500+ hours of learning
23 Milestone
100+ Assignments & Projects
100+ Job Opportunities

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Career
Opportunities
Python Developer, ML Engineer, Data Scientist, Data Analyst, etc
Top skills you
will learn?
Understanding Mathematical Models will help in capturing information from data.This course will help students in understanding fundamental concepts about supervised & unsupervised learning Algorithms.Developing Skills to use Python Libraries such as Numpy, Keras, Sklearn, Matplotlib & many such libraries.
Who can join
the program?
Freshers, Students doing B.E. / BTech, BSc, MSc, MTech, BCA, MCA, BCom, Development Enthusiast, Working Professionals
Minimum
Eligibility
Should know fundamentals of Computer Science

Curriculum

Best-in-class content by leading faculties & industry leaders in form of Live Classes, Projects, Industry Case studies & Assignments.
Milestone 1

Getting Started with language

  • Basics of Programming
  • What is a programming language?
  • Why do we need programming in Industry?
  • Practical examples (without code part)
Milestone 2

Language basics but essentials

  • Introduction to language
  • Data types
  • Type Conversion and typecasting
  • Keywords
  • Variables
  • Identifiers
  • Operators
Milestone 3

Program Flow and Design

  • Flow Control Structures
  • Iterative Control Structures (Loops)
  • Functions
  • Classes & Objects
  • Inheritance
  • Polymorphism
  • Abstraction
  • Encapsulation
Milestone 4

Object Oriented Concepts

  • Classes & Objects
  • Inheritance
  • Polymorphism
  • Abstraction
  • Encapsulation
Milestone 5

Production Level Essentials

  • Exception Handling
  • File Handling
  • Code Debugging
Milestone 6

Numpy

  • Numpy Introduction
  • Numerical operations using Numpy
Milestone 7

Pandas

  • Pandas Introduction
  • Handling datasets and processing data
Milestone 8

Stats for Machine Learning

  • Probability and Statistics
  • Population and Sample
  • Gaussian Normal distribution and CDF
  • Symmetric distribution and skewness
  • Standard normal variate and standardization
  • Kernel density estimation
  • Sampling distribution and Central limit theorem
  • Q-Q plot
  • Various distributions and their use
  • Chebyshev’s inequality
  • Discrete and Uniform distribution
  • Bernoulli and Binomial distribution
  • Log-Normal distribution
  • Power law distribution
  • Box-CoxTransform
  • Application of non-Gaussian distributions
  • Co-Variance
  • Pearson Correlation Coefficient
  • Spearman rank correlation coefficient
  • Correlation Vs Causation
  • Use of correlations
  • Introduction of confidence Interval
  • Computation of confidence interval
  • Hypothesis Testing
  • Resampling and Permutation Test
  • K-S test for similarity of two distributions
  • Proportional Sampling
Milestone 9

Python Essentials for Machine learning

  • Basics of Data Sets
  • Introduction to dimensionality reduction
  • Row and Column Vector
  • Representation of Data Set
  • Representing Data Set as a matrix
Milestone 10

Data Analysis

  • Factors affecting classification algorithms
  • Balanced Vs Imbalanced Datasets
  • Impact of outliers
  • Space and Run time complexity
  • K distance
  • Multiclass classification
  • Time and space complexity of K-Nearest neighbor
  • Feature Importance
  • Handling categorical and numerical features
  • Handling missing values
  • Curse of dimensionality
  • Bias-Variance tradeoff
Milestone 11

Data Preprocessing

  • Data Preprocessing
  • Mean of data matrix
  • Column standardization
  • Covariance of data matrix
  • MNIST Data set
  • PCA (Principle Component Analysis) for dimensionality reduction
  • Limitations of PCA
  • t-SNE for dimensionality reduction
Milestone 12

Preprocessing continued (Text Focused)

  • Preprocessing DataSet
  • Data Cleaning
  • Convert text to vector
  • Bag of words
  • Stemming
  • tf-IDF
  • Word2Vec
Milestone 13

Python Visualizations

  • MatPlotLib Introduction
  • Plotting various types of graphs such as scatter plot, line plot, histogram, etc
  • I2C, on-chip EEPROM
  • Watchdog timer, etc. Case Study of MC
Milestone 14

Visualization Practicals

  • Introduction to Iris dataset
  • 2D scatter plots
  • 3D scatter plots
  • Pair Plots
  • Histograms and Probability density function (PDF)
  • Univariate Analysis using PDF
  • Mean, Median, Variance and standard deviation
  • Cumulative distribution function (CDF)
  • Percentiles and Quantiles
  • Box Plots with whiskers
  • Violin Plots
Milestone 15

Supervised Machine Learning Algorithms

  • Naive Bayes algorithm for classification
  • Logistic Regression
  • Linear Regression
  • Gradient descent algorithm
  • Support Vector Machine
  • Decision tree algorithm for classification
  • Ensembles
  • Random Forest
  • Gradient boosting
  • XGboot and AdaBoost
  • Classification & Regression in Machine learning
  • K-Nearest Neighbour
  • Time and space complexity of K-Nearest neighbor
Milestone 16

Unsupervised Machine Learning Algorithm

  • Clustering algorithms
  • K means algorithm
  • Agglomerative clustering
  • Density-based clustering (DBSCAN)
Milestone 17

Model Performance Metrics

  • Accuracy measure of classification algorithm
  • Accuracy
  • Confusion matrix
  • ROC and AUC curve
  • Log-Loss
  • R-squared coefficient of determination
  • Median absolute deviation (MAD
Milestone 18

Working with different types of datasets

  • Feature Engineering
  • Moving window for time series
  • Fourier decomposition
  • Image histogram
  • Relational data
  • Graph data
  • Feature binning
  • Feature slicing
Milestone 19

Basic of Deep Learning

  • Neural network and Deep Learning
  • History of neural network and comparison with biological neuron
  • Multilayer perceptron
  • Training a single layer model
  • Training MLP model
  • Back Propagation
  • Activation function
  • Vanishing gradient problem
Milestone 20

Components of Deep Learning

  • Deep layer perceptron
  • Drop Outs
  • Regularization
  • RELU
  • OptimizerHill-descent 2D
  • OptimerHill-descent 3D
  • SGD
  • Adam optimizer algorithm
  • Softmax for multiclass classification
  • Tensor Flow and Keras
  • GPU vs CPU
  • Google collaboratory
Milestone 21

Deep Learning Algorithms

  • Convolutional Networks
  • Understanding Visual cortex
  • Edge detection in images
  • Padding and strides
  • Convolutional layer
  • Max Pooling
  • ImageNet data sets
  • AlexNet
  • VGGNet
  • Mini Project: Cats Vs Dogs
  • Given an image of an animal identify whether it is an image of Dog OR Cat OR None
Milestone 22

Advanced Deep Learning Algorithms

  • Recurrent Neural Networks
  • Training RNN model by backpropagation
  • Types of RNN
  • LSTM
  • Deep RNN
  • Bidirectional RNN
Milestone 23

MLOps

  • APIs
  • Docker Containers
  • Hosting

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