• Global CertificationISO 9001:2015
  • Emailinfo@infidata.in
  • Contact+91 7090240240

Register for a Course

Best Machine learning Training in Bangalore

Machine Learning

Thumb

Course

Machine Learning

Course ID

ML100

Duration

50 Hours

Price

₹ 12,000/-
Enroll

Machine Learning (ML) and Artificial Intelligence (AI) are the two hottest buzzwords right now across the globe and India is no exception. The demand for ML&AI talent is skyrocketing as this emerging technology trend is revolutionizing every industry and disrupting the way we do business globally, thereby offering a plethora of opportunities for developers and consultants in many large companies and startups alike.
Pre Requisite: Basic Python

Course Objective

  1. Understand Python and its applications
  2. Understand ML,Data science and how to use python to solve data science problem
  3. Learn python libraries such as Numpy, Pandas,Matlablib, Scikit learn,Web scraping, OpenCV and NLTK to build
  4. Learn machine learning algorithms
  5. Build sample ML project to solve real life problems

Syllabus

Module 1: Introduction

  1. What is Machine Learning
    - Definitions – Theoretical
    - Definitions – Applied Practice
  2. What is Artificial Intelligence
    - Definitions – Theoretical
    - Definitions – Applied Practice
  3. Machine Learning vs AI
  4. Machine Learning vs Deep Learning
  5. What makes Machine Learning tick Algorithms
    - History, Authors
    - Purpose or Objective (Minimize Error or Cost Function)
    - Variety of Algorithms
      * Learning Style
      * Similarity Style
    - Number of Algorithms
    - Infographic
    - Top 10/Most Common ML Algorithms
  6. Types of Machine Learning
    - Supervised
    - Unsupervised
    - Reinforcement
  7. General Steps or Process of Machine Learning
    - SourceX → Feature Extraction → Feature Correlation →
    - Feature TransformX→ Train Model→ Ensemble→ Evaluate
    - Data cleaning, data transform/fitting
    - Overfitting, Underfitting, Variance, Bias
  8. Required Maths
    - Linear Algebra - In Numpy
    - Probability
    - Stats
    - Calculus (Derivates)
  9. Tool Kit
    - Python Basics
    - Python Advance
    - Numpy
    - Pandas
    - Matplotlib
    - Scikit-learn or sklearn Library

Module 2: Supervised Learning

  1. Classification
    - Random Forest
    - Decision Trees
    - Logistic Regression
    - Support Vector Machines
    - KNN
    - Naïve Bayes
    - Usage
  2. Regression
    - Linear Regression
    - Regularization Techniques (LASSO)
    - Polynomial Regression
    - Usage
  3. Case Study (Classification)

Module 3: Unsupervised Learning

  1. Clustering
    - K-Means
    - K Nearest Neighbours
    - Association Rule Learning
  2. Dimensionality Reduction
    - PCA
    - SVD
    - tSNE
  3. Case Study (Clustering/Anomaly/Fraud Detection)

Module 4: Reinforcement Learning

  1. Markov Decision
    - Monte Carlo Prediction
    - Case Study (next best offer, dynamic pricing)

Module 5

  1. Neural Networks/Deep Learning
    - CNN
    - RNN/LSTM/GRU
    - Transfer Learning
    - Case Study (CNN)
  2. Natural Language Processing
    - Text Mining
    - Generation
    - Case Study (Generation)
  3. Predictive Analytics – Forecasting
    - Logistic
    - Time Series (ARIMA)
    - Case Study (Time Series)
  4. Ensemble Techniques
    - Boosting
    - Bagging

Module 6

  1. Machine Learning Applications across Industries
    - Healthcare
    - Retail
    - Financial Services
    - Manufacturing
    - Hospitality
  2. Cloud Based ML Offerings
  3. Top 10 AI Startups
  4. Flashcards (Tips, Tricks, Definitions)

Assesment Details

  1. -Online Test (MCQ) : 50 marks

  2. - Weekly Assesment:20 Marks

  3. - Project Work : 30 marks

Certificate Sample

7090240240

7090240240