
Instructor
Manaranjan Pradhan

Category
ML and DL using python

Course Fees
Quotation on request Rs.
Course name : Machine Learning Deep Learning using Python 5 Days
MACHINE LEARNING AND DEEP LEARNING USING PYTHON
Machine learning is one of the fastestgrowing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for machine learning and deep learning models, and design intelligent systems that learn from complex and/or largescale datasets.
We will introduce the basic concepts of statistics and machine learning models. We’ll also demonstrate how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced with working examples. You will also be introduced to H2O package to build a robust and scalable machine learning and deep learning model. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.
WHO SHOULD ATTEND
Irrespective of type of industry (retail, ecommerce, manufacturing, real estate & construction, telecom, hospitality, banking, healthcare, IT, supply chain &logistic, etc.); data forms the crux of decision making. This course is designed for graduates and post graduates who will venture into the corporate set up and will be assisting the management in various decision making process. This course is equally suited to hone up analytical skills and business acumen of midlevel and senior level corporate professionals trying to understand the nuances of data science and help them the machine learning techniques an efficient way to generate insights for customers which in turn optimizes the bottom line of organizations.
HARDWARE AND SOFTWARE
 Participants should bring their laptop (preferably Windows 7 or higher/ Mac OS installed).
 Operating System (any of the following):
 Mac OS X with XQuartz
 Windows (Version XP or later) is required.
 Minimum 8 GB RAM on the system is advisable. 16GB RAM is preferable.
INSTALLATIONS:
 For Windows, go to https://docs.anaconda.com/anaconda/install/windows.html
 For MacOS, go to https://docs.anaconda.com/anaconda/install/macos
 For Linux, go to https://docs.anaconda.com/anaconda/install/linux
More about anaconda can be found at https://docs.anaconda.com. Participants are expected to resolve any installation issues of the software prior to the commencement of the session.
PREREQUISITE & COURSE DELIVERABLE
 Participants should have basic programming skills. Participants are expected to spend time with the code set as a home assignment to leverage the classroom training hours to the fullest.
 High speed internet connection will be provided at the training venue.
 Deliverable: Python code and dataset. Soft copy of the content being covered (PDF file)
COURSE OUTLINE
Day 1: Understanding Anaconda Framework platform and other useful packages in Python
Session 1–Introduction to Business Analytics
 What is Business Analytics
 Why is it needed and how industries are adopting it
 Different components of analytics
 Applications of analytics in different domains
 Statistical learning vs. Machine learning
 Artificial Intelligence, Machine Leaning and Deep Learning
 What is Data Science and skills of a data scientist
 Different types of machine learning algorithms–Supervised, Unsupervised and Reinforcement learning
Session 2 & 3–Introduction to Anaconda and Python
 Overview of Anaconda framework
 Python – Variables, objects, loops, conditions, function.
 Python Data structures – lists, tuples, dictionaries, sets
 Introduction to Numpy – ndarrays, ndarrays indexing, ndarrays datatypes and operations, statistical sorting and set operation
 Introduction to Pandas – Data ingestion, descriptive statistics, visualization, frequent data operations, merging dataframes, parsing timestamps
 Introduction to visualization – Matplotlib (Optional)
 Introduction to H2O package, AutoML, Driverless AI (Optional)
Session 4 & 5–Basics of Statistics
 Random Variable – Discrete and Continuous
 Probability density function and Cumulative density function
 Distribution Family – Gaussian Distribution, Standard Normal Distribution
 Population and Sample
 Central limit theorem
 Demonstration of Central limit theorem on finance data
 Hypothesis testing – Z test, t test, test for proportion, analysis of variance (ANNOVA)
 Covariance and Correlation
Day 2: Understanding regression and logistic regression and its implementation using Python
Session 1, 2 & 3–Lab 1: Linear Regression
 Introduction to simple and multiple linear regression
 Regression diagnostic–Rsquared, ttest, Ftest, error terms distribution, heteroscedasticity, identifying multicollinearity and handling, AIC  model selection strategy
 Common task framework for model evaluation – training and test set.
 Case study using regression techniques and handson using Python code for regression
Session 4&5–Lab 2: Logistic Regression
 Introduction to logistic regression
 Logistic regression diagnostic: Wald statistics, Hosmer Lemeshow test, Classification Matrix, Sensitivity, Specificity, ROC Curve, precision, recall, F1score
 Strategy to find the optimal cutoff
 Bias and variance in the model, Bias vs. variance tradeoff
 Case study using logistic regression techniques and handson using Python code for regression
Day 3: Understanding supervised learning and gradient descent algorithm
Session 1 –Lab 2: Logistic Regression
 Introduction to logistic regression
 Logistic regression diagnostic: Classification Matrix, Sensitivity, Specificity, ROC Curve, precision, recall, F1score
 Strategy to find the optimal cutoff , cost of misclassification, F Score strategy
 Bias and variance in the model, Bias vs. variance tradeoff
 Case study using logistic regression techniques and handson using Python code for regression
Session 2–Lab 3: Decision Trees
 Decision tree – Classification and regression trees (CART), Gini Index, Entropy
 Decision tree – Chisquare automatic interaction detection (CHAID)
 Case study using decision tree techniques
 Handson using Python code
Session 3 & 4–Lab 4: Other Machine learning models (Ensemble Methods)
 What is Machine learning
 Different sampling strategies–Bootstrapping, Up–Sample, Down–Sample, Synthetic Sample, Cross–Validation Data
 Introduction to Bagging–Random Forest, Other Bagging algorithms
 Introduction to Boosting– Adaptive boosting, Other Boosting algorithms
 Case study of an imbalanced data and application of sampling strategies & ensemble methods
 Handson using Python code on an imbalanced data
Session 5 –Lab 5: Introduction to Gradient Descent
 Hypothesis formulation for linear regression
 Deriving the cost function for linear regression
 Cost function– Intuition for linear regression with one parameter and two parameters
 Gradient descent algorithm–application in linear regression
 Hypothesis formulation for logistic regression
 Deriving the cost function for logistic regression
 Cost function– Intuition for logistic regression
 Gradient descent algorithm–application in logistic regression
 Handson using Python code to implement gradient descent for regression and logistic regression
Day 4: Understanding Deep Learning concepts and libraries
Session 1 – Introduction to Deep Learning
 Understanding how machines learn
 Understanding perceptron and multiplayer Neural Networks
 Overview of Deep Learning Frameworks: Keras & Tensorflow
Session 2, 3 & 4 – Deep Dive into Deep Learning
 Understanding Gradient Descent: Minibatch and Stochastic
 Overview Back Propagation, Cost Function & Optimizers
 Writing a Deep Learning Algorithm from Scratch using Tensorflow
 Developing a deep learning model using Keras
 Developing a Regression & Classification Model using NN
 NN Network Architecture & it’s hyperparameters
Session 5  Convolutions Neural Networks
 Understanding Convolutions, Filters, Max Pooling, Dropouts
 Understanding Image Classification
 Overview of different NN architectures – VGG Net, Lenet, googLenet
 Building a CNN Model using Keras
 Tensorboard for monitoring
Day 5: Understanding Deep Learning concepts and libraries
Session 1  Convolutions Neural Networks
 Understanding Convolutions, Filters, Max Pooling, Dropouts
 Understanding Image Classification
 Overview of different NN architectures – VGG Net, Lenet, googLenet
 Building a CNN Model using Keras
 Tensorboard for monitoring
Session 2, 3 & 4: Sequence Models
 Recurrent Neural Networks
 Natural Language Processing and Word Embeddings
 Sequence Models
 Natural Language Processing  Building an RNN Model using Keras
Session 5: Model Evaluation
 Creating Training, validation and Test Data Sets
 Cross validations
 Understanding Evaluation Metrics: RMSE, Rsquare, Confusion Matrix, Precision, Recall, Accuracy etc.
COURSE SCHEDULE
Day 1: Understanding Anaconda Framework platform and other useful packages in Python
This day will be about basic concepts in Python and statistics
Topic 
Session 
From 
To 
Introduction to business analytics 
1 
9 AM 
10:15 AM 
Introduction to Anaconda framework and Python 
2 
10:30 AM 
11:45 AM 
Introduction to Anaconda framework and Python…cont. 
3 
12:00 PM 
1:15 PM 
Introduction to statistics 
4 
2:15 PM 
3:30 PM 
Introduction to statistics…cont. 
5 
3:45 PM 
5:00 PM 
Day 2: Understanding regression and logistic regression and its implementation using Python
This day will be about underlying concepts of regression and logistic regression
Topic 
Session 
From 
To 
Lab 1: Linear Regression 
1 
9 AM 
10:15 AM 
Lab 1: Linear Regression…cont. 
2 
10:30 AM 
11:45 AM 
Lab 1: Linear Regression…cont. 
3 
12:00 PM 
1:15 PM 
Lab 2: Logistic Regression 
4 
2:15 PM 
3:30 PM 
Lab 2: Logistic Regression…cont. 
5 
3:45 PM 
5:00 PM 
Day 3: Understanding supervised learning and gradient descent algorithm
This day will be about underlying concepts of advanced machine learning algorithms
Topic 
Session 
From 
To 
Lab 2: Logistic Regression…cont. 
1 
9 AM 
10:15 AM 
Lab 3: Decision Trees 
2 
10:30 AM 
11:45 AM 
Lab 4: Ensemble Methods 
3 
12:00 PM 
1:15 PM 
Lab 4: Ensemble Methods…cont. 
4 
2:15 PM 
3:30 PM 
Lab 5: Introduction to Gradient Descent 
5 
3:45 PM 
5:00 PM 
Day 4: Understanding of Neural Networks, Deep Learning and Libraries
This day will be about underlying concepts of neural networks and CNN
Topic 
Session 
From 
To 
Introduction to Deep Learning 
1 
9 AM 
10:15 AM 
Deep dive into Deep Learning 
2 
10:30 AM 
11:45 AM 
Deep dive into Deep Learning…cont. 
3 
12:00 PM 
1:15 PM 
Deep dive into Deep Learning…cont. 
4 
2:15 PM 
3:30 PM 
Convolutions Neural Networks 
5 
3:45 PM 
5:00 PM 
Day 5: Advanced Concepts of Deep Learning and Applications
Application driven advanced concepts of RNN and model evaluation in Deep Learning
Topic 
Session 
From 
To 
Convolutions Neural Networks…cont. 
1 
9 AM 
10:15 AM 
Sequence Models 
2 
10:30 AM 
11: 45 AM 
Sequence Models…cont. 
3 
12:00 PM 
1:15 PM 
Sequence Models…cont. 
4 
2:15 PM 
3:30 PM 
Model Evaluation 
5 
3:45 PM 
5:00 PM 
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