Python for AI in Data Science

Course Description:

Embark on an extensive journey into the world of Data Science and AI with our comprehensive online training course. Kickstart your learning with Jupiter notebook and Python programming basics, advancing through data structures and essential packages like Numpy and Seaborn. Dive into the core of Data Science and Machine Learning, covering regression, decision trees, and support vector machines.

Explore classical machine learning algorithms, delve into Natural Language processing,  venture into Deep Learning with Artificial Neural Networks and more. Our course also includes sessions on LLMS, complete LANGCHAIN with LLMs, Open AI, and Open AI API with Python.

What You’ll Receive:

Gain an Edge in the Job Market: Acquire valuable skills to stand out and establish a foundation for a rewarding career in Data Science and AI.
Future-Ready Expertise: With the growing demand for skilled professionals in Data Science and AI, this course equips you for future opportunities in this dynamic field.

Our Offerings:

60 Hours of Live Classes: Benefit from 60 hours of engaging live classes, ensuring a thorough understanding of concepts and ample opportunities for clarification.
Guidance from Industry Experts: Learn from seasoned professionals who share real-world insights and experiences, providing practical perspectives on applying Data Science and AI.
Hands-on Project Experience: Apply your knowledge in real-world scenarios through live projects, gaining practical experience to enhance your technical skills.

Modules:

1. Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

2. Introduction to Python

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

3. Python Programming Basic

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

• Python Packages

  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library

•  Importing Data 

  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to CSV file

• Manipulating Data

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

4. Statistics Basics 

• Central Tendency
• Mean
• Median
• Mode
• Skewness
• Normal Distribution
• Probability Basics
• What does mean by probability?
• Types of Probability
• ODDS Ratio?
• Standard Deviation
• Data deviation & distribution
• Variance
• Bias variance Trade-off
• Underfitting
• Overfitting
• Distance metrics
• Euclidean Distance
• Manhattan Distance
• Outlier analysis
• What is an Outlier?
• Inter Quartile Range
• Box & whisker plot
• Upper Whisker
• Lower Whisker
• Scatter plot
• Cook’s Distance
• Missing Value Treatment
• What is an NA?
• Central Imputation
• KNN imputation
• Dummification
• Correlation
• Pearson correlation
• positive & Negative correlation

5. Error Metrics 

• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score
• Regression
• MSE
• RMSE
• MAPE

6. Machine Learning

• Supervised Learning 

  • Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
  • Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure Bias Variance Tradeoff
  • ROC curve
  • Bias Variance Tradeoff

• Unsupervised Learning 

  • K-Means
  • K-Means ++
  • Hierarchical Clustering

• SVM

  • Support Vectors
  • Hyperplanes
  • 2-D Case
  • Linear Hyperplane

• SVM Kernal

  • Linear
  • Radial
  • polynomial

• Other Machine Learning algorithms 

  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest

7. ARTIFICIAL INTELLIGENCE

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AI Applications

8. Deep Learning Algorithms 

• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network
• ANN – Artificial Neural Network

• Introduction to NLP (Duration-5hrs)

  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization

• Text to Features (Feature Engineering)

  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s

• Tasks of NLP 

  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching