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Machine Learning is the basis for the most exciting careers in data analysis today. Machine learning brings together computer science and statistics to harness that predictive power. Understanding the philosophy behind machine learning. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it

Module List

  • Introduction
  • Linear Regression with One Variable
  • Linear Algebra Review
  • Linear Regression with Multiple Variables
  • Octave/Matlab
  • Logistic Regression
  • Regularization
  • Neural Networks: Representation
  • Neural Networks: Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design
  • Support Vector Machines
  • Unsupervised Learning
  • Dimensionality Reduction
  • Anomaly Detection
  • Recommender Systems
  • Large Scale Machine Learning
  • Application Example: Photo OCR


    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?

    Case study

    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

    Practice assignment

    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

    Practice assignment

    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

    Practice assignment

    Introduction to NumPy arrays, functions & properties

    Introduction to Pandas & data frames

    Importing and exporting external data in Python

    Feature engineering using Python

    Linear Regression

    Regularisation of Generalised Linear Models

    Ridge and Lasso Regression

    Logistic Regression

    Methods of threshold determination and performance measures for classification score models

    Case Study

    Introduction to decision trees

    Tuning tree size with cross validation

    Introduction to bagging algorithm

    Random Forests

    Grid search and randomized grid search

    ExtraTrees (Extremely Randomised Trees)

    Partial dependence plots

    Case Study & Assignment

    Concept of weak learners

    Introduction to boosting algorithms

    Adaptive Boosting

    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

    Cost functions

    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

    Case Study

    Need for dimensionality reduction

    Principal Component Analysis (PCA)

    Difference between PCAs and Latent Factors

    Factor Analysis

    Hierarchical, K-means & DBSCAN Clustering

    Case study

    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

    Sentiment analysis

    Case study

    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

    Case study

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Training Advantages
35 contact hours
Industry Case Studies
Industry case studies
Real time training


The machine learning field is continuously evolving. And along with evolution comes a rise in demand and importance. There is one crucial reason why data scientists need machine learning, and that is: ‘High‑value predictions that can guide better decisions and smart actions in real‑time without human intervention.’

Top-notch professionals in that field who understands how to convey things in technical as well as subject matter experts.

It increases quality and reduces development time due to re-use of previous work, real mapping to the problem domain and modular architecture

You can reach us through +302-440-1478. Or you can share your queries through info@krishitservices.org. Estimated turnaround time will be 24 hours for emails.