Building Intelligent Applications with AI and ML - Level 1
Learn the fundamentals of Artificial Intelligence and Machine Learning to develop intelligent applications.
FormatsVirtual Classroom Live · Classroom LiveTopicsAnalytics And Data Management
What you’ll learn
- Getting Started with Jupitor Notebooks and Python
- Installing Python 3.x and Data Science environment
- Importing required modules
- Writing and executing Python code in Jupiter notebook
- Understanding Data Visualizations using Matplotlib and seaborn.
- Running Python scripts
- Statistics and Probability Essentials
- Understanding Descriptive and Inferential Statistics and the difference between them
- Understanding Data types - quantitative and qualitative
- How to understand the spread of data with measures of Central Tendency and dispersion
- Understanding dirty data - missing values and outliers
- Understanding probability distributions, Probability density function and probability mass function
- Understanding the spread and distribution of data using Python.
- Introduction to Percentiles and Moments and why they are important?
- Understanding Hypothesis Testing and various types of Hypothesis tests with their applications.
- Advanced Probability Concepts
- Covariance and Correlation and their role in understanding Data
- How Conditional Probability helps in predictive analytics?
- Baye's Theorem and it's applications
- Machine Learning Algorithms
- Understanding Different Types of Machine Learning Algorithms - Supervised, Semi-Supervised, UnSupervised, Reinforcement Learning
- Distinguish between Linear and Non-Linear, Distance-based, Parametric and Non-Parametric machine learning models
- Understanding different phases of building Machine Learning Models
- Differentiate between Classification and Regression
- An overview of linear and logistic regressions
- An overview of decision trees and random forests
- An overview of KNN and SVM
- How to Build Predictive Models with available data?
- Understanding your data - Data loading and descriptive analysis
- Dealing with unclean data - Data Cleaning and Pre-processing
- Building machine learning models with Linear Regression and Logistic Regression
- Understand when to apply Polynomial Regression and build a model
- Building a predictive model using multivariate regression
- Multi-level models
- Evaluating and Tuning your models with advanced Machine Learning with Python
- Understanding model fit - overfitting and underfitting, Bias-Variance Trade-Off
- Understanding model evaluation metrics for regression and classification
- Understanding K-fold cross-validation to avoid overfitting
- Bayesian models
- Implementing Email spam classifier with Naïve Bayes Classifier
- Understand K-Nearest Neighbors Algorithm and using KNN for predictive analytics
- Understand Gradient Descent, Stochastic Gradient Descent, and tune your model
- Understand ensemble methods, bagging and boosting
- Understand various boosting algorithms
- Unsupervised Machine Learning
- Understanding Clustering
- Understand K-Means Clustering with a case study
- Understanding Dimensionality Reduction and Principal Component Analysis
- Applying PCA on a real world dataset
- Understanding and Building Recommendation Systems
- What are recommendation Systems
- Understanding User-based and Item-based Collaborative Filtering
- Finding similar movies
- Improving the results of movie similarities
- Making movie recommendations to people
- Improving our recommendation results
Upcoming training
Dates and availability are confirmed by Bolt when we follow up. All times are shown in the provider’s stated time zone.
2026-11-09 — 2026-11-118:30 AM - 4:30 PM EST
Virtual Classroom LiveONLINE · English