Machine Learning
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MobiGnosis’ Machine Learning training program will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. You will gain in-depth knowledge on all the concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, etc. through real-time industry use cases, and this will help you in clearing the Machine Learning Certification Exam.
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
- WHAT YOU WILL LEARN
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Key Learnings :
- Describe Machine Learning(ML) and its concepts
- Gain insights into the roles played by a Machine Learning Engineer
- Discuss various ML algorithms and their implementation
- Validate ML algorithms
- Introduction to Data Science
- Introduction to Python
- Iterative Operations & Functions in Python
- Data summary & visualization in Python
- Data Handling in Python using NumPy & Pandas
- Generalised Linear Models in Python
- Tree Models using Python
- Boosting Algorithms using Python
- Machine Learning Basics
- Support Vector Machines (SVM) & kNN in Python
- Unsupervised learning in Python
- Text Mining in Python
- Version Control using Git and Interactive Data Products
- Practice Test & Interview Questions
- COURSE CURRICULUM
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Topics Covered During Classroom :
1. Introduction to Machine Learning
- Introduction to Big Data and Machine Learning
2: Walking with Python or R
- Understanding Python or R
3. Machine Learning Techniques
- Types of Learning
- Supervised Learning
- Unsupervised Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
4. Supervised Learning
- Regression
- Classification
5. Supervised Learning – Regression
- Predicting house prices: A case study in Regression
- Linear Regression & Logistic: A Model-Based Approach
- Regression fundamentals : Data and Models
- Feature selection in Model building
- Evaluating over fitting via training/test split
- Training/ Test curves
- Adding other features
- Regression ML block diagram
6. Supervised Learning – Classification
- Analyzing the sentiment of reviews: A case study in Classification
- Classification fundamentals : Data and Models
- Understanding Decision Trees and Naive Bayes
- Feature selection in Model building
- Linear classifiers
- Decision boundaries
- Training and evaluating a classifier
- False positives, false negatives, and confusion matrices
- Classification ML block diagram
7. Unsupervised Learning
- Clustering
- Recommendation
- Deep Learning
8. Unsupervised Learning – Clustering
- Document retrieval: A case study in clustering and measuring similarity
- Clustering System Overview
- Clustering fundamentals : Data and Models
- Feature selection in Model building
- Prioritizing important words with tf-idf
- Clustering and similarity ML block diagram
9. Unsupervised Learning – Recommendation
- Recommending Products
- Recommender systems overview
- Collaborative filtering
- Understanding Collaborative Filtering and Support Vector Machine
- Effect of popular items
- Normalizing co-occurrence matrices and leveraging purchase histories
- The matrix completion task
- Recommendations from known user/item features
- Recommender systems ML block diagram
10. Unsupervised Learning Deep Learning
- Deep Learning: Searching for Images
- Searching for images: A case study in deep learning
- Learning very non-linear features with neural networks
- Application of deep learning to computer vision
- Deep learning performance
- Demo of deep learning model on ImageNet data
- Deep learning ML block diagram
11. Spark Core and MLLib
- Spark Core
- Spark Architecture
- Working with RDDs
- Machine learning with Spark Mllib
Training Duration & Pricing:
For Individuals
Duration: 1.5 Months and we also offer 2 Months Offline Support
Mode: Classroom & Online
Course Fees: Call us at +91-9900001329
For Corporate Training
The Mobignosis Corporate Training Program is designed for organisations who require practical upskilling for their employees to gain knowledge on the current trending technologies
Machine Learning
CERTIFICATION
Candidates receive Mobignosis course completion certificate upon successful completion of course
FAQs
The course is an instructor led classroom/online coaching session
The instructors are industry experts (Machine Learning Professionals) who consult with leaders in technology services like SAP, Capgemini, Cisco and many others
As a team of practicing Machine Learning professionals, we use the leading edge methodologies in our consulting work and have used the same methodologies to develop the Machine Learning course content for classroom coaching. So, you are exposed to the most up to date quality course contents
The Machine Learning Training program includes 2 months free technical support post training, the participants can repeat the session free of cost, For any additional assistance we are just a phone call away