AI with ML & DL

Artificial Intelligence with Machine Learning & Deep Learning

(179 reviews)
Artificial Intelligence Machine Learning Deep Learning

Artificial Intelligence with Machine Learning & Deep Learning Classroom Instructor-Led Course has been composed by two expert Data Scientists with the goal that we can share our insight and enable you to learn complex hypothesis, calculations,coding libraries on machine learning & Deep Learning

Machine learning language is man made brainpower will characterize the up and coming age of programming arrangements. This course gives a review of AI, Machine Learning and Deep Learning clarify how it can be utilized to assemble brilliant applications that assistance associations be more proficient and advance individuals’ lives.

Course Features

  • Students 179
  • Duration4/6 week
  • PrerequisitesPython Concept
  • LanguageEnglish
    • Module 1

      Introduction Duration: 4 Hours

      Artificial Intelligence & Machine Learning Introduction Who uses AI?

      AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain

      AI v/s ML v/s DL and Data Science

      Typical applications of Machine Learning for optimizing IT Operations

      Supervised & Unsupervised Learning Reinforcement Learning

      Regression & Classification Problems Clustering and Anomaly Detection Recommendation System

      What makes a Machine Learning Expert?

      What to learn to become a Machine Learning Developer?

      Module 2

      Math for Machine Learning – Statistics Basics


      Duration: 4 Hours

      Types of variable

      Categorical and Continuous Data Ratio and Interval

      Nominal and Ordinal Data

      Measure of Central Tendency – Mean, Mode and Median Percentile and Quartile

      Measure of Spread – IQR, Variance and Standard Deviation Empirical Rule

       Chebyshev’s Theorem Z Test

      Coefficient of Variation Kurtosis and Skewness


      Assignment 1

      Module 3

      Math for Machine Learning – Analysing Data using Statistics & Probabilistic Analysis


      Duration: 4 Hours

      Analysing Categorical and Continuous Data

      Proportional Test Chi Square Test Covariance Correlation

      T Test Anova


      Probabilistic Analysis

      Events and their Probabilities Rules of Probability

      Conditional Probability and Independence



      Bayes Theorem

      Moment Generating Functions Central Limit Theorem

      Expectation & Variance

      Standard Distributions – Bernoulli, Binomial & Multinomial

      Module  4 Introduction to Python programming



      Duration: 4 Hours

      Introduction to Python Programming What is Python?

      Understanding the Spyder Integrated Development Environment (IDE)

      Python basics and string manipulation lists, tuples, dictionaries, variables

      Control Structure – If loop, For loop and while Loop Single line loops

      Writing user defined functions

      Object oriented programming with Python


      Assignment 2

      Module 5

      Python for Data handling

      – numpy and Pandas Duration: 4 Hours

      Mathematical Computing with Numpy

      NumPy Overview

      Properties, Purpose, and Types of ndarray Class and Attributes of ndarray Object Basic Operations: Concept and Examples

      Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays

      Copy and Views

      Universal Functions (ufunc)

      Shape Manipulation & Broadcasting Linear Algebra using numpy Stacking and resizing the array


      Introduction to Pandas

      Data Structures

      Series, DataFrame & Panel DataFrame basic properties

      Importing excel sheets, csv files, executing sql queries Importing and exporting json files

      Selection of columns Filtering Dataframes Handling Missing Values

      Finding unique values and deleting duplicates



      Module 6

      Python for Data Handling

      – pandas

      Data Visualization with matplotlib and seaborn


      Duration: 4 Hours

      Descriptive Analysis with pandas

      Creating new categorical features from continuous variable groupby operations

      groupby statistical Analysis Apply method

      String Manipulation


      Introduction to Data Visualization

      Matplotlib Features:

      Line Properties Plot with (x, y) Controlling Line Patterns and Colors Set Axis, Labels, and Legend Properties Alpha and Annotation

      Multiple Plots Subplots

      Types of Plots and Seaborn Boxplots

      Distribution Plots Clustermaps Heatmaps

      Voilin plots

      Swarmplots and countplots


      Assignment 3

      Module 7

      Linear Regression Duration: 4 Hours

      Regression Problem Analysis

      Mathematical modelling of Regression Model OLS method for Linear Regression

      Finding the coefficients and intercept Gradient Descent Algorithm Programming Process Flow

      Use cases

      Programming Using python

      Bifurcate Data into Training / Testing Data set Build Model on Training Data Set

      Predict using Testing Data Set Validate the Model Performance

      Building simple Univariate Linear Regression Model

      Module 8

      Linear Regression Duration: 4 Hours

      Multivariate Regression Model

      Correlation Analysis – Analyzing the dependence of variables Apply Data Transformations

      L1 & L2 Regularization

      Identify Multicollinearity in Data Treatment on Data




      Identify Heteroscedasticity Modelling of Data

      Variable Significance Identification Model Significance Test

      R2, MAPE, RMSE

      Project: Predictive Analysis using Linear Regression

      Module 9

      Logistic Regression Duration: 4 Hours

      Classification Problem Analysis Variable and Model Significance Sigmoidal Function

      Maximum Likelihood Concept Null Vs Residual Deviance Cost Function Formation Mathematical Modelling


      Model Parameter Significance Evaluation Accuracy, recall, precision and F1 Score Drawing the ROC Curve

      Estimating the Classification Model Hit Ratio Isolating the Classifier for Optimum Results

      Project: Predictive Analysis using Logistic Regression


      Assignment 4

      Module 10

      KNN and Decision Tree Duration: 4 Hours

      K Nearest Neighbour Understanding the KNN Distance metrics

      Case Study on KNN Example with Python


      Decision Trees

      Forming Decision Tree Components of Decision Tree Mathematics of Decision Tree

      Variance – Decision Tree for Regression

      Gini Impurity, Chi Square – Decision Tree for Classification Decision Tree Evaluation

      Module 11 Decision Tree and Random Forest


      Duration: 4 Hours

      Decision Tree

      Practical Examples & Case Study

      Project: Financial Prediction with Decision Tree


      Random Forest




      Bag of Trees

      Random Forest Mathematics

      Examples & use cases using Random Forests Case Study:

      Bank Marketing Analysis Customer Churn Analysis


      Assignment 5

      Module 12 Artificial Neural Networks


      Duration: 4 Hours

      Neurons, ANN & Working Single Layer Perceptron Model Multilayer Neural Network Feed Forward Neural Network Cost Function Formation

      Applying Gradient Descent Algorithm

      Backpropagation Algorithm & Mathematical Modelling Programming Flow for backpropagation algorithm

      Use Cases of ANN

      Programming SLNN using Python Programming MLNN using Python

      Project – Predictive Analysis with Neural Networks

      Module 13 Support Vector Machines


      Duration: 4 Hours

      Concept and Working Principle Mathematical Modelling Optimization Function Formation Slack Variable

      The Kernel Method and Nonlinear Hyperplanes Use Cases

      Programming SVM using Python

      Project - Character recognition using SVM

      Module 14

      Image Processing with Opencv


      Duration: 4 Hours

      Image Processing with Opencv

      Image Acquisition and manipulation using opencv Video Processing

      Edge Detection Corner Detection Face Detection

      Image Scaling for ANN

      Face Detection in an image frame Object detection

      Training ANN with Images Character Recognition




      Assignment 6

      Module 15

      Time Series Prediction Duration: 4 Hours

      Definition of Time Series Time Series Decomposition

      Simple Moving Average Method Weighted Moving Average Method Single Exponential Smoothing Method Double Exponential Smoothing Method Triple Exponential Smoothing Method Stationarity of Data

      ARIMA Models

      Module 16 Unsupervised Learning – Clustering


      Duration: 4 Hours


      Application of clustering DBSCAN

      Hierarchical Clustering K Means Clustering

      Use Cases for K Means Clustering Programming for K Means using Python

      Image Color Quantization using K Means Clustering Technique Customer segmentation using KMeans

      Cluster Size Optimization vs Definition Optimization Projects & Case Studies

      Module 17

      Principal Component Analysis and Anomaly Detection


      Duration: 4 Hours

      Principal Component Analysis Dimensionality Reduction, Data Compression Curse of dimensionality

      Multicollinearity Factor Analysis

      Concept and Mathematical modelling Use Cases

      Programming using Python


      Anomaly Detection Moving Average Filtering Mean, Standard Deviation

      Statistical approach for Anomaly Detection OneClass SVM for Anomaly Detection Isolation Forest for Anomaly Detection Hands on project on Anomaly Detection Do’s and Don’ts for Anomaly Detection




      Assignment 7

      Module 18 Natural Language Processing


      Duration: 4 Hours

      Natural Language Processing & Generation Semantic Analysis and Syntactic Analysis Text Cleaning and Preprocessing using Regex Using NLTK & Textblob

      Basic Text data processing

      Tokenization, Stemming and Lemmatization Pos Tagging

      Tf-IDF, count vector and Word2vec Sentiment Analysis

      Using Google, Bing and IBM Speech to Text APIs Project: Streaming live tweets and Sentiment Analysis Wordcloud

      Project: Building an Email Classification Model Chatbots

      Building Chatbots using Dialog Flow and Facebook Messenger Facebook Messenger API Integration

      Project: Building a utility based chatbot


      Assignment 8

      Module 19 Recommendation Systems


      Duration: 4 Hours

      Introduction to Recommendation System Popularity based Filtering

      Content based Filtering Collaborative Filtering Examples and Use cases

      Project: Movie Recommendation System

      Module 20

      Working with Tensorflow and Theano


      Duration: 4 Hours

      Introduction to TensorFlow & Theano The Programming Model

      Data Model, Tensor Board

      Working with constants, variables and placeholders Linear Regression using Tensorflow

      Logistic Regression using Tensorflow Tensorflow low level APIs

      Data manipulation using Tensorflow Working with Theano

      Building Linear Regression and Logistic Regression with Theano

      Examples and use cases



      Module 21 Neural Network Revisiting


      Duration: 4 Hours

      Activation Functions for Neural Networks Optimization Techniques – SGD, ADAM, LBFGS Regularization

      Momentum in Neural Networks

      Neural Network Tuning and Performance Optimization


      Introducing Feed Forward Neural Nets Softmax Classifier & ReLU Classifier Dropout Optimization

      Back propagation Neural networks with Tensorlfow Deep Neural Networks using Tensorflow


      Assignment 9

      Module 22

      Bagging and Boosting Duration: 4 Hours

      Gradient Boosting Methods GBM – idea and beefits XGBoost

      LightGBM CatBoost

      Module  23 Deep Learning Introduction and

      Convolutional Neural Networks


      Duration: 4 Hours

      Convolutional Neural Networks CNN Architecture

      Convolution Process MaxPooling, dropout Maths behind CNNs Feature Extraction

      Variants of the Basic Convolution Function Efficient Convolution Algorithms

      The Neuroscientific Basis for Convolutional Networks Variety of Convolutional Networks

      Implementing CNNs using Keras

      MNIST Data – Digit Classification using CNN


      Assignment 10

      Module 24 Recurrent Neural Networks


      Duration: 4 Hours

      Recurrent Neural Networks Basic concepts of RNN

      Unfolding Recurrent Neural Networks The Vanishing Gradient Problem

      The Exploding Gradient Problem LSTM Networks

      Recursive Neural Networks




      Case study

      Basic Time Series Forecasting using LSTM Bitcoin Prices prediction using LSTM Airlines Volume Prediction using LSTM

      Module 25 Recurrent Neural Networks


      Duration: 4 Hours

      LSTM for NLP

      Word Embedding and LSTM Text Classification using LSTM

      Project: IMDB Feedback classification Word2vec

      Word Embedding

      Text Classification using LSTM Text Summarization using LSTMs Concept and methods

      Sequence to Sequence Model using LSTMs


      Assignment 11

      Module 26 Autoencoders & RBM – Concept, Mathematics, Programming & Example


      Duration: 4 Hours

      Autoencoders, RBM Introducing Autoencoders

      Representational Power, Layer Size and Depth Stochastic Encoders and Decoders

      Improving Autoencoders Case study


      Restricted Boltzmann Machines Maths behind RBM

      Concept of Boltzman Machine Programming RBM


      Self-Organizing Maps Example and Use cases

      Programming SOMs using Keras


      Assignment 12

      Module 27 and 28



      Duration: Last 11 Days participants will be guided in project


      Project #1:


      Industry: Social Media

      Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters.


      Actions to be performed:

      Load the corresponding dataset. Perform data wrangling visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your goal. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest.



      Project #2:


      Industry: FMCG

      Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.


      Actions to be performed:

      You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principa components which explain the max variance.






Course Name: Artificial Intelligence with Machine Learning & Deep Learning









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All participants will get ISO certified Certificate of the course from Teach Tech Services in association with iSmriti, IIT Kanpur

This certificate is globally accepted.


Address :Jalandhar - Delhi G.T. Road, Phagwara, Punjab 144411

Phone : +91-9023647226

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