Machine Learning with Python Training in Abu Dhabi, United Arab Emirates

• Python Overview
• Pandas for pre-Processing and Exploratory Data Analysis
• Numpy for Statistical Analysis
• Matplotlib & Seaborn for Data Visualization
• Scikit Learn
  

Hands-on: No hands-on

• Machine Learning Modelling Flow
• How to treat Data in ML
• Types of Machine Learning
• Performance Measures
• Bias-Variance Trade-Off
• Overfitting & Underfitting

Hands-on: No hands-on

• Maxima and Minima
• Cost Function
• Learning Rate
• Optimization Techniques

Hands-on: No hands-on

• Linear Regression
• Case Study
• Logistic Regression
• Case Study
• KNN Classification
• Case Study
• Naive Bayesian classifiers
• Case Study
• SVM - Support Vector Machines
• Case Study

• With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
• This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
• Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
• We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
• Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.

• Clustering approaches
• K Means clustering
• Hierarchical clustering
• Case Study

• Decision Trees
• Case Study
• Introduction to Ensemble Learning
• Different Ensemble Learning Techniques
• Bagging
• Boosting
• Random Forests
• Case Study
• PCA (Principal Component Analysis) and Its Applications
• Case Study

• Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
• 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 than a single model.
• Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.

• Introduction to Recommendation Systems
• Types of Recommendation Techniques
• Collaborative Filtering
• Content based Filtering
• Hybrid RS
• Performance measurement
• Case Study