AI and Machine Learning Level 1

About Course

This comprehensive diploma will equip you with the knowledge and skills to excel in the exciting fields of artificial intelligence and machine learning. You’ll gain a strong foundation in Python programming, data science principles, and core machine-learning techniques. Through hands-on projects and assignments, you’ll apply your learning to real-world scenarios, building a portfolio that demonstrates your expertise to potential employers.

Course Curriculum

PYTHON

Introduction to Python Programming
Fundamentals of Python Programming (• Variables • Strings • Arithmetic Operations • Comparison Operations • Identity Operations • Assignment Operations • Membership operations • List)
Control Structures and Data Collections in Python (• If statement • Data structure • TUPLE • SET • DICTIONARY • loop)
Advanced Python Concepts: Functions, Error Handling, and File Management
OOP

AI and Machine Learning Level 1

About Course

What I will learn?

Course Curriculum

PYTHON

Introduction to Python Programming

Fundamentals of Python Programming (• Variables • Strings • Arithmetic Operations • Comparison Operations • Identity Operations • Assignment Operations • Membership operations • List)

Control Structures and Data Collections in Python (• If statement • Data structure • TUPLE • SET • DICTIONARY • loop)

Advanced Python Concepts: Functions, Error Handling, and File Management

OOP

DATA SCIENCE

Linear Algebra

Numpy

Pandas

Data Preprocessing Essentials for Data Science

Introduction to Data Visualization

Matplotlib

Seaborn

INTRODUCTION TO PLOTLY

Statistical Analysis

MACHINE LEARNING

What is Machine Learning

Classification & Model Evaluation

Ensemble Techniques

Grid Search and Random Search

SUPERVISED LEARNING

Bias and Variance

Overfitting and Underfitting

Regularization

Linear Regression

Optimization & loss function

Logistic Regression

Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)

Decision Tree

Random Forest

Introduction to Ensemble Learning

Bagging (Bootstrap Aggregating)

Ada boost

Gradient Boosting

Stacking

XGBOOST

K-Nearest Neighbors (KNN)

Support Vector Machines (SVM)

Cross Validation

Naive Bayes

UNSUPERVISED LEARNING

Hierarchical Clustering Overview

KMEANS

DBSCAN

Dimensionality Reduction & PCA

Anamoly Detection

Association Rule Learning (Apriori, Eclat)

DEEP LEARNING

Introduction to Deep Learning

Perceptron Algorithm

Artificial Neural Networks (ANN)

Back and forward propagation

Activation Functions

MLOPs

Machine Learning Lifecycle

Building a Simple Chatbot

Model Deployment

End-to-End Model Deployment

Model Deployment Workflow and intro into MLflow

Sales Forecasting Model

Deploying Models Using Streamlit

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Target Audience

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