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Road to Data Scientist


Instructor led best course for beginners to make career in AI and Machine learning as Data scientist or AI scientist.

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4.7

Rating

75 Hrs

Duration

Beginner

Level

70

Assignments

What you'll learn

Who should take this course

Skills you'll gain

Course contents

Python Origin, Where it is used and comparison with other programming languages.
  • Vanila Installation
  • Anaconda Installation
  • Miniforge Installation
In command window also in IDE like Jupyter notebook and VS code.
  • Built-in data types
  • Variable assignment
  • Additional data types
  • Arithmetic operators
  • Assignment operators
  • Comparison operators
  • Logical operators
  • Identity operators
  • Membership operators
  • Bitwise operators
  • Slicing, modifying, concatenating and formating strings
  • Practical assignment
  • Access, update, add and remove list items
  • Copy, join and sort list
  • Practical assignment
  • Using "if then else" and "match"
  • Practical assignment
  • Using "for" and "while" loop
  • Practical assignment
  • Reading and writing txt, csv, excel and Json file
  • Practical assignment
  • User defined function
  • Lambda function
  • Practical assignment
  • Exception handling using "try"..."except"
  • Practical assignment
  • Applying regex function like findall, search, split and sub
  • Practical assignment
  • Applying regex function like findall, search, split and sub
  • Practical assignment
  • Applying python's inbulit math function to perform mathematical tasks on numbers
  • Practical assignment
  • Why tuple and various tuple operation
  • Practical assignment
  • Why set and various set operation
  • Practical assignment
  • Why dict and various dict operation
  • Practical assignment
  • Creating multi domentional array
  • Performing various array operations
  • Mathemetical operations using array
  • Practical assignment
  • Creating data frame
  • Performing various data frame operations
  • Exploratory data analysis and data manipulation
  • Practical assignment
  • Creating date objects using differnt formats
  • Practical assignment
  • How to create class and objects
  • Basics of object oriented programming using class
  • Practical assignment
  • Creating visualization using matplotlib, plotly
  • Practical assignment
  • Creating python based REST api
  • Practical assignment
  • Basic overview of Scikit Learn and Scipy library
  • Practical assignment
  • Data Types
  • Sampling types
  • Central tendency
  • Measure of spread and position
  • Skewness
  • Sampling
  • Confidence Interval
  • Hypothesis testing
  • Comparison test
  • Correlation test
  • Supervised Learning
  • Unsupervised Learning
  • Semi supervised Learning
  • Reinforcement Learning
  • Problem Definition
  • Data selection
  • Exploratory data analysis
  • Data cleaning
  • Outlier analysis
  • Data transformation
  • Feature selection
  • Training the model
  • Model validation and performance metrics
  • Understanding the case study
  • Data preprocessing
  • Defining the hypothesis
  • Assumptions of Linear regression
  • Defining the cost function
  • Training the model using python sk-learn
  • Error analysis and performance metrics
  • Understanding the case study
  • Data preprocessing
  • Defining the hypothesis
  • Assumptions of Logistic regression
  • Defining the cost function
  • Training the model using python sk-learn
  • Error analysis and performance metrics
  • Understanding the case study
  • Data preprocessing
  • Defining the hypothesis
  • Assumptions of decision tree
  • Defining the cost function
  • Training the model using python sk-learn
  • Error analysis and performance metrics
  • Types and techniques
  • Understanding the case study
  • Data preprocessing
  • Defining the hypothesis
  • Assumptions of K-Means
  • Defining the cost function
  • Training the model using python sk-learn
  • Error analysis and performance metrics
  • Benefits of this technique
  • Assumptions of Hierarchical clustering
  • Defining the cost function
  • Implementation using python sk-learn
  • Output analysis and performance metrics
  • Understanding the case study
  • Benefits of PCA
  • Data preprocessing
  • Assumptions of PCA
  • Implementation using python sk-learn
  • Output analysis and performance metrics
  • Overview and Benefits
  • Keys concepts
  • Types and techniques
  • Understanding the case study
  • Assumptions of ARIMA
  • Parameter identification
  • Defining the cost function
  • Model fitting and forecasting using python sk-learn
  • Output analysis and performance metrics
  • Overview
  • Application of NLP
  • Python NLP libraries NLTK, Spacy
  • Understanding sentence structure
  • Word sense Disambiguation
  • Identifying entities and their relation
  • Bag of words
  • N gram
  • Different types of similarity measures
  • Text cleaning
  • Stop word removal
  • Stemming
  • Lemmatization
  • TF IDF
  • Word2Vec
  • Case study: categorizing news into groups
  • Topic mining
  • Case study: Customer feedback analysis
  • Sentiment analysis
  • Identifying aspects from topics
  • Case study: Customer feedback on iPhone product
Basic overview of how Neural network works
How to use Pytorch for deep learning models
Case study on classification using deep learning

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