Data Science

Machine Learning and its Benefits

Lesson 1 – Python Basics
Lesson 2 – Python Data Structures
Lesson 3 – Python Programming Fundamentals
Lesson 4 – Working with Data in Python
Lesson 5 – Working with NumPy Arrays

Data Science Course with Python

Key Learning Objectives of Data Science Course in Hyderabad

• Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
• Install the required Python environment and other auxiliary tools and libraries.
• Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions.
• Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions.
• Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
• Perform data analysis and manipulation using data structures and tools provided in the Pandas package.
• Gain expertise in Machine Learning using the Scikit-Learn package.
• Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline.
• Use the Scikit-Learn package for natural language processing.
• Use the matplotlib library of Python for data visualization.
• Extract useful data from websites by performing web scraping using Python.

Data Science Course curriculum

Lesson 1 – Data Science Overview.
Lesson 2 – Data Analytics Overview.
Lesson 3 – Statistical Analysis and Business Applications.
Lesson 4 – Python Environment Setup and Essentials.
Lesson 5 – Mathematical Computing with Python (NumPy).
Lesson 6 – Scientific Computing with Python (Scipy).
Lesson 7 –Data Manipulation with Pandas.
Lesson 8 – Machine Learning with Scikit–Learn.
Lesson 9 – Natural Language Processing with Scikit Learn.
Lesson 10 – Data Visualization in Python using Matplotlib.
Lesson 11 – Web Scraping with BeautifulSoup.

• What is Deep Learning?
• What is Data?
• Data Preprocessing and its implementations.
• Exploratory Data Analysis.
• Feature Engineering and Extraction.
• Data Wrangling.
• Data Manipulation.
• Data Visualization.
• Statistics.
• Linear Algebra.
• Calculus
• Data Science Course in Hyderabad- Machine Learning
• Supervised Problems
• Unsupervised Problems
• Semi-Supervised Problem
• Supervised Algorithms

Linear Regression

• Correlation Analysis
• Principles of Regression
• Introduction to Simple Linear Regression
• Python Flask
  • Introduction to Python Flask (deployment)
• Multiple Linear Regression
• Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

• Scatter diagram
  • Correlation Analysis
  • Correlation coefficient
• Ordinary least squares
• Principles of regression
• Splitting the data into training, validation and testing datasets
• Understanding Overfitting (Variance) vs Underfitting (Bias)
• Generalization error and Regularization techniques
• Introduction to Simple Linear Regression
• Heteroscedasticity / Equal Variance

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

Topics

• LINE Assumption
  • Collinearity (Variance Inflation Factor)
  • Linearity
  • Normality
• Multiple Linear Regression
• Model Quality metrics
• Deletion diagnostics

• Logistic Regression

Description: Learn to analyze Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

Topics

• Principles of Logistic Regression
• Types of Logistic Regression
• Assumption and Steps in Logistic Regression
• Analysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures, and its interpretation. Know what is a confusion matrix and its elements. Get introduced to the “Cut off value” estimation using the ROC curve. Work with a gain chart and lift chart.

Topics

• Multiple Logistic Regression
• Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1
• Receiver operating characteristics curve (ROC curve)
• Lift charts and Gain charts

Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples, and Random Forest algorithms.

Topics

• Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information Gain
• Ensemble techniques
• Decision Tree C5.0 and understanding various arguments
• Random Forest and understanding various arguments

• Support Vector Machine
• Naïve Bayes
• Bagging and Boosting Algorithms
• Unsupervised Algorithms
• Clustering
• K-Means Clustering
• Prototype based Clustering
• DBSCAN
• RDBSCAN
• Association
• Apriori Algorithm

Data Science Course in Hyderabad – Deep Learning

• Neural Network its intuition and implementation
• Single Layer Perceptron intuition
• Multi-Layer Perceptron intuition and differences
• Knowledge on ANN
• Knowledge on CNN and its Implementation
• RNN Intuition
• LSTM Intuition
• GRU Intuition
• Knowledge on GANS
• Knowledge on SAIMESE Net
• Working with Keras
• Knowledge of how TensorFlow works.

Data Science Course in Hyderabad- Natural Language Processing:

• Text reading in PYTHON
• Text preprocessing and its techniques
• Text Mining with Python
• Text Extraction uring NLTK
• Text Summarization
  • Extractive summarization
  • Abstractive summarization
• Sentiment Analysis
• Spacy
• Regular Expressions
• Feature representations like Bag of Words, TF-IDF, Word2Vec.
• Knowledge on Glove(Global Vectors)
• Stop-words
• N-Grams
• Word-Net
• Grammarly-Bot
• Text Classification

Data Science Course in Hyderabad- Computer Vision:

• Image preprocessing
• Image segmentation
• Binarize Images
• Blurring Images
• Cropping Images
• Detect Edges
• Enhance Contrast of Color Image
• Enhance Contrast of Greyscale Image
• Harris Corner Detector
• Installing OpenCV
• Isolate Colors
• Load Images
• Remove Backgrounds
• Save Images
• Sharpen Images
• Shi-Tomasi Corner Detector
• Using Mean Color as A Feature

Data Science Course in Hyderabad Projects:

• Santander Bank Customer Satisfaction
• Digit Recognition
• Author Identification
• Cancer Detection