Machine Learning Training

Live Online & Classroom Enterprise Training

By learning concepts of machine learning & artificial intelligence, you will be able to make machines do things as intelligently as humans do. Now, wouldn’t that be too amazing to ignore? Well, why not! This Machine Learning Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.

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What is Machine Learning Course about?

This course Machine Learning Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

What are the objectives of Machine Learning Course ?

  • During this Online training, you’ll learn:
    • Gain insight into the ‘Roles’ played by a Machine Learning Engineer
    • Automate data analysis using python
    • Describe Machine Learning
    • Work with real-time data
    • Learn tools and techniques for predictive modeling
    • Discuss Machine Learning algorithms and their implementation
    • Validate Machine Learning algorithms
    • Explain Time Series and it’s related concepts
    • Gain expertise to handle business in future, living the present

Who is Machine Learning Course for?

The following professionals can go for this course:

  • Developers aspiring to be a ‘Machine Learning Engineer’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘Python’ professionals who want to design automatic predictive models

What are the prerequisites for Machine Learning Course?

  • Python Development experience
  • Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus.

Available Training Modes

Live Online Training
72 Hours/12 Weekends

Classroom Training
72 Hours/12 days

Course Outline

Introduction to Python

  • Concepts of Python programming
  • Configuration of Development Environment
  • Variable and Strings Functions
  • Control Flow and Loops
  • Tuple, Lists and Dictionaries
  • Standard Libraries
  • Plotting in Python
  • Data wrangling
  • Python for Data Science

Introduction to Data Science and Machine learning

  • Data science overview
  • Machine learning overview
  • Supervised vs unsupervised learning
  • Regression, Classification, Clustering and other tasks in ML
  • Popular case study examples

Statistical foundations

  • Measures of central tendency and spread
  • Probability distributions (discrete and continuous)
  • Bernoulli, Binomial and Poisson distribution
  • Normal distribution
  • Central limit theorem
  • Inferential statistics
  • Hypothesis testing

Exploratory Data Analysis, visualization, pre-processing

  • Univariate analysis
  • Multivariate analysis
  • Data visualization
  • Outlier detection and handling, data clean up, missing value treatment

Regression techniques: Linear Regression

  • Simple linear regression
    • Fitting a model
    • Statistics and inference
  • Multiple linear regression
    • Moving from one to many predictor – new considerations
    • Feature selection
    • Model interpretation

Classification techniques: Logistic regression

  • Introduction to logistic regression
  • Multivariate logistic regression
  • Model evaluation and assessment metrics
    • Confusion matrix
    • Precision, Recall curves

Classification techniques: Tree based methods

  • Decision trees
  • Visualizing decision trees
  • Ensembling multiple trees
    • Bagging
    • RandomForest
    • Boosting

Unsupervised learning: Clustering

  • Introduction to clustering
  • Applications and use cases
  • Clustering algorithms
    • K means clustering algorithm
    • Hierarchal clustering
  • Comparing different clustering techniques
  • Practical issues in clustering

Text Analytics / Natural Language Processing – text processing and analysis

  • Exploring and analyzing text data
  • Frequency and TfIdf
  • Text cleanup
  • Text visualization

Text Analytics / Natural Language Processing – Sentiment analysis, Social media analysis

  • Text classification basics
  • Sentiment classification/prediction
  • Social media analytics

Recommender systems

  • Approaches to recommender systems
  • Collaborative filtering
    • User-user
    • Item-item
  • Evaluating recommender systems
  • Practical considerations

Who is the instructor for this training?

Our trainer for this training course has extensive experience in Machine Learning including years of years of experience imparting Python and Machine Learning training for corporate and professionals alike.



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