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Machine Learning

Machine learning Training in Mohali and Chandigarh

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!

Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more

Solve any problem in your business, job or personal life with powerful Machine Learning models.

  • Set up a Python development environment correctly
  • Gain complete machine learning tool sets to tackle most real world problems
  • Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
  • Combine multiple models with by bagging, boosting or stacking
  • Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
  • Develop in Jupyter (IPython) notebook, Spyder and various IDE
  • Communicate visually and effectively with Matplotlib and Seaborn
  • Engineer new features to improve algorithm predictions
  • Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
  • Use SVM for handwriting recognition, and classification problems in general
  • Use decision trees to predict staff attrition
  • Apply the association rule to retail shopping datasets
  • And much much more!
  • Anyone willing and interested to learn machine learning algorithm with Python
  • Any one who has a deep interest in the practical application of machine learning to real world problems
  • Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
  • Any intermediate to advanced EXCEL users who is unable to work with large datasets
  • Anyone interested to present their findings in a professional and convincing manner
  • Anyone who wishes to start or transit into a career as a data scientist
  • Anyone who wants to apply machine learning to their domain

Curriculum For This Course

  • Introduction
  • What Does the Course Cover?
  • How to Succeed in This Course
  • Project Files

Getting Started with Anaconda

  • [Windows OS] Downloading & Installing Anaconda
  • [Windows OS] Managing Environment
  • [Mac OS] Intructions on Installing Anaconda and Managing Environment
  • Linux OS
  • Practice Activity: Create a New Environment
  • Navigating the Spyder & Jupyter Notebook Interface
  • Downloading the IRIS Datasets
  • Data Exploration and Analysis
  • Presenting Your Data
  • Getting Started

Regression

  • Introduction
  • Categories of Machine Learning
  • Machine Learning Basic Concepts
  • Working with Scikit-Learn
  • Boston Housing Data – EDA
  • Correlation Analysis and Feature Selection
  • Simple Linear Regression Modelling with Boston Housing Data
  • Robust Regression
  • Evaluate Model Performance
  • Multiple Regression with statsmodel
  • Multiple Regression and Feature Importance
  • Ordinary Least Square Regression and Gradient Descent
  • Regularised Method for Regression
  • Polynomial Regression
  • Dealing with Non-linear relationships
  • Feature Importance Revisited
  • Data Pre-Processing 1
  • Data Pre-Processing 2
  • Variance Bias Trade Off – Validation Curve
  • Variance Bias Trade Off – Learning Curve
  • Cross Validation

Classification

  • Introduction
  • Logistic Regression 1
  • Logistic Regression 2
  • MNIST Project 1 – Introduction
  • MNIST Project 2 – SGDClassifier
  • MNIST Project 3 – Performance Measures
  • MNIST Project 4 – Confusion Matrix, Precision, Recall and F1 Score
  • MNIST Project 5 – Precision and Recall Tradeoff
  • MNIST Project 6 – The ROC Curve
  • MNIST Exercise

Support Vector Machine (SVM)

  • Introduction
  • Support Vector Machine (SVM) Concepts
  • Linear SVM Classification
  • Polynomial Kernel
  • Gaussian Radial Basis Function
  • Support Vector Regression
  • Advantages and Disadvantages of SVM

Tree

  • Introduction
  • What is Decision Tree
  • Training a Decision Tree
  • Visualising a Decision Trees
  • Decision Tree Learning Algorithm
  • Decision Tree Regression
  • Overfitting and Grid Search
  • Where to From Here
  • Project HR – Loading and preprocesing data
  • Project HR – Modelling

Ensemble Machine Learning

  • Introduction
  • Ensemble Learning Methods Introduction
  • Bagging Part 1
  • Bagging Part 2
  • Random Forests
  • Extra-Trees
  • AdaBoost
  • Gradient Boosting Machine
  • XGBoost
  • Project HR – Human Resources Analytics
  • Ensemble of ensembles Part 1
  • Ensemble of ensembles Part 2

k-Nearest Neighbours (kNN)

  • kNN Introduction
  • kNN Concepts
  • kNN and Iris Dataset Demo
  • Distance Metric
  • Project Cancer Detection Part 1
  • Project Cancer Detection Part 2

Dimensionality Reduction

  • Introduction
  • Dimensionality Reduction Concept
  • PCA Introduction
  • Dimensionality Reduction Demo
  • Project Wine 1: Dimensionality Reduction with PCA
  • Project Abalone
  • Project Wine 2: Choosing the Number of Components
  • Kernel PCA
  • Kernel PCA Demo
  • LDA & Comparison between LDA and PCA

Unsupervised Learning: Clustering

  • Introduction
  • Clustering Concepts
  • MLextend
  • Ward’s Agglomerative Hierarchical Clustering
  • Truncating Dendrogram
  • k-Means Clustering
  • Elbow Method
  • Silhouette Analysis
  • Mean Shift

LEARN FROM THE INDUSTRY EXPERT & GET CERTIFIED

Machine Learning Job Openings in Mohali and Chandigarh

  • Machine Learning key skills are on statistics, data mining, reporting/visualization, classified algorithms, supervised and unsupervised machine learning algorithms. In the current IT market, there are plenty of Machine Learning opportunities for the experienced professionals who are aware of the above technologies.
  • If you possess strong Machine Learning experience with deep learning,NLP, C, C++Java and Python, you can get job as Machine Learning expert.
  • If you possess Machine Learning as a co-skill along with business analysis, SQL and R, you can get job as Data Scientist.
  • If you possess Machine Learning as a co-skill along with random forest, neural networks, gradient boosting, C C++, Python, Bayesian and predictive analytics, you can get job as Research Analyst.
  • If you possess Machine Learning as a co-skill along with Hadoop, NoSQL and Big Data concepts, you can get job as Technology Architect.
  • Some of the companies that hire for Machine Learning experts are JP Morgan, Accenture, Aricent, Fiserv, SAP, Sutherland, Intel, AIG, Bosch, Qualcomm.

Compared to other training institutes, ITronix Solutions Mohali is one of the best MSBI training institutes in Mohali where you can acquire the best MSBI training and placement guidance.