Machine Learning Overview

Machine Learning Course provide trainees with an in-depth understanding of the concepts of Machine Learning and its implementation in Python programming language. This training also help learners in understanding the concepts of Reinforcement Learning, Regression, Classification, Time Series Analysis. This course also showcases the different use cases of Machine Learning in Python. 

Machine Learning Key Features

100% Money Back Guarantee
  • Live Training 58 hours of blended learning
  • Live Training 14 hours of Online self-paced training
  • Live Training 44 hours of instructor-led training
  • Live Training 4 industry-based projects
  • Live Training Interactive learning with Jupyter notebooks integrated labs
  • Live Training Dedicated mentoring session
  • Live Training Flexible access to online classes
  • Live Training No exam included
  • Live Training 36 Hours of Online self-paced training
  • Live Training Real-life Case Studies
  • Live Training Practical assignments
  • Live Training Practical assignments
  • Live Training 24 x 7 Expert Support
  • Live Training Certification
  • Live Training Global community forum for all our users
  • Live Training Industry based Projects
  • Live Training No exam Included
  • Live Training 20 Hrs of online self-paced training
  • Live Training Real-life Case Studies
  • Live Training Assignments
  • Live Training Lifetime access to all the videos
  • Live Training Certification
  • Live Training Community forum for all our Learners
  • Live Training No Exam Included

Skills Covered

  • Supervised and unsupervised learning
  • Linear and logistic regression
  • Time series modeling
  • Different aspects of Business Intelligence and Data Science
  • Data Extraction, Wrangling, & Visualization
  • Machine Learning with Python
  • Dimensionality Reduction, Reinforcement Learning and Time Series Analysis
  • Develop Machine Learning Applications
  • Different Machine Learning techniques.
  • Collaborative filtering, Clustering and Categorization
  • Analyze Big Data using Hadoop and Mahout
  • Implement a recommender using MapReduce

Benefits

  • Growing demand in the market
  • Better career prospects
  • Lucrative pay packages 

Training Package Options

Course Content

  • Machine Learning

    • Introduction to Data Science

        • What is Data Science?
        • What does Data Science involve?
        • Era of Data Science
        • Business Intelligence vs Data Science
        • Life cycle of Data Science
        • Tools of Data Science
        • Introduction to Python
        • Data Extraction, Wrangling, & Visualization
        • Data Analysis Pipeline
        • What is Data Extraction
        • Types of Data
        • Raw and Processed Data
        • Data Wrangling
        • Exploratory Data Analysis
        • Visualization of Data
    • Introduction to Machine Learning with Python

        • Python Revision (numpy, Pandas, scikit learn, matplotlib)
        • What is Machine Learning?
        • Machine Learning Use-Cases
        • Machine Learning Process Flow
        • Machine Learning Categories
        • Linear regression
        • Gradient descent
    • Supervised Learning - I

        • What is Classification and its use cases?
        • What is Decision Tree?
        • Algorithm for Decision Tree Induction
        • Creating a Perfect Decision Tree
        • Confusion Matrix
        • What is Random Forest?
    • Dimensionality Reduction

        • Introduction to Dimensionality
        • Why Dimensionality Reduction
        • PCA
        • Factor Analysis
        • Scaling dimensional model
        • LDA
    • Supervised Learning - II

        • What is Naïve Bayes?
        • How Naïve Bayes works?
        • Implementing Naïve Bayes Classifier
        • What is Support Vector Machine?
        • Illustrate how Support Vector Machine works?
        • Hyperparameter optimization
        • Grid Search vs Random Search
        • Implementation of Support Vector Machine for Classification
    • Unsupervised Learning

        • What is Clustering & its Use Cases?
        • What is K-means Clustering?
        • How does K-means algorithm works?
        • How to do optimal clustering
        • What is C-means Clustering?
        • What is Hierarchical Clustering?
        • How Hierarchical Clustering works?
    • Association Rules Mining and Recommendation Systems

        • What are Association Rules?
        • Association Rule Parameters
        • Calculating Association Rule Parameters
        • Recommendation Engines
        • How Do Recommendation Engines work?
        • Collaborative Filtering
        • Content Based Filtering
    • Reinforcement Learning

        • What is Reinforcement Learning
        • Why Reinforcement Learning
        • Elements of Reinforcement Learning
        • Exploration vs Exploitation dilemma
        • Epsilon Greedy Algorithm
        • Markov Decision Process (MDP)
        • Q values and V values
        • Q ? Learning
        • ? values
    • Time Series Analysis

        • What is Time Series Analysis?
        • Importance of TSA
        • Components of TSA
        • White Noise
        • AR model
        • MA model
        • ARMA model
        • ARIMA model
        • Stationarity
        • ACF & PACF
    • Model Selection and Boosting

        • What is Model Selection?
        • Need of Model Selection
        • Cross ? Validation
        • What is Boosting?
        • How Boosting Algorithms work?
        • Types of Boosting Algorithms
        • Adaptive Boosting