Artificial Intelligence with Machine Learning & Deep Learning
Artificial Intelligence with Machine Learning & Deep Learning Classroom InstructorLed 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
Clustering
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
TfIDF, 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
SelfOrganizing Maps Example and Use cases
Programming SOMs using Keras
Assignment 12
Module 27 and 28
Projects
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
Student
8,500
6000
14,500
Professional
9,500
6,500
16,000
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Certification:
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.
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