Advanced Machine Learning
Tools Covered
- R
- Python
- Jupyter
- Spark
- H2O
- AzureML
Pre-requisites
- Understanding of data science
- Passion for new technologies
What you will learn
- Machine Learning Tools In demand by MNCs
- Machine Learning Methods with real world case studies
About Our Trainer
- Karthik work as an AI Consultant & Instructor, He has over 20+ years of Implementation experience and recognized expert in computer vision, NLP, robotics, AI, Machine Learning, Deep Learning & Tensorflow.
Bhasker is a Data Science evangelist and practitioner with proven record of thought leadership and incubating analytics practices for various organizations. With over 12 years of experience in the area of Business Analytics, he is well recognized as an expert within the industry. Earlier, Bhasker worked as Vice President at Goldman Sachs.
Bhasker currently advises various startups on how to utilize analytics and thinks about interesting ideas in this space in free time. He is B.Tech from Indian Institute of Technology, Varanasi and MBA from Indian Institute of Management, Lucknow.
Want a free career Advice or any career related queries? Reach him by
+91- 8939915572
Course Curriculum
Curriculum
Unit 1 : Machine Learning Model Building
- Machine Learning Foundation
- Hypothesis Testing & P values
- Statistical Tests in R
- Sampling Methods & CI
- Feature Engineering Methods
- Dimensionality Reduction
- Overfitting & Under-fitting
- Bias & Variance Tradeoff
- Dataset Sampling & Partitions
- Computational Limitations
- Model Building Steps in R
- Model Building Steps in Python
Unit 2: Predictive Analytics
- Data Exploration Case Study
- Linear Regression
- Linear Model Fits & Evaluation Metrics
- Non Linear Regression
- L1 & L2 Regularization
- Classification Methods Overview
- Accuracy,Confusion Matrix & ROC curve
- Logistic Regression
- Logistic Model Fits & Evaluation Metrics
- Decision Trees & Rule Learners
- Trees Model Fits & Evaluation Metrics
- Naïve Bayes Classifier Model
- KNN Classifier Model
- Model Selection Parameters
- Case Study 1 : Classification
- Case Study 2 : Trees
- Case Study 3 : Regression
Unit 3: ML Advanced : Black Box & Ensemble
- Support Vector Mahcines
- Neural Networks
- ML Ensemble Methods
- Bagging & Random Forest
- Gradient Boosting Methods (GBM)
- Case Study 4 : SVM
- Case Study 5 : Random Forest
- Case Study 6 : GBM
Unit 4: Unsuperwised Machine Learning
- K-means Clustering
- Hierarchical Clustering
- Principle Component Analysis(PCA)
- Feature Hashing
Unit 5: Machine Learning Tools
- Hadoop Architecture for big data
- R & h2o
- Python SciKit Learn
- Spark Architecture
- Spark ML and PySpark
- Spark ML and Sparklyr
- Microsoft AzureML
- Working with Azure Cloud
- Project selection on real life case study
- Best Practices & Project progress discussion
Unit 6: Job Interview Essentials
- CV Preparation
- Effective selling of CV in job sites
- Soft Skills for Data Scientist
- Project: Final presentation
- Executive presence for Interview
