Data Science for Managers

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ML DL with Python

Course Overview

Clear algorithm explanations that help you to understand the principles that underlie each technique.
Quick mathematical primer for each algorithm on white-board to show you exactly how each model learns.
Real worked examples so that you can see exactly the numbers in and the numbers out, there’s nowhere for the details to hide.

Modes of Training

Classroom

Day 1: Python Programming

Python programming environment, installing and upgrading required packages for Machine Learning and Deep Learning.
Python data types
Python Fast Track (NumPy & SciPy packages).
Python Programming constructs, loops, conditional statements etc..
How to write Python functions, modules and packages.

Data Fetching and Visualisation

Pandas library, Data importing, working with dataFrame objects
Understanding data (statistically)
Visualizing your data using matplotlib and Seaborn library.
Feature Selection by data visualisation and statistical analysis.

Day 2: Deep-Dive into Scikit-Learn Package for Machine Learning

Descriptive & Inferential Statistics using statsmodels
Data preprocessing, (Rescaling, Normalization, Standardization, One-Hot encoding etc..),
Feature Section methods in sklearn package.

Model Performance and Comparison

Different Performance Metrics used in Machine Learning Algorithms,
K-fold Cross-Validation techniques.
Implementing Classification and Regression Algorithms in actual datasets.
Comparison of these algorithms.
Some case studies.

Machine Learning Algorithms which will be discussed on Days 2-3:

Linear Regression using Analytical solution (OLS) and Stochastic Gradient Descent: Problem Formulation, derivation and application to toy datasets for each algorithm below.
Same for Logistic Regression & Linear Discriminant Analysis.
Generalised Linear Models (GLMs)
Support Vector Machines (SVMs) for Classification & regression
Naïve Bayes Classifier, and Gaussian Naïve Bayes Classifier.
Decision Trees: Calculations of Gini Index, Entropy and Information Gain to decide root and branch splitting criterion for features. Random Forests.
K-Means Clustering and regression; and K-mode for classifications
Dimensionality Reduction using PCA

Day 3: Regularisation and Ensemble Methods in Machine Learning

All types of Regularization methods (Ridge, Lasso, ElasticNetetc..)
Use of Lasso Regularisation in Feature Selection
Clustering techniques in Machine learning problems.
Hyper-parameter tuning of different machine learning algorithms using cross-validations
Using Ensemble methods (Bagging and Boosting)
Improving the performance of models by tuning algorithm parameters using grid-search CV
Using Pipelines to automate machine learning workflows.
Finalizing your model with using pickle and joblib packages in Python and deployment.

Day – 4: Introduction to Deep Learning & Neural Networks

Setting Up Environment for Deep Learning: Keras, Tensorflow, Jupyter etc
Theoretical Foundations of Deep Learning, Deep Learning vs Machine Learning
Deep Learning history, biological inspirations and demo with MNIST dataset to start with.
Understanding Neural Network, How neural networks learn,
Architecture of Neural Networks
Activations Functions: Sigmoid, Tanh, Softmax, Softmax crossentropy, Sigmoid Crossentropy
Basic ANN Types: Dense Neural Networks, Convolution Neural Networks, Recurrent Neural Networks
CNN: Deep-dive, Overfitting, Decaying Leaning, Dropout
CNN Project – finding presence of a certain class of object in images.

Day – 5: Deep Learning Projects

Recurrent Neural Networks: LSTM, GRU CELL
Time Series Analysis using LSTM, with and without windowing
RNN Project: Project – Toxic comment detector
NLP Basics and Feature Extraction in Textual data
LSTM Project: Sentiment Analysis
Other Deep Learning Projects, as time permits

Course Objectives

What are the Objectives of Data Science Online Training ?

“After completing this course, the candidates will be able to:

Gain in-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
Gain a comprehensive understanding of distinct data transformation tools and techniques
Implement Text Mining on text data
Gain insights into Data visualization and optimization techniques”

Why should you learn Data Science to grow your career?

“Marketsandmarkets.com stated that the advanced analytics market would be worth $39.56 Billion by next year.
Wired.com, in a recent Glassdoor report, pointed that a skilled data scientist earns $128,709 per annum.
According to Randstad, the pay hikes in the analytics industry are 50% higher than its competitors.”

Who should learn Data Science?

“Getting certified in Data Science is beneficial for the following job roles:
Big Data, Analyst and Business Intelligence Professionals
Machine Learning Professionals
Predictive Analytics and Information Architects
Big Data Statisticians
Individuals seeking a Data Science career”

What are the prerequisites for the Data Science course?

Anyone interested in learning data science can take this training. Knowledge of Mathematics and Statistics is beneficial.

What will you learn in this Data Science training?

“After completing this training, the learners will be in a position to master the following areas:

Roles and responsibilities of a Data Scientist
Machine Learning algorithms
Integrating R with the Hadoop ecosystem
Linear and logistic regression
Clustering, analysis segmentation, and prediction
Deploying recommender systems
Data interpretation, plotting techniques, and sampling methods”

ML with Python

Course Overview

Modes of Training

Classroom

Day 1: Python for Data Science: Basics, Data Fetching and Visualisation

Python programming environment, installing and upgrading required packages for Machine Learning and Deep Learning.
Python data types
Python Fast Track (NumPy & SciPy packages).
Python Programming constructs, loops, conditional statements etc..
How to write Python functions, modules and packages.
Pandas library, Data importing, working with dataFrame objects
Understanding data (statistically)
Visualizing your data using matplotlib library.
Feature Selection by data visualisation and statistical analysis.

Day 2: Machine Learning Algorithms which planned to be discussed:

Machine Learning basics
Linear Regression using Analytical solution (OLS) and Stochastic Gradient Descent: Problem Formulation, derivation and application to toy datasets for each algorithm below.
Same for Logistic Regression & Linear Discriminant Analysis.
Support Vector Machines (SVMs) for Classification & regression
Naïve Bayes Classifier, and Gaussian Naïve Bayes Classifier.
Decision Trees: Calculations of Gini Index, Entropy and Information Gain to decide root and branch splitting criterion for features. Random Forests.
K-Means Clustering and regression; and K-mode for classifications

Day 2 – 3 : Deep-Dive into Scikit-Learn Package for Machine Learning

Data preprocessing, (Rescaling, Normalization, Standardization, One-Hot encoding etc..),
Feature Section methods in sklearn package.

Model Performance and Comparison

Different Performance Metrics used in Machine Learning Algorithms,
K-fold Cross-Validation techniques.
Implementing Classification and Regression Algorithms in actual datasets.
Comparison of these algorithms.
Some case studies.

Regularisation and Ensemble Methods in Machine Learning

All types of Regularization methods (Ridge, Lasso, ElasticNet etc..)
Use of Lasso Regularisation in Feature Selection
Clustering techniques in Machine learning problems.
Hyper-parameter tuning of different machine learning algorithms using cross-validations
Using Ensemble methods (Bagging and Boosting)
Improving the performance of models by tuning algorithm parameters using grid-search CV
Using Pipelines to automate machine learning workflows.

Course Objectives

What are the Objectives of Data Science Online Training ?

After completing this course, the candidates will be able to:

Gain in-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
Gain a comprehensive understanding of distinct data transformation tools and techniques
Implement Text Mining on text data
Gain insights into Data visualization and optimization techniques

Why should you learn Data Science to grow your career?

Marketsandmarkets.com stated that the advanced analytics market would be worth $39.56 Billion by next year.
Wired.com, in a recent Glassdoor report, pointed that a skilled data scientist earns $128,709 per annum.
According to Randstad, the pay hikes in the analytics industry are 50% higher than its competitors.

Who should learn Data Science?

Getting certified in Data Science is beneficial for the following job roles:

Big Data, Analyst and Business Intelligence Professionals
Machine Learning Professionals
Predictive Analytics and Information Architects
Big Data Statisticians
Individuals seeking a Data Science career

What are the prerequisites for the Data Science course?

Anyone interested in learning data science can take this training.
Knowledge of Mathematics and Statistics is beneficial.

What will you learn in this Data Science training?

After completing this training, the learners will be in a position to master the following areas:

Roles and responsibilities of a Data Scientist
Machine Learning algorithms
Integrating R with the Hadoop ecosystem
Linear and logistic regression
Clustering, analysis segmentation, and prediction
Deploying recommender systems
Data interpretation, plotting techniques, and sampling methods

Date	Price	duration	Start Time-End Time	Instruction Language	Buy Now
10-Dec-2019	Rs.10,000	5	09:00 AM - 17:00 PM	en	Buy Now
10-Dec-2019	Rs.10,000	5	09:00 AM - 17:00 PM	en	Buy Now