Data science/machine learning with python 2019-08-28T12:09:31+00:00

Data Science & Machine Learning with Python (DSMLP)

Get familiar with Python using Data science & machine learning Techniques.

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About Course

Data Science and Machine Learning with Python – Python is a programming language that lets you work more quickly with high accuracy and integrate your systems more effectively. Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.


Module 01: Introduction to Data Science:

  • Introduction to Data Science
  • Life cycle of data science
  • Skills required for data science
  • Career path in data science
  • Applications of data science

Module 02: Statistics in Data science:

  • Introduction to Data:
    • Data types
    • Data Collection Techniques
  • Descriptive Statistics:
    • Measures of Central Tendency
    • Measures of Dispersion Measures of Skewness and Kurtosis
    • Visualization
  • Inferential Statistics:
    • Sampling variability and Central Limit Theorem
    • Confidence Interval for Mean Hypothesis Testing, t- Test, F-Test, Chi-square Test
    • ANOVA
  • Random Sampling and Probability Distribution:
    • Probability and Limitations, Discrete Probability, Continuous Probability
    • Bernoulli, Binomial Poisson distribution and Normal Distribution

Module 3: Statistical Learning:

  • What Is Statistical Learning?
  • Why Estimate f?
  • How Do We Estimate f?
  • The Trade-Off Between Prediction Accuracy and Model Interpretability
  • Supervised Versus Unsupervised Learning
  • Regression Versus Classification Problems
  • Assessing Model Accuracy
  • Measuring the Quality of Fit
  • The Bias-Variance Trade-Off

1. Linear Regression:

  • Simple Linear Regression:
    • Estimating the Coefficients
    • Assessing the Accuracy of the Coefficient Estimates
    • Assessing the Accuracy of the Model

2. Multiple Linear Regression:

  • Estimating the Regression Coefficients
  • Some Important Questions
  • Other Considerations in the Regression Model
  • Qualitative Predictors
  • Interaction Terms
  • Non-linear Transformations of the Predictors
  • Extensions of the Linear Model
  • Potential Problems

3. Classification:

  • An Overview of Classification
  • Why Not Linear Regression

4. Logistic Regression:

  • The Logistic Model
  • Estimating the Regression Coefficients
  • Making Predictions
  • Multiple Logistic Regression
  • Logistic Regression for >2 Response Classes

5. Resampling Methods:

  • Cross-Validation:
    • The Validation Set Approach
    • Leave-One-Out Cross-Validation
    • k-Fold Cross-Validation
    • Bias-Variance Trade-Off for k-Fold
    • Cross-Validation
    • Cross-Validation on Classification Problems
    • The Bootstrap

6. Linear Model Selection and Regularization:

  • Subset Selection
  • Best Subset
  • Selection Stepwise
  • Selection Forward and Backward Stepwise Selection
  • Choosing the Optimal Model

7. Shrinkage Methods:

  • Ridge Regression
  • The Lasso Regression K-Nearest Neighbor

Module 4 : Deep dive into Machine Learning:

Tree-Based Methods

1. Basics of Decision Trees:

  • Regression Trees
  • Classification Trees
  • Trees Versus Linear Models
  • Advantages and Disadvantages of Trees

Bagging, Random Forests, Boosting:

  • Bagging
  • Random Forests
  • Boosting

2. Support Vector Machines:

  • Hyperplane
  • The Maximal Margin Classifier
  • Support Vector Classifiers
  • Support Vector Machines
  • Kernel Trick
  • Gamma, Cost and Epsilon
  • SVMs with More than Two Classes

Module 5: Python for Data Science:

1.Python programming:

  • Environment Setup
  • Jupyter Notebook Overview
  • Data types:
    • Numbers,Strings
    • Printing, Lists
    • Dictionaries, Booleans,Tuples ,Sets
    • Comparison Operators
    • if, elif, else Statements

2. Loops:

  • for Loops, while Loops
  • range()
  • list comprehension
  • functions

3. Python for Exploratory Data Analysis:

  • Numpy
  • Pandas

4. Python for Data Visualization:

  • Matplotlib
  • Seaborn
  • Pandas built in visualization

Module 6: Unsupervised Learning:

The Challenges of Unsupervised Learning

1.Principal Components Analysis:

  • What Are Principal Components
  • Another Interpretation of Principal Components
  • More on PCA
  • Other Uses for Principal Components

2. Clustering Methods:

  • K-Means Clustering
  • Hierarchical Clustering
  • Practical Issues in Clustering

Module 7: Association Rules Mining and Times Series Analysis

1. Association Rules Mining:

  • Market Basket Analysis
  • Apriori/Support/Confidence/Lift

2. Time Series Analysis:

  • What is Times Series Data?
  • Stationarity in Time Series Data
  • Augmented Dickey Fuller Test
  • The Box-Jenkins Approach
  • The AR Process
  • The MA Proces What is ARIMA?
  • ACF,PACF and IACF plots
  • Decomposition of Times Series
  • Trend, Seasonality and Cyclic
  • Exponential Smoothing | EWMA

Course Details

The Advanced certification program is delivered is the most pragmatic learning approach which is an interfusion of theoretical & practical learning to ensure the participants comprension is accurate.

• Technology infused learning
• 24*7 access to curriculum & access to case studies & data sets
• Guest Lectures by Industry experts
• Hackathons & Real time projects
• A most friendly & supportive environment

Case Studies:
Recommendation system for movies using KNN in Python

Examination results using Logistic Regression in Python

Text Analystics using Bag of words and multi-class classification in Python

Stochastic Gradient Descent method using Mini Batch Algorithm in Python

Hand written images using clustering in Python

Hand written digits using Auto Encoder Neural Network in Python

Mahabharata using Bag of words in Python

Image processing using KNN in Python

Weather and pollution analysis using Times Series in Python

Face Recognition using SVM in Python

Artificial Intelligence and Data Science

In 2012, Harvard Business Review named data scientist the “sexiest job of the 21st century.” More recently, Glassdoor named it the “best job of the year” for 2016.

“It isn’t a big surprise,” Dr. Andrew Chamberlain, Glassdoor’s chief economist, told Business Insider. “It’s one of the hottest and fastest growing jobs we’re seeing right now.”

python language

According to Glassdoor, data scientists earn a base pay of $116,840 a year, on average.

Here’s how much they take in, on average, at some of the hottest tech companies, according to Glass-Door’s employee salary reviews:
Apple: $149,963
LinkedIn: $138,798
Facebook: $133,841
Twitter: $134,861
Microsoft: $119,129
Airbnb: $117,229

The advanced certification program is perfect for the participants who are very keen on working towards analytics, automation, AI & to enhance their skillset in the most advanced technology in the world.

1. What is the pyhton language used for?
Python is a general purpose and high-level programming language. You can use Python for developing desktop GUI applications, websites, and web applications. Also, Python, as a high-level programming language, allows you to focus on the core functionality of the application by taking care of common programming tasks.

2. Why should I use Python?
Python is a high-level, interpreted and general-purpose dynamic programming language that focuses on code readability. The syntax in Python helps the programmers to do coding in fewer steps as compared to Java or C++. The Python is widely used in bigger organizations because of its multiple programming paradigms.

3. Is it easy to learn python?
While Python can be more user-friendly than Java, as it has a more intuitive coding style, both languages do have their unique advantages for developers and end users. However, if you are just beginning your path towards a programming career, you might want to start by learning Python, as it is less complex.

4. Which is better R or Python?
In a nutshell, he says, Python is better for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

5. Are Python & R similar?
Its syntax is more similar to other languages than R’s syntax is. Python can be read much like a verbal language. The real difference between Python and R comes in being production ready. Python is a full-fledged programming language and many organizations use it in their production systems.

6. How long does it take to learn Python?
On average, how long does it take for a newbie to learn the general fundamentals and functions of Python if it’s their first language? I’ve been learning python for about six months now. Not spending 8 hours a day on it but more as a hobby.

7. Is Python Used in Data science & machine learning?
Python is a general-use high-level programming language that bills itself as powerful, fast, friendly, open, and easy to learn. Facebook turns to the Python library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications.

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