Duration
3 days
What you'll learn
- Master Machine Learning on Python
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
Target Audience
- Anyone interested in Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Day 1
9:00am - 10:30am
-------------------- Part 1: Data Preprocessing --------------------
Importing the Libraries
Importing the Dataset
For Python learners, summary of Object-oriented programming: classes & objects
Missing Data
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Categorical Data
Outliers
Feature Engineering
Splitting the Dataset into the Training set and Test set
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
-------------------- Part 2: Regression (Supervised Learning)--------------------
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Day 2
9:00am - 10:30am
-------------------- Part 3: Classification (Supervised Learning) --------------------
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Naive Bayes
Introduction to Ensemble Models
Decision Tree Classification
Random Forest Classification
Hyperparameter Model Tuning
Regularization – Ridge and Lasso
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
Cross Validation and Model Evaluation and Selection
Evaluating Classification Models Performance
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
-------------------- Part 4: Clustering --------------------
K-Means Clustering
Hierarchical Clustering
Day 3
9:00am - 10:30am
-------------------- Part 5: Introduction to Deep Learning --------------------
Artificial Neural Networks
Convolutional Neural Networks
10:30am - 10:45am Morning Break
10:45am - 12:30pm
-------------------- Part 6: Dimensionality Reduction --------------------
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
-------------------- Part 7: Model Selection & Boosting --------------------
Model Selection
XGBoost
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Review and summary
Programme scope, delivery arrangements and suitability are confirmed during enquiry.