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TOSS’s Data Science, Machine, Deep Learning using Python programming language course is a platform that enables the participants to get with the nuts and bolts of manipulating, processing, cleaning, and crunching data. The objective of this training program is to emphasize broad range of techniques for Data Science, Machine Learning and Deep Learning using Open Source Python software.


Overview of Data Science

  • Source of Big Data
  • How to Handle Big Data
  • What is Data Science, Artificial Intelligence and Machine Learning?
  • Statistical Learning and Machine Learning

  Big Data, Data Analytics and Python

  • Why Python programming for Data Science
  • Why R programming for Data Science


 Data Wrangling and data Exploration

  • Data Wrangling and Data Exploration
  • Handle issues in Data Wrangling
  • Model selection in Data Exploration

 Introduction of Statistics

  • How to approach the data Focus on data Assumption?
  • EDA Technique (Quantitative and Graphical)
  • Statistical Analysis
  • Inferential method
  • Descriptive Method
  • Discussion on Mean, Median, Variance and Standard Deviation
  • Understanding Skewness of Data using Bell Curve

 Python Fundamentals

  • Python Installation and Environment setup for Machine learning
  • Python Basic programming
  • Python Data types, statement and Loop
  • Python Method and Class
  • Python data Structure for Data Science implementation

  Python Data Structures 

  • Tuples
  • List
  • Dictionary
  • Set
  • Sequence

Python Package for Scientific computing – Numpy 

  • NumPy – Introduction
  • NumPy – Environment
  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • NumPy – Array from Existing Data
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • NumPy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations

 Data Analysis using Pandas 

  • Using Pandas for Analyzing Data – Data Munging
  • Using Pandas for Analyzing Data – Grouping and Aggregating
  • Using Pandas for Analyzing Data – Visualization
  • Analyzing Data with Pandas – Time Series
  • Getting & Knowing Your Data
  • Filtering & Sorting
  • Grouping, Apply, Merge, Stats, Visualization
  • Creating Series and Data Frames
  • Time Series
  • Deleting

 Statistical Learning 

  • Introduction to Statistical learning
  • Introduction to Scikit-learn package
  • Datasets
  • Estimators objects

Supervised learning and Supervised learning

  • Introduction to Supervised learning
  • Nearest neighbor and the curse of dimensionality
  • Linear model: from regression to sparsity
  • Support vector machines (SVMs)
  • Introduction to Unsupervised learning
  • Clustering: grouping observations together
  • Decompositions: from a signal to components and loadings
  • Linear regression with single and multiple variable to solve predictive problem.

Machine Learning Algorithm

  • Gradient Descent of Machine Learning
  • Logistic Regression
  • Support Vector Machines in Machine Learning
  • Non Linear hypothesis in Machine Learning
  • Cost Function Evaluating model in machine Learning
  • Unsupervised Learning -K means Algorithm in machine learning
  • Anomaly Detection in machine Learning
  • Implement Reinforcement Learning for Artificial Intelligence
  • Discussion on Gradient Boosting for Regression and Classification problem
  • Dimension Reduction and PCA algorithm
  • Recommender Systems in machine Learning
  • Introduction to Tensor Flow- Deep learning for image classification
  • Decision tree and Random Forest implementation with gradient boosting.
  • Neural Networks
  • Understand basic of Deep Learning
  • Back Propagation in Deep
  • Implement Neural Networks to classify images -Deep Learning approach

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