understand. Especially for beginner, the explanation by Mr Yash really help me to do some python exercise, and he also always fast at answering my questions. I enjoy this program and would recommend for beginner.
Viviana Edora
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