Become master in data scientist with our Prime Training.

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Course Duration

60 Days

Training Options

Classroom / Online

**Tools Covered: **R, Python, Jupyter

**Pre-requisites: **Passion to learn data tools

**What you will learn: **Data Analytics, Data Visualization, ML foundation

- Customized curriculum as per industry needs.
- Training by real-time professionals.
- The best classroom and Lab infrastructure.
- Student friendly staff and management.
- 365 Days placement support with partner clients.

- Live demonstration of of features and practicals.
- Get LMS access of each online training session that you attend through GotoMeeting.
- Gain guidance on certification.
- Attend a Free Demo before signing up.

**Section 1**

- Introduction to Data Science
- Business Intelligence Vs Data Analysis
- Data Analysis Vs Data Scientist
- Data Scientist Roles.
- Different Disciplines of Data Science
- Machine Learning
- Natural Language Processing
- Deep Learning
- Artificial Intelligence

- When to use Machine Learning Models and Deep Learning Models.
- Applications of Machine Learning
- Why Machine Learning is the Future
- what are prerequisites for Data Science.
- Statistics
- R language essentials for Data Science
- Python essentials for Data Science

- [ Recorded Sessions of prerequisites will be given free of cost]
- different R language packages used for Data Science
- different Python modules used for Data Science
- Installing Python and Anaconda (Mac, Linux & Windows)
- Installing R and R Studio (Mac, Linux & Windows)

**Section 2: Data Preprocessing**

- Why need to preprocess data
- Importing the Libraries
- Importing the Dataset
- python : overview of Object-oriented programming: classes & objects
- Missing Data
- Categorical Data
- Splitting the Dataset into the Training set and Test set
- Feature Scaling
- Data Preprocessing Template!

**Section 3: Predictions using Regression **

- Linear Vs Non Linear Regression
- Types of Linear Regressions
- what is slope and intercept.
- How to Derive Simple Linear Regression coefficients.
- Dataset + Business Problem Description
- Simple Linear Regression Intuition – Step 1
- Simple Linear Regression Intuition – Step 2
- Simple Linear Regression in Python – Step 1
- Simple Linear Regression in Python – Step 2
- Simple Linear Regression in Python – Step 3
- Simple Linear Regression in Python – Step 4
- Simple Linear Regression in R – Step 1
- Simple Linear Regression in R – Step 2
- Simple Linear Regression in R – Step 3
- Simple Linear Regression in R – Step 4

**Section 4: Multiple Linear Regression**

- Dataset + Business Problem Description
- Multiple Linear Regression Intuition – Step 1
- Multiple Linear Regression Intuition – Step 2
- Multiple Linear Regression Intuition – Step 3
- Multiple Linear Regression Intuition – Step 4
- Prerequisites: What is the P-Value?
- Multiple Linear Regression Intuition – Step 5
- Multiple Linear Regression in Python – Step 1
- Multiple Linear Regression in Python – Step 2
- Multiple Linear Regression in Python – Step 3
- Multiple Linear Regression in Python – Backward Elimination – Preparation
- Multiple Linear Regression in Python – Backward Elimination – HOMEWORK !
- Multiple Linear Regression in Python – Backward Elimination – Homework Solution
- Multiple Linear Regression in Python – Automatic Backward Elimination
- Multiple Linear Regression in R – Step 1
- Multiple Linear Regression in R – Step 2
- Multiple Linear Regression in R – Step 3
- Multiple Linear Regression in R – Backward Elimination – HOMEWORK !
- Multiple Linear Regression in R – Backward Elimination – Homework Solution
- Multiple Linear Regression in R – Automatic Backward Elimination

**Section 5:**

- Polynomial Regression Intuition
- How to get the dataset
- Polynomial Regression in Python – Step 1
- Polynomial Regression in Python – Step 2
- Polynomial Regression in Python – Step 3
- Polynomial Regression in Python – Step 4
- Python Regression Template
- Polynomial Regression in R – Step 1
- Polynomial Regression in R – Step 2
- Polynomial Regression in R – Step 3
- Polynomial Regression in R – Step 4
- R Regression Template

**Section 6: Support Vector Regression**

- How to get the dataset
- SVR Intuition
- SVR in Python
- SVR in R

**Section 7: Decision Tree Regression**

- Decision Tree Regression Intuition
- How to get the dataset
- Decision Tree Regression in Python
- Decision Tree Regression in R

**Section 8: Random Forest Regression**

- Random Forest Regression Intuition
- How to get the dataset
- Random Forest Regression in Python
- Random Forest Regression in R

**Section 9: Evaluating Regression Models.**

- R-Squared Intuition
- Adjusted R-Squared Intuition
- Evaluating Regression Models Performance – Homework’s Final Part
- Interpreting Linear Regression Coefficients
- Developing Accuracy testing functions.

**Section 10: Classification Algorithms**

- What is a classification model.
- different types of classification models
- when to use what type of model
- how to measure accuracy of a classification model

**Section 11 : Logistic Regression**

- Logistic Regression Intuition
- How to get the dataset
- Logistic Regression in Python – Step 1
- Logistic Regression in Python – Step 2
- Logistic Regression in Python – Step 3
- Logistic Regression in Python – Step 4
- Logistic Regression in Python – Step 5
- Python Classification Template
- Logistic Regression in R – Step 1
- Logistic Regression in R – Step 2
- Logistic Regression in R – Step 3
- Logistic Regression in R – Step 4
- Logistic Regression in R – Step 5
- R Classification Template

**Section 12: K-Nearest Neighbors [knn]**

- K-Nearest Neighbor Intuition
- How to get the dataset
- K-NN in Python
- K-NN in R

**Section 13: Support Vector Machine [svm]**

- SVM Intuition
- How to get the dataset
- SVM in Python
- SVM in R

**Section 14: Kernel SVM**

- Kernel SVM Intuition
- Mapping to a higher dimension
- The Kernel Trick
- Types of Kernel Functions
- How to get the dataset
- Kernel SVM in Python
- Kernel SVM in R

**Section 15: Naive Bayes Classifier**

- Bayes Theorem
- Naive Bayes Intuition
- Naive Bayes Intuition (Challenge)
- Naive Bayes Intuition (Extras)
- How to get the dataset
- Naive Bayes in Python
- Naive Bayes in R

**Section 16: Decision Tree Classifier**

- Decision Tree Classification Intuition
- Entropy of Target variable
- Entropy of Input Variable on Target variable
- Information Gain
- How to get the dataset
- Decision Tree Classification in Python
- Decision Tree Classification in R

**Section 17: Random Forest Classifier**

- random Forest Classification Intuition
- how random forest works
- How to get the dataset
- Random Forest Classification in Python
- Random Forest Classification in R

**Section 18: Evaluating classification models performance**

- False Positives & False Negatives
- Confusion Matrix
- Accuracy Paradox
- CAP Curve
- CAP Curve Analysis

**Section 19: Clustering Algorithms**

- What is a unsupervised learning.
- how to use unsupervised for business problems
- different Clustering models.

**Section 20: K-means Clustering**

- K-Means Clustering Intuition
- K-Means Random Initialization Trap
- K-Means Selecting The Number Of Clusters
- How to get the dataset
- K-Means Clustering in Python
- K-Means Clustering in R

**Section 21: Hierarchical Clustering**

- Hierarchical Clustering Intuition
- Hierarchical Clustering How Dendrograms Work
- Hierarchical Clustering Using Dendrograms
- How to get the dataset
- HC in Python – Step 1
- HC in Python – Step 2
- HC in Python – Step 3
- HC in Python – Step 4
- HC in Python – Step 5
- HC in R – Step 1
- HC in R – Step 2
- HC in R – Step 3
- HC in R – Step 4
- HC in R – Step 5

**Section 22: Association Rule Mining**

- what are recommendation systems
- types of recommendation systems
- different ARM algorithms.
- how ARM used for recommendations.

**Section 23: Apriori algorithm [one of arm]**

- Apriori Intuition
- How to get the dataset
- Apriori in R – Step 1
- Apriori in R – Step 2
- Apriori in R – Step 3
- Apriori in Python – Step 1
- Apriori in Python – Step 2
- Apriori in Python – Step 3

**Section 24: Eclat algorithm [ARM]**

- Eclat Intuition
- How to get the dataset
- Eclat in R

**Section 25: FPGrowth algorithm [ARM]**

- FPGrowth Intuition
- problems with other ARM model.
- How to construct Growing tree.
- How to get the dataset
- FPGrowth in Python
- FPGrowth in R

**Section 26: Reinforcement Learning**

- What is Reinforcement Learning
- Models used in Reinforcement Learning

**Section 27: Upper Confidence Bound [ part of Reinforcement Learning]**

- The Multi-Armed Bandit Problem
- Upper Confidence Bound (UCB) Intuition
- How to get the dataset
- Upper Confidence Bound in Python – Step 1
- Upper Confidence Bound in Python – Step 2
- Upper Confidence Bound in Python – Step 3
- Upper Confidence Bound in Python – Step 4
- Upper Confidence Bound in R – Step 1
- Upper Confidence Bound in R – Step 2
- Upper Confidence Bound in R – Step 3
- Upper Confidence Bound in R – Step 4

**Section 28: Thompson Sampling [part of Reinforcement Learning]**

- Thompson Sampling Intuition
- Algorithm Comparison: UCB vs Thompson Sampling
- How to get the dataset
- Thompson Sampling in Python – Step 1
- Thompson Sampling in Python – Step 2
- Thompson Sampling in R – Step 1
- Thompson Sampling in R – Step 2

**Section 29: Natural Language Processing**

- what is nlp and its importance.
- what we can do with nlp
- Introduction to spam engines .
- Introduction to sentiment analyzers.
- word tokenization
- sentence tokenization
- parts of speech tagging
- lemmatization
- lemmatization
- removing stop words
- building word clouds
- feature extraction techniques and importance
- Word Existence feature
- Word proportion feature.
- TFIDF feature.
- NLP vs Machine Learning
- How to get the dataset
- Natural Language Processing in Python – Step 1
- Natural Language Processing in Python – Step 2
- Natural Language Processing in Python – Step 3
- Natural Language Processing in Python – Step 4
- Natural Language Processing in Python – Step 5
- Natural Language Processing in Python – Step 6
- Natural Language Processing in Python – Step 7
- Natural Language Processing in Python – Step 8
- Natural Language Processing in Python – Step 9
- Natural Language Processing in Python – Step 10
- Homework Challenge
- Natural Language Processing in R – Step 1
- Natural Language Processing in R – Step 2
- Natural Language Processing in R – Step 3
- Natural Language Processing in R – Step 4
- Natural Language Processing in R – Step 5
- Natural Language Processing in R – Step 6
- Natural Language Processing in R – Step 7
- Natural Language Processing in R – Step 8
- Natural Language Processing in R – Step 9
- Natural Language Processing in R – Step 10
- Homework Challenge

**Section 30: Deep Learning**

- problems of Machine Learning Models.
- Neural Networks
- How gradient descent algorithm works.
- what are Deep Neural Networks.

**Section 31: Artificial Neural Networks [as part of Deep Learning]**

- The Neuron
- The Activation Function
- How do Neural Networks work?
- How do Neural Networks learn?
- Gradient Descent
- Stochastic Gradient Descent
- Back propagation
- How to get the dataset
- Business Problem Description
- ANN in Python – Step 1 – Installing Theano, Tensor flow and Keras
- ANN in Python – Step 2
- ANN in Python – Step 3
- ANN in Python – Step 4
- ANN in Python – Step 5
- ANN in Python – Step 6
- ANN in Python – Step 7
- ANN in Python – Step 8
- ANN in Python – Step 9
- ANN in Python – Step 10
- ANN in R – Step 1
- ANN in R – Step 2
- ANN in R – Step 3
- ANN in R – Step 4 (Last step)

**Section 32: Convolutional Neural Networks [as part of Deep Learning]**

- What are convolutional neural networks?
- Step 1 – Convolution Operation
- Step 1(b) – ReLU Layer
- Step 2 – Pooling
- Step 3 – Flattening
- Step 4 – Full Connection
- Softmax & Cross-Entropy
- How to get the dataset
- CNN in Python – Step 1
- CNN in Python – Step 2
- CNN in Python – Step 3
- CNN in Python – Step 4
- CNN in Python – Step 5
- CNN in Python – Step 6
- CNN in Python – Step 7
- CNN in Python – Step 8
- CNN in Python – Step 9
- CNN in Python – Step 10
- CNN in R

**Section 33: Reducing Dimensionality**

- what is a dimension.
- why we should reduce dimensionality.
- different techniques to reduce

**Section 34: Principal Compound Analysis**

- PCA in Python – Step 1
- PCA in Python – Step 2
- PCA in Python – Step 3
- PCA in R – Step 1
- PCA in R – Step 2
- PCA in R – Step 3

**Section 35: Linear Discriminate Analysis**

- How to get the dataset
- LDA in Python
- LDA in R

**Section 36: Kernal PCA**

- How to get the dataset
- Kernel PCA in Python
- Kernel PCA in R

**Section 37: Model Selection and Boosting**

- How to select a model
- deferent techniques to select a model

**Section 38: Model Selection**

- How to get the dataset
- k-Fold Cross Validation in Python
- k-Fold Cross Validation in R
- Grid Search in Python – Step 1
- Grid Search in Python – Step 2
- Grid Search in R

**Section 39: XGBoost**

- how to get the dataset
- XGBoost in Python – Step 1
- XGBoost in Python – Step 2
- XGBoost in R

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