Machine learning Diploma 100 HRS
Module one: Python
Basics
o why python?
o python with AI and ML
o Input & Output
o Variables
o Data types
o Boolean & Comparison and Logic
o If Conditions
o For Loops
o Built-in functions & Operators
o Numbers & Math
o Functions
o Variables Scope
o Modules
o Command Lines
o File Handling
Object-Oriented Programming (OOP)
o Strings
o Special Functions
o Classes
o Inheritance
o Regular expressions
o Working with files
o Python generators
NumPy
o Create Numpy Array
o Indexing
o Arithmetic and Logic
o Universal Array Functions
•Pandas
o Series
o Data Frames
o Data Input & Output
o Useful Methods
o Apply function
o Grouping data and aggregate functions
o Merging, Joining and Concatenating
o Pivoting
Environment
Jupyter Notebook
GPU And Google Colab
Anaconda
• Project (Analyze SF Salaries dataset from Kaggle)
• Project (Analyze Ecommerce Purchase dataset from Kaggle)
Module Two: Data Preprocessing
- Feature Engineering and Extraction
o Domain knowledge features
o Date and Time features
o String operations
o Web Data
o Geospatial features
o Work with Text
• Feature Transformations
o Data Cleaning or Cleansing
o Work with Missing data
o Work with Categorical data
o Detect and Handle Outliers
o Deal with Imbalanced classes
o Split data to Train and Test Sets
o Feature Scaling
o Project (Preprocess Loan data)
Module Three: Machine Learning
Introduction to ML and Business cases
o The difference between ML, Big data, Data analysis and Deep Learning
o Linear Algebra and Statistics for ML
o Data preprocessing
Introduction to ML and Business cases
o The difference between ML, Big data, Data analysis and
Deep Learning
o Linear Algebra and Statistics for ML
o Data preprocessing
Regression problem
o Linear Regression
o Multi-linear regression
o Polynomial regression
o K-nearest neighbour regression
o Decision tree regression
o Regression Evaluation Metrics
Project (Ecommerce Expenses Prediction)
Project (Kaggle Bike Demand Predictions)
Project (Kaggle Black Friday Purchase Predictions)
Classification problem
o Logistic Regression
o Naive Bayes
o K-nearest neighbour classifier
o Support vector machine (SVM)
o Decision tree classifier
o Ensemble learning
o Classification Evaluation Metrics
o Random Forests
o XGBoost
Project (Predict Loan Approval Problem)
Project (Advertising Problem)
Project (Sentiment Analysis Problem)
Clustering Problems
o Dimensionality reduction
o K-means
o DBSCAN
o hierarchical clustering
o Association Rules
Model Selection and evaluation
o Loss functions
o Gradient descent
o Bias-variance tradeoff
o Cross-validation
o Hyperparameter tuning
- Project (Stock Market Prediction)
