Best Machine Learning with Python Course in Bangalore | India

Machine Learning with Python

Machine Learning with Python has been designed for the provision of having strong hold in creating Machine learning algorithms with the base of Python. This has been preferred as the best and robust platform for having Machine Learning systems.

$ 499 $ 399

Courses Description


UPCOMING TRAININGS SCHEDULE

Machine Learning With Python Online Training

5th May - 3rd Jun 2018 | Sat-Sun (5 Weekend) | 02:00 PM - 05:00 PM IST

19th May - 17th June 2018 | Sat-Sun (5 Weekends) | 02:00 PM - 05:00 PM IST

26th May - 24th June 2018 | Sat-Sun (5 Weekends) | 02:00 PM - 05:00 PM IST

14th July - 12th August 2018 | Sat-Sun (5 Weekends) | 02:00 PM - 05:00 PM IST


Machine Learning with Python has been designed for the provision of having strong hold in creating Machine learning algorithms with the base of Python. This has been preferred as the best and robust platform for having Machine Learning systems.

WHY TO PURSUE MACHINE LEARNING?
  • Machine learning is almost acquiring the globe in terms of resource requirement for tech companies, who are into the same domain and execute the projects on Machine Learning.
  • There is a tentative estimation for machine learning market expansion from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022.

  • WHAT ADVANTAGES WILL YOU HAVE WITH THIS MACHINE LEARNING WITH PYTHON CERTIFICATION TRAINING?

    Pursuing this course will make you being able to perform below:

  • Identify different applications for machine learning based algorithms
  • Different learnings, which includes supervised and unsupervised
  • Performing supervised learning techniques such as linear and logistic regression
  • Understanding on data classifications and models
  • Development of robust Machine Learning models
  • Top algorithms among many for any given Machine Learning problem
  • Create accurate predictions and powerful analysis
  • Usage of unsupervised learning algorithms as Clustering & Segmentation.
  • After course completion, a participant can get entitled with the below:

  • Strong knowledge on AI, Data Science and Machine Learning
  • Implementation and having strong holds on principles, algorithms and applications of Machine Learning through unique teaching approach
  • Having depth knowledge of Machine Learning models with the base of mathematical and heuristic aspects of Machine Learning
  • Comprehensive relation between theory and practical for Machine Learning
  • Problem solving ability for different real world scenarios on Machine Learning as we will be covering various real time use cases and you need to accomplish 7+ ML projects
  • There is an increasing demand for skilled Machine Learning Engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning Training particularly for the following professionals:

  • Enthusiastic Software Developers to be a Data Scientist or Machine Learning Engineers
  • Managers from Analytics background
  • BA profile holders, who are willing to know about Data Science techniques
  • Software/Solution Architects, who wants to have strong hold on Machine Learning algorithms
  • Analytics candidates, who are opting to work in the domain of Machine Learning or Artificial Intelligence
  • Graduates seeking for building their career in Data Science and Machine Learning
  • Some of the real-life projects included in the Course are like,

  • Predicting Housing Price
  • Predicting a business deal outcome (win or loss)
  • Predicting sales performance of newly on-boarded dealer/distributors
  • Predicting credit card defaulter (Yes or No)
  • Predicting commodity price
  • Customer segmentation
  • Time series forecasting
  • The objective of the projects is to provide a perspective of the technical challenges faced by any data scientist in the real life scenarios.

  • David Barber’s Bayesian Reasoning and Machine learning
  • Kevin Murphy’s Machine learning: a Probabilistic Perspective
  • Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning
  • Bishop’s Pattern Recognition and Machine Learning
  • Mitchell’s Machine Learning
  • Deep Learning & Artificial Intelligence (AI). These are surely going to be the next waves in the data science space. As we have started living in a ‘smart’ environment, AI is going to play an important role to build all the smart and autonomous systems; and potentially creating significant job opportunities in this field.

    According to Naukri.com data scientist is a lucrative career choice in the India with an average salary of 17 Lac per year. A top-level Machine learning professionals with extensive experience in the industry can earn as much as 52 Lac+ a year – making it one of the highest paying positions in the entire IT industry. Given below is the salary trend for data scientists at different levels of their career –

    EXPERIENCE AVERAGE SALARY:
  • 1-5 Years 6Lac-12lac
  • 6-10 Years 12Lac-22 Lac
  • 10-20 Years 22 Lac-42 Lac
  • 20+ Years 52 Lac+
  • [Reference: www.naukri.com ]

    CURRENT JOB VACANCY IN INDIA:

    Listed below are the number of job advertisements for Machine learning professionals across various popular job portals, as measured in middle of December 2017.

    NO. OF COMPANIES HIRING VIA JOB SITES:
  • Indeed, 2350
  • Naukri.com,9343
  • Monster.com, 2340
  • Courses Curriculum

    Lecture1.1 Data science & its importancePreview

    Lecture1.2 Key Elements of Data Science

    Lecture1.3 Data Warehousing

    Lecture1.4 Business Intelligence

    Lecture1.5 Data Visualization

    Lecture1.6 Data Mining

    Lecture1.7 Machine Learning

    Lecture1.8 Artificial Intelligence

    Lecture1.9 Cloud Computing

    Lecture1.10 Big Data

    Lecture1.11 Artificial Intelligence: A previewPreview

    Lecture1.12 What is Artificial Intelligence & its importance

    Lecture1.13 Artificial Intelligence vs Machine Learning

    Lecture2.1 What is Machine Learning (ML)?Preview

    Lecture2.2 How machines learn Preview

    Lecture2.3 Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement

    Lecture2.4 Basics of Classification, Regression and Clustering algorithms

    Lecture2.5 Creating your first Prediction Model

    Lecture2.6 Training & Model evaluation

    Lecture2.7 Choosing Machine Learning algorithm

    Lecture3.1 A quick refresh on basic intermediate maths

    Lecture3.2 Linear Algebra (Vectors, Matrix, Eigen Values)

    Lecture3.3 Probability and Statistics Preview

    Lecture3.4 Hypothesis testingPreview

    Lecture3.5 Optimization

    Lecture4.1 A quick crash course on basics of PythonPreview

    Lecture4.2 What is PythonPreview

    Lecture4.3 Working with Python

    Lecture4.4 Basic scripts on

    Lecture4.5 Read, write, data handling

    Lecture4.6 Loops

    Lecture4.7 Conditions (if-else)

    Lecture4.8 Function

    Lecture4.9 Code modularization

    Lecture4.10 Scikit-Learn package

    Lecture4.11 Basic visualization

    Lecture5.1 Data Collection & Preparation

    Lecture5.2 Data Mugging

    Lecture5.3 Outlier Analysis

    Lecture5.4 Missing value treatment

    Lecture5.5 Feature Engineering

    Lecture5.6 Data TransformationPreview

    Lecture5.7 Normalization vs Standardization

    Lecture5.8 Creating Dummies

    Lecture5.9 Dimensionality Reduction

    Lecture5.10 Principal Component AnalysisPreview

    Lecture6.1 Supervised Machine Learning algorithms

    Lecture6.2 Linear RegressionPreview

    Lecture6.3 Logistic RegressionPreview

    Lecture6.4 Decision/Classification Tree

    Lecture6.5 Ensemble Models

    Lecture6.6 Bagging

    Lecture6.7 Boosting

    Lecture6.8 Random Forest

    Lecture6.9 K-Nearest Neighbours (KNN)

    Lecture6.10 Naive Bayes

    Lecture6.11 Neural Network (Deep Learning)

    Lecture6.12 Support Vector Machine

    Lecture6.13 Unsupervised Machine Learning algorithms

    Lecture6.14 Clustering with K-means Clustering

    Lecture6.15 Bias-Variance Trade off

    Lecture6.16 Regularization

    Lecture6.17 Parameter tuning & grid search optimization

    Lecture7.1 Real life cases with Python

    Lecture7.2 Predicting Housing Price

    Lecture7.3 Predicting a business deal outcome (win or loss)

    Lectures7.4 Predicting sales performance of newly on-boarded dealer/distributors

    Lectures7.5 Predicting credit card defaulter (Yes or No)

    Lectures7.6 Predicting commodity price

    Lectures7.7 Customer segmentation

    Lectures7.8 Time series forecasting

    Lectures7.9 Others

    Lecture8.1 Students would be given challenging real life cases to solve – just to augment their learning skills.

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