Python Syllabus
Machine Learning course included all concepts of
• Python
• Mathematics Linear Algebra & statics
• Data processing and visualization
• All Machine Learning algorithms
• Examples & case studies with practical
• Deep learning algorithms
• Assignments
• Real time application projects in micro hardware & electronics
Duration
navratri special discount offer
50%
Available Seats
30
Schedule
Weekly:- 5.00 pm - 7.00 pm
WeekEnd:- Saturday - Sunday : 11:00 am - 2:00 pm
1 Python Fundamentals
a. Basic python programming need for machine learning
2 NumPy
2.1 Computer Programming and Programming Languages a. Perform Mathematical operation needed for ML in python using NumPy package
3 Pandas
a. Using pandas package Perform Data manipulations.
4 Math’s Linear Algebra
a. Basic mathematics linear Algebra for ML.
5 Math’s Statistics
a. Basic mathematics Statistics for ML.
6 Hypothesis Testing
a. Case studies using mathematics
7 Data Visualizations
7 a. Using Data visualization packages show data in graphical state
8 Data Analysis
a.In Data Analysis perform data extraction methods
9 Simple Multiple Linear Regression
a. Multiple Linear Regression ML algorithm
b. Examples and Case studies
10 Multiple Linear Regression
a. Learn Multiple Linear Regression ML algorithm
b. Examples and Case studies
11 Gradient Descent
a. Optimization Technique
b. Differential Mathematics
12 KNN
a. Learn KNN ML algorithm
b. Examples and Case studies
13 Model Performance Metrics
a. Performance calculation of ML algorithms
14 Model Selection Part1
a. Best ML model section technique
15 Naive Bayes
a. Learn Naive Bayes ML algorithm
b. Examples and Case studies
Logistic Regression
a. Learn Logistic Regression ML algorithm
b. Examples and Case studies
Support Vector Machine (SVM)
a. Learn Support Vector Machine (SVM) ML algorithm
b. Examples and Case studies
Decision Tree
a. Learn Decision Tree ML algorithm
b. Examples and Case studies
Ensembling
a. Ensembling Concepts
b. Examples and Case studies
Model Selection Part2
a. Best ML model section technique
Unsupervised Learning
a. Learn Kmeans ML algorithm.
b. Examples and Case studies
Dimension Reduction
a. ML Data dimensions reduction concepts Using PCA
Advanced Machine Learning Algorithms
a. Optimal Solution
b. Regularization
c. Ridge and Lasso
d. Model Selection
5.Deep Learning
a. Learn Neural Network ML algorithm
b. Examples and Case studies