Certified Online Data Science Training Institute with 100% Placement Assistance

• Introduction to data science
• Statistics for Data Science
• Introduction to Data
• Descriptive Statistics

1. INTRODUCTION 

• What is Python?
• Why Data Science requires Python?
• Installation of Anaconda
• Understanding Jupyter Notebook
• Basic commands in Jupyter Notebook
• Understanding Python Syntax

2. Data Types and Data Structures

• Variables
• Strings
• Lists
• Sets
• Tuples
• Dictionaries

3. Control Flow and Conditional Statements

• Conditional Operators, Arithmetic Operators and Logical Operators
• If, Else if and Else Statements
• While Loops
• For Loops
• Nested Loops

4. Functions

• Lambda Functions
• Map, Filter and Reduce
• Modules and Packages

5. File Handling

• Create, Read, Write files
• Operations in File Handling
• Errors and Exception Handling

Numpy 

• Arrays
• Basic Operations in Numpy
• Indexing
• Array Processing

Pandas

• Series
• Data Frames
• Indexing and slicing
• Groupby
• Concatenating
• Merging Joining
• Missing Values
• Operations
• Data Input and Output
• Pivot
• Cross tab

Data Visualization

• • Introduction to Matplotlib
  • Line plots
  • Histograms
  • Box and Violin Plots
  • Scatterplot
  • Heatmaps
  • Subplots
  • Visualization with Seaborn

Project 1:

• Data Mining

This project starts completely from scratch which involves collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of Data Science Life Cycle which involves.

1. Data Collection
2. Data Mining
3. Data Preprocessing
4. Data Visualization

Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Probability Distributions

• Discrete Probability Distributions
  • Bernouli Distribution
  • Binomial Distribution
  • Poisson Distribution
• Continuous Probability Distributions
  • Normal Distribution
  • Standard Normal Distribution

INFERENTIAL STATISTICS

• Hypothesis Testing
• Parametric Tests
  • t- Test
  • Z-Test
  • ANOVA
• Non-Parametric Tests
  • Chi – Square Test

Introduction

• What is Machine Learning?
• Supervised Vs Unsupervised Learning
• Regression Vs Classification Problems

Regression Techniques

Linear Regression:

• Simple Linear Regression
• Multiple Linear Regression
• Estimating the Regression Coefficients
• Polynomial Regression

Classification Techniques:

• An Overview of Classification
• Why Not Linear Regression?
• Logistic Regression
• Evaluation Metrics for Classification Models
• A Case Study on Classification using Logistic Regression

Decision Trees (Rule Based Learning)

• Basic Terminology in Decision Tree
• Root Node and Terminal Node
• Regression Trees
• Classification Trees
• Advantages and Disadvantages of Trees
• Gini Index, Information Gain/Entropy and Reduction in Variance
• Overfitting and Pruning
  • A case study on Decision Trees

 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

 Ensemble Methods in Tree Based Models:

• What is Ensembled Learning?
• What is Bagging and how does it work?
• What is Random Forest and how does it work?
• The Bootstrap
• Variable selection using RandomForest
  • A case study on Random Forest

Support Vector Machines:

• Hyperplane
• The Maximal Margin Classifier
• Support Vector Classifiers
• Support Vector Machines
• Hard and Soft Margin Classification
• Classification with Non-linear Decision Boundaries
• Kernel Trick
• Linear, Polynomial and Radial
• Tuning Hyper parameters for SVM
• Gamma, Cost and Epsilon
• SVMs with More than Two Classes

Case Study:

• K Nearest Neighbors:
  • K-Nearest Neighbor Algorithm
  • Eager Vs Lazy learners
  • How does the KNN algorithm work?
  • How do you decide the number of neighbors in KNN?
  • Curse of Dimensionality
  • Pros and Cons of KNN
  • How to improve KNN performance?
• Project 2:  A Project on Supervised Learning

The Challenges of Unsupervised Learning

• Principal Components Analysis:
• Introduction to Dimensionality Reduction and it’s necessity
• What Are Principal Components?
• Eigen Values, Eigen Vectors and Orthogonality
• Transforming Eigen values into a new data set
• Proportion of variance explained in PCA
• A Case Study on PCA
• Clustering Methods:
• K-Means Clustering
• Centroids and Medoids
• Deciding optimal value of ‘K’ using Elbow Method
• Linkage Methods
• Hierarchical Clustering
• Divisive and Agglomerative Clustering
• Dendrograms and their interpretation
• Applications of Clustering
• A Case Study on Clustering
• Project 3: A Project on Unsupervised Learning

Introduction to NLP

• • What is Natural Language Processing?
  • Structured vs Unstructured Data
  • NLTK
  • Importance and Advantages of Natural Language Processing
  • Text Mining
• Text Preprocessing:
• Regular Expressions for Pattern Matching
• Text Normalization
• Text Tokenization
  • Sentence Tokenization
  • Word Tokenization
• Text Segmentation
  • Stemming
  • Lemmatization
• POS Tagging
• Natural Language Understanding:
• Word Embedding’s
  • Bag of Words Model
  • TF-IDF Vectorizer
  • Count Vectorizer

Case Study on Spam Detection

Projects: Extracting unstructured text from files or Websites or any sources

Sentiment Analysis

Introduction to Neural Networks

• • Introduction to Perceptron
  • Activation Functions
  • Cost Functions
  • Gradient Decent
  • Stochastic Gradient Descent
  • Back propagation
• Tensorflow:
  • Tensorflow Basic Syntax
  • Tensorflow Graphs
  • Variables and Placeholder
  • Saving and Restoring Models
  • Tensorboard
• Building Neural Network with Tensorflow:
  • Neural Network for Regression
  • Neural Network for Classification
  • Evaluating the ANN
  • Improving and tuning the ANN
• Building a Convolutional Neural Networks with Keras:
  • Introduction to Computer Vision
    • OpenCV library in Python
  • Convolution Operation
  • ReLU Layer
  • Pooling
  • Flattening
  • Softmax and Cross Entropy
  • Building a CNN model with Keras
  • Training the CNN model
  • Saving the CNN model
• Projects:

Image Processing Using Convolution Neural Network