Data Science and AI Programming using Python

Objective: Immerse yourself in the world of data science, machine learning, and AI programming using Python. This course provides a holistic approach, from mastering statistical concepts to advanced machine learning techniques. Gain hands-on experience in AI programming and set the foundation for a career as a Data Scientist.

Week 1: Statistics

• Introduction to Statistics
• Different types of Statistics
• Population vs Sample
• Mean, Median, and Mode
• Variance, Standard Deviation
• Variables and Random Variables
• Percentiles & quartiles
• 5-number summary
• Histograms
• Gaussian – Normal distribution
• Standard Normal distribution
• Application Of Z Score
  
  

Week 2: Data Manipulation and Visualization

• Numpy
• Pandas
• Data Visualization with Seaborn and Matplotlib
  
  

Week 3: Exploratory Data Analysis (EDA)

• Feature Engineering and Selection
• Perform EDA with automated library
  
  

Week 4: Machine Learning Module – Basics

• Introduction to machine learning
• Difference between Supervised, Unsupervised Algorithms
• Linear Regression
• Logistics regression
• Overfitting & Underfitting

Week 5: More on Regression and Performance Metrics

• Ridge Regression
• Lasso Regression
• Logistics regression
• Difference between Linear Regression and Logistic Regression
• Performance metrics (Confusion matrix, F-beta Score)
  
  

Week 6: Machine Learning Module – Advanced

• Decision Tree
• Random Forest
• SVM (Support Vector Machine)
• K Nearest Neighbour
  
  

Week 7: Advanced Machine Learning Techniques

• Ada boosting
• Gradient boosting
• XGBoosting
  
  

Week 8: Unsupervised Machine Learning

• K-Means
• Dimension Reduction Techniques (Principal Component Analysis – PCA)