Trainer Name : Dhanush 13 Years Exp

About The Instructor

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Dhanush is an IT professional with 14+ years of Experience. He worked on various technologies.

He got 10+ years of experience in practice.He is very enthusiastic trainer providing best service to his students.

Course Schedules

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What Will I Learn?

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Description

About the Course

Data Science is the study of the generalizable extraction of knowledge from data. Being a data Scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and programming languages along with a good understanding of the craft of problem formulation to engineer effective solutions.

This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset.

- Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. - The focus in the treatment of these topics will be a balanced approach on breadth and depth, and emphasis will be placed on integration and synthesis of concepts and their application to real time problems. - To make the learning contextual, real datasets from a variety of disciplines will be used...

Curriculum For This Course

139 Lessons

Introduction to Data Science

1

- Data Science roles – functions, pay across domains, experience

Business Statistics

7

- What is data science?
- How is data science different from BI and Reporting? What is difference between AI, Data Science, Machine Learning, Deep Learning Job Land scape and Preparation Time
- Who are data scientists?
- Who are data scientists?
- What is day to day job of Data Scientist
- What kind of projects they work on?
- End to End Data Science Project Life Cycle

Introduction to statistics

5

- Summarizing Data
- Data Visualization
- Probability basics
- Parametric and Non parametric Statistical Tests
- ANOVA

Artificial Intelligence – Machine Learning Introduction

30

- Probability Distributions
- Expected value and variance
- Discrete and Continuous
- Bernoulli Distribution
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Empirical Rule
- Chebyshev’s Theorem
- Sampling methods and Central Limit Theorem
- Overview
- Random sampling
- Stratified sampling
- Cluster sampling
- Central Limit Theorem
- Hypothesis Testing
- Type I error
- Type II error
- Null and Alternate Hypothesis
- Reject or Acceptance criterion
- P-value
- Confidence Intervals
- Introduction to Machine Learning
- What is Machine Learning?
- Statistics (vs) Machine Learning
- Types of Machine Learning
- ‘f’ Test
- ‘z’ Test
- ‘t’ Test
- Chi-Square test - Reinforcement Learning

Artificial Intelligence – Supervised Machine Learning

2

- Optimization
- Gradient Descent (Batch and Stochastic)

Artificial Intelligence – Unsupervised Machine Learning

36

- Classification
- Nearest Neighbor Methods (knn)
- Logistic
- Tree based Models – Decision Tree
- Basics
- Classification Trees
- Regression Trees
- Probabilistic methods
- Bayes Rule
- Naïve Bayes
- Regression Analysis
- Simple Linear Regression
- Assumptions
- Model development and interpretation
- Sum of Least Squares
- Model validation
- Multiple Linear Regression
- Regression Shrinkage Methods
- Lasso
- Ridge
- Advanced Models – Black Box
- Support Vector Machine
- Neural Networks
- Ensemble Models
- Bagging
- Boosting
- Random Forest
- Gradient Boosting
- Association Rules (Market Basket Analysis)
- Apriori
- Dimensionality Reduction
- Principal Component Analysis
- K-fold Cross Validation
- Text Modelling
- POS tagging
- TFIDF and Classification

Model Validation

1

- K-fold Cross Validation

Artificial Intelligence – Natural Language Processing

2

- Text Modelling
- TFIDF and classification

Artificial Intelligence – Deep Learning

1

- Comparison between DL and ML performances over the MNIST dataset

R Programming Language

23

- Cluster Analysis
- Hierarchical clustering
- K-Means clustering
- Confusion Matrix and its metrics
- ROC Curve (AUC)
- R Squared
- R Squared
- Adjusted R Squared
- Root Mean Square Error (RMSE)
- Introduction to Natural Language Processing
- Sentiment Analysis
- Text Similarity
- Text Preprocessing
- Tokenization
- Stemming
- Lemmatization
- Sigmoid, Depth vs Width tradeoffs
- Convolutional networks
- Concepts of filters
- Sliding
- Pooling and Padding
- Data Visualization using ggplot2
- Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc.

Python Programming Language

31

- Introduction
- R Overview
- Installation of R and RStudio software
- Important R Packages
- Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames Decision making & Loops
- If-else, while, for
- Next, break. try-catch Functions
- Writing functions
- Nested functions Built-in functions
- Vapply, Sapply, Tapply, Lapply etc.
- Data Preparation/Manipulation
- Reading and Writing Data
- Summarize and structure of data
- Exploring different datasets in R
- Sub Setting Data Frames
- String manipulation in Data Frames
- Handling Missing Values, Changing Data types, Data Binning Techniques, Dummy Variables
- Introduction. How is Python different from R
- Installing Anaconda- Python
- Setting up with spyder
- Datatypes in Python
- Importing modules
- Importing modules
- String manipulation
- Control loops:
- For ,While , If else
- Functions : Lambda , apply, Numpy, Pandas.
- Introduction to Dataframes
- Conversion of written R codes into python
- Scikit-Learn - Machine Learning in Python
- Matplotlib

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