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BUILDING INTELLIGENT APPLICATIONS WITH AI AND ML - LEVEL 1
Exclusive - Learn the fundamentals of Artificial Intelligence and Machine
Learning to develop intelligent applications.
Intelligent applications that are developed using Artificial Intelligence and
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This course focuses on covering the fundamentals like Statistics, probability,
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* Virtual Classroom Live (5)
Virtual Classroom Live | May 20 - 22, 2024 | 8:30 AM - 4:30 PM EDT | ONLINE
Virtual Classroom Live | May 20 - 22, 2024 | 8:30 AM - 4:30 PM EDT | ONLINE
Virtual Classroom Live | Jun 24 - 26, 2024 | 8:30 AM - 4:30 PM EDT | ONLINE
Virtual Classroom Live | Jul 08 - 10, 2024 | 8:30 AM - 4:30 PM EDT | ONLINE
Virtual Classroom Live | Sep 23 - 25, 2024 | 8:30 AM - 4:30 PM EDT | ONLINE
Virtual Classroom Live | Nov 11 - 13, 2024 | ONLINE
Start learning as soon as today! Click Add To Cart to continue shopping or Buy
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Access Period:
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IS THIS THE RIGHT COURSE?
* 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.
WHO SHOULD ATTEND?
This is an introduction-level hands-on course suitable for everyone who wants to
explore the field of Artificial Intelligence (AI) and Machine Learning (ML).
This course covers the foundations of AI, ML, programming, search, and logic
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* Business Analysts
* Data Analysts
* Developers
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* Anyone new to AI and ML who wants to understand the foundations of ML and AI
for developing ML applications.
WHAT YOU'LL LEARN
* 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
COURSE OUTLINE
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
BUY NOW
PREREQUISITES
* 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.
FOLLOW-ON COURSES
* Building Intelligent Applications with AI and ML - Level 2
$2195
GK# 821536 Vendor# AL/ML Level 1
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