Machine Learning

machine-learning-course-in-nagpur
Program Overview
Key Highlights
  • 1400+ Hours of Learning
  • Designed for freshers and professionals
  • 15+ Case Studies and Assignments
  • 6 Practical Hands-on Capstone Projects
  • Live Coding Classes & Profile Building Workshops
  • No Cost EMI Options available
  • Dedicated Student Success Mentor
  • 3 Electives to customize your learning path
  • Learning management system
  • Daily Doubt Resolution Sessions
  • Career Mentorship Sessions(1:1)
  • High Performance Coaching(1:1)
  • Exclusive Job opportunities
  • AI Powered Profile Builder
  • Personalised Industry Session
  • Creation of portfolio website on Github to boost the learners’ career persona
Tools Covered For Machine Learning
Python-img
Python
NumPy-img
NumPy
Matplotlib-img
Matplotlib
Scikit-Learn-img
Scikit Learn
Pandas-img
Pandas
Seaborn-img
Seaborn
And More....
Description
Machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Skills You Will Learn
  1. Python for Machine Learning
  2. EDA and Data Processing
  3. Statistical Learning
  4. Time-series Forecasting
Syllabus
Module 1 : Python for Machine Learning
  • Python Basics
  • Python Functions and Packages
  • Working with Data Structures, Arrays, Vectors & Data Frames
  • Jupyter Notebook – Installation & function
  • Pandas, NumPy, Matplotlib, Seaborn
Self paced module : EDA and Data Processing
  • Data Types
  • Dispersion & Skewness
  • Uni & Multivariate Analysis
  • Data imputation
  • Identifying and normalizing Outliers
Module 2 : Statistical Learning
  • Descriptive Statistics
  • Probability & Conditional Probability
  • Hypothesis Testing
  • Inferential Statistics
  • Probability Distributions
Module 3 : Supervised learning
  • Linear Regression
  • Multiple Variable Linear Regression
  • Logistic Regression
  • Naive Bayes Classifiers
  • k-NN Classification
  • Support Vector Machines
Module 4 : Ensemble Techniques
  • Decision Trees
  • Bagging
  • Random Forests
  • Boosting
Module 5 : Feature Selection
  • Feature Engineering and its importance
  • EDA
  • Feature Selection ( Forward selection, Backward Elimination)
  • Regularization for Feature Selection
  • Regularizing Linear Models (Shrinkage methods) - Lasso and Ridge
Module 6 : Unsupervised learning
  • K-means Clustering
  • Hierarchical Clustering
  • Dimension Reduction-PCA
Module 7 : Model Selection & Hyper parameter Tuning
  • Model Selection - Cross Validation
  • Bootstrap Sampling
  • Hyper Parameters & Tuning
  • Hyper Parameters & Tuning (GridsearchCV/RandomizedSearchCv)
  • Performance Evaluation
  • Sampling
Self paced modules : Time-series Forecasting
  • Introduction to forecasting data
  • Properties of Time Series data
  • Examples and features of Time Series data
  • Naive, Average and Moving Average Forecasting
  • Exponential Smoothing
  • ARIMA Approach
Model deployment
  • Model serialization- pickle and joblib
  • Rest APIs- Flask (real-time prediction)
  • Docker Containerization
  • Kubernetes (using Google cloud)
Module 8 : Recommendation Systems
  • Introduction to Recommendation Systems
  • Popularity based model
  • Content based Recommendation System
  • Collaborative Filtering (User similarity & Item similarity)
  • Hybrid Models
Duration - 6 Months
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