Machine Learning Foundation

Machine Learning is a method of getting computers to act independently without being explicitly programmed. The Machine Learning course in Dubai also covers topics of supervised and unsupervised learning, as well as the best practices in Machine Learning (bias/variation theory; the process of innovation in machine learning and artificial intelligence).

ELIGIBILITY: The Machine Learning Foundation course is most suitable for professionals seeking to gain knowledge on the fundamentals of ML. There are no mandatory prerequisites for this Machine Learning course in Dubai; however, familiarity with statistics and basic programming knowledge is recommended

CAREER OPPORTUNITIES:

Market Trends: Technological advancements and the increase in data generation have led to the expansion of the Machine Learning (ML) market. Market analysts anticipate a market growth of ML to reach $ 8.81 billion by 2022, representing a 44.1% year – over – year increase. Job opportunities for ML professionals in Dubai are set to expand as Machine Learning job trends impact every industry.

Salary Trends: Machine Learning jobs are among the highest paying in the world today, with salaries for Machine Learning Engineers increasing by 5.8% in 2019. Due to the high demand for Machine Learning Dubai jobs, the compensation and benefits for the experts in this field have been elevated by manifolds. Machine Learning Engineers in the UAE with less than 3 years of experience earn an average salary of AED 221,314 annually.

Demand and Opportunities: Machine Learning expertise has emerged as the most sought – after technical skill in Artificial Intelligence (AI) jobs. ML jobs are expected to be valued at almost $31 billion by 2024 with an annual growth rate of more than 40%. In the past 4 years, ML jobs have grown by almost 75%, boosting the demand for professionals with Machine Learning expertise.

COURSE OUTLINE:

Machine Learning Introduction

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

Machine Learning Algorithm

• Popular Machine Learning Algorithms
• Clustering, Classification, and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm: Train, Evaluation, and Testing.
• How to choose Machine Learning Algorithm

Supervised Learning I

• Simple Linear Regression: Theory, Implementing in Python (and R), Working on the use case
• Multiple Linear Regression: Theory, Implementing in Python (and R), Working on the use case
• K – Nearest Neighbors: Theory, Implementing in Python (and R), KNN advantages, Working on the use case
• Decision Trees: Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on the use case
• Random Forests: Theory, Implementing in Python (and R), Reliability of Random Forests, Working on the use case

Supervised Learning II

• Naive Bayes Classifier: Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on the use case
• Support Vector Machines: Theory, Support vector machines with Python and R, Improving the performance with Kernels, Working on the use case
• Association Rules: Theory, Implementing in Python (and R), Working on the use case
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models

Unsupervised Learning II

• K – Means Clustering: Theory, Euclidean Distance method.
• K – Means hands – on with Python (and R)
• K – Means Advantages & Disadvantages
• Hierarchical Clustering: Theory
• Hierarchical Clustering with Python (and R)
• Hierarchical Advantages & Disadvantages

Dimensionality Reduction

• Dimensionality Reduction: Feature Extraction & Selection
• Principal Component Analysis (PCA): Theory, Eigen Vectors
• PCA example with Python (and R) with use case
• Advantages of Dimensionality Reduction
• Application of Dimensionality Reduction with a case study
• Collaborative Filtering & Its Challenges

KEY TAKEAWAYS:

After completing our Machine Learning Foundation course in Dubai will enable a candidate to:

• Become familiar with Machine Learning concepts
• Develop a deep understanding of the fundamentals of Machine Learning and Statistical Modeling
• Incorporate Machine Learning into practice
• Familiarize with Supervised and Unsupervised Learning
• Employ the concepts of Machine Learning at the workplace

ACCREDITATION:

Our Machine Learning Foundation course is accredited by ISO 9001: 2015 and the Knowledge and Human Development Authority (KHDA).

ALUMNI: Our academy graduates are successfully placed in some of the top – notch companies in Dubai like Jumeirah, Aramex, Airarabia, Damac, Emaar, etc. They are entrepreneurs, career changers, and lifelong learners.

WHY LEARNER’S POINT:

Being the leading Machine Learning course provider in Dubai, we have substantial experience in teaching relevant skills and knowledge.

Our Machine Learning course offers the following advantages:

• Our Machine Learning course is designed based on real – world scenarios, formulating and evaluating practical requirements
• Our courses strive to provide a reality check and insight into the requirements of a challenging role and how to fulfill it
• We also provide live interactive sessions and hands – on experience through assignments associated with the actual work environment
• To enhance the learning experience of our students we provide organized group sessions and integrated training modules
• Our discrete approach to career guidance enables our students to get successfully placed in renowned organizations