You’re using machine learning. Like an invisible web, these human-written algorithms impact the world around you in both obvious and obscure ways. As a data analyst, you might want to add the magic of machine learning to your analytic arsenal. You, too, could be answering new, interesting, and future-facing questions.
In fact, machine learning can deliver benefits to every department in your company since it allows you to recognize patterns in vast assortments of data to predict possible outcomes, allowing leaders to plan and take action. From pattern recognition comes predictive use cases, like customer response modeling, demand and inventory forecasting, and many more applications that drive business performance.
A study released by the World Economic Forum shows that data-related jobs will be the most in demand within the next four to five years, along with AI and machine learning specialists. The job categories that will be the most in demand include data analysts and data scientists; AI and machine learning specialists; software and applications developers and analysts; and big data specialists, although it’s likely these people will have other titles in the coming years.
Why Attend the Bootcamp
- Pre-study course materials
- Live instruction
- Digital courseware
- Daily reinforcement materials
- Catered lunches
- Access to Learning Management System
- 100% Satisfaction Guarantee
- Knowledge Transfer Guarantee
Book the Bootcamp(scroll down)
What will Learn
- Recognized problems can be solved with ML
- Master ML via Python and Scikit Learn and other modules
- Build Powerful Machine Learning Models
- Hands-on Tasks
Topics to be Covered
- Introduction to Machine Learning & Data Wrangling
- Blackbox Introduction to Machine Learning
- Supervised and Unsupervised
- Essential Numpy
- Essential Pandas for Machine Learning
- Linear Models, Trees and Preprocessing
- Linear Model for Regression & Classification
- Preprocessing Techniques using Sci-kit
- Decision Tress
- Model Evaluation, Feature Selection and Pipelining
- Model Selection & Evaluation
- Feature Selection Techniques
- Composite Estimators Using Pipelines & FeatureUnions
- Bayes, Nearest Neighbours & Clustering
- Naïve Bayes
- Nearest Neighbors
- Cluster Analysis
- SVM
- Ensemble Methods
- Case Study
