Building Intelligent Applications with AI and ML - Level 1 Training in Kazakhstan

Is This The Right Course?

Who Should Attend?

• Basic familiarity with Python programming.
• Basic understanding of Data Terminologies.
• Familiarity with enterprise IT.
• Foundational knowledge in mathematical concepts like linear algebra and probability.
• Basic linux skills.
• Basic SQL skills.

• Explore Jupiter notebooks and Python
• Recall and Remember Statistics and Probability concepts and explore coding in Python for data analysis
• Understand advanced probability concepts and data visualizations using libraries such as Matplotlib, seaborn
• Understand various Machine Learning Algorithms and their applications
• Understand various Predictive Models
• Apply Machine Learning algorithms to build predictive models
• Understand Recommendation Systems
• Understand how to deal with data in the real world
• Apply Machine Learning on Big Data using Apache Spark
• build UI and REST APIs for ML models

1. 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
2. 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
3. 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