What is data science and why is it so important?
Applications of data science
Various data science tools
Data Science project methodology
Tool of choice-Python: what & why?
Installation of Python framework and packages: Anaconda & pip
Writing/Running python programs using Spyder Command Prompt
Working with Jupyter notebooks
Creating Python variables
Numeric , string and logical operations
Data containers : Lists , Dictionaries, Tuples & sets
Writing for loops in Python
While loops and conditional blocks
List/Dictionary comprehensions with loops
Writing your own functions in Python
Writing your own classes and functions
Need for data summary & visualization
Summarising numeric data in pandas
Summarising categorical data
Group wise summary of mixed data
Basics of visualisation with ggplot & Seaborn
Inferential visualisation with Seaborn
Visual summary of different data combinations
Introduction to NumPy arrays, functions & properties
Introduction to Pandas & data frames
Importing and exporting external data in Python
Feature engineering using Python
Regularisation of Generalised Linear Models
Ridge and Lasso Regression
Methods of threshold determination and performance measures for classification score models
Introduction to decision trees
Tuning tree size with cross validation
Introduction to bagging algorithm
Grid search and randomized grid search
ExtraTrees (Extremely Randomised Trees)
Partial dependence plots
Case Study & Assignment
Concept of weak learners
Introduction to boosting algorithms
Extreme Gradient Boosting (XGBoost)
Case Study & assignment
Converting business problems to data problems
Understanding supervised and unsupervised learning with examples
Understanding biases associated with any machine learning algorithm
Ways of reducing bias and increasing generalisation capabilites
Drivers of machine learning algorithms
Brief introduction to gradient descent
Importance of model validation
Methods of model validation
Cross validation & average error
Introduction to idea of observation based learning
Distances and similarities
k Nearest Neighbours (kNN) for classification
Brief mathematical background on SVM/li>
Regression with kNN & SVM
Need for dimensionality reduction
Principal Component Analysis (PCA)
Difference between PCAs and Latent Factors
Hierarchical, K-means & DBSCAN Clustering
Gathering text data using web scraping with urllib
Processing raw web data with BeautifulSoup
Interacting with Google search using urllib with custom user agent
Collecting twitter data with Twitter API
Naive Bayes Algorithm
Feature Engineering with text data
Need and Importance of Version Control
Setting up git and github accounts on local machine
Creating and uploading GitHub Repos
Push and pull requests with GitHub App
Merging and forking projects
Introduction to Bokeh charts and plotting
Examples of static and interactive data products
Upon course completion, the candidate will be able to:
Machine Learning is being used by most among the world leading organizations. This training is the perfect choice for:
The participants of our training should have:
The following are the essential skills that users will gain upon completion of our training course:
27 Sep, 2020 - 22 Oct, 2020
30 Sep, 2020 - 13 Oct, 2020
03 Oct, 2020 - 01 Nov, 2020
04 Oct, 2020 - 29 Oct, 2020
07 Oct, 2020 - 20 Oct, 2020
10 Oct, 2020 - 08 Nov, 2020
Our Machine Learning course covers all the topics that are required to clear Machine Learning certification. Trainer will share Machine Learning certification guide, Machine Learning certification sample questions, Machine Learning certification practice questions.
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Yes, you get two kinds of discounts. They are group discount and referral discount. Group discount is offered when you join as a group, and referral discount is offered when you are referred from someone who has already enrolled in our training.
The trainer will give Server Access to the course seekers, and we make sure you acquire practical hands-on training by providing you with every utility that is needed for your understanding of the course.
The trainer is a certified consultant and has significant amount of experience in working with the technology.
Yes, we accept payments in two installments.
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within first 2 sessions of the training. Please make a note that refunds will be processed within 30 days of prior request.
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