Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)

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Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) Course Overview

Enrol for this 3-days Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) training from Koenig Solutions. This introduction-level hands-on course explores the field of artificial intelligence (AI), programming, logic, search, machine learning (ML), and natural language understanding. You’ll learn current AI and ML methods, tools, techniques, and their application to computational problems.

We’ll focus on the algorithms used to create machine learning models. Using clear explanations, simple Python code (no libraries), and step-by-step labs, you’ll discover how to load and prepare data, evaluate your models, and implement a suite of linear and nonlinear algorithms along with assembling algorithms from scratch.

You’ll also learn about algorithm applicability along with their limitations and practical use cases.

This course presents a wide variety of related technologies, concepts, and skills in a fast-paced, hands-on format. This provides you with a solid foundation for understanding and getting a jumpstart into working with artificial intelligence and machine learning.

Target Audience:

  • Business Analysts
  • Data Analysts
  • Developers
  • Administrators
  • Architects
  • Managers, and others new to AI and ML who want to understand the core skills and how to put them into practice.

Learning Objectives

  • Getting Started with Python and Jupyter
  • Statistics and Probability Refresher and Python Practice
  • Matplotlib and Advanced Probability Concepts
  • Algorithm Overview
  • Predictive Models
  • Applied Machine Learning
  • Recommender Systems
  • Dealing with Data in the Real World
  • Machine Learning on Big Data (with Apache Spark)
  • Testing and Experimental Design
  • GUIs and REST: Build a UI and REST API for your Models

The 1-on-1 Advantage

Methodology

Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

Module 1: Getting Started
  • Installing a Python Data Science Environment
  • Using and understanding iPython (Jupyter) Notebooks
  • Python basics: Part 1
  • Understanding Python code
  • Importing modules
  • Python basics: Part 2
  • Running Python scripts
  • Types of data
  • Mean, median, and mode
  • Using mean, median, and mode in Python
  • Standard deviation and variance
  • Probability density function and probability mass function
  • Types of data distributions
  • Percentiles and moments
  • A crash course in Matplotlib
  • Covariance and correlation
  • Conditional probability
  • Bayes' theorem
  • Data Prep
  • Linear Algorithms
  • Non-Linear Algorithms
  • Ensembles
  • Linear regression
  • Polynomial regression
  • Multivariate regression and predicting car prices
  • Multi-level models
  • Machine learning and train/test
  • Using train/test to prevent overfitting of a polynomial regression
  • Bayesian methods: Concepts
  • Implementing a spam classifier with Naïve Bayes
  • K-Means clustering
  • What are recommender systems?
  • Item-based collaborative filtering
  • How item-based collaborative filtering works?
  • Finding movie similarities
  • Improving the results of movie similarities
  • Making movie recommendations to people
  • Improving the recommendation results
  • K-nearest neighbors - concepts
  • Using KNN to predict a rating for a movie
  • Dimensionality reduction and principal component analysis
  • A PCA example with the Iris dataset
  • Data warehousing overview
  • Reinforcement learning
  • Bias/variance trade-off
  • K-fold cross-validation to avoid overfitting
  • Data cleaning and normalization
  • Cleaning web log data
  • Normalizing numerical data
  • Detecting outliers
  • Installing Spark
  • Spark introduction
  • Spark and Resilient Distributed Datasets (RDD)
  • Introducing MLlib
  • Decision Trees in Spark with MLlib
  • K-Means Clustering in Spark
  • TF-IDF
  • Searching wikipedia with Spark MLlib
  • Using the Spark 2.0 DataFrame API for MLlib
  • A/B testing concepts
  • T-test and p-value
  • Measuring t-statistics and p-values using Python
  • Determining how long to run an experiment for
  • A/B test gotchas
  • Build a UI for your Models
  • Build a REST API for your Models
Live Online Training (Duration : 24 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

1400 + If you accept merging of other students.
4 Hours
8 Hours
Ultra-Fast Track
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
1250 Per Participant
Online
12 - 14 Dec
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
16 - 18 Jan
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Course Prerequisites
  • Basic Python skills
  • A grounding in enterprise computing
  • Be familiar with enterprise IT
  • Have a general (high-level) understanding of systems architecture
  • Knowledge of business drivers that might be able to take advantage of applying AI
  • Good foundational mathematics in linear algebra and probability
  • Basic Linux skills
  • Familiarity with command line options such as ls, cd, cp, and su

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FAQ's


In both, you choose the schedule. In public, other participants can join, Private other participants want to join.
Yes, course requiring practical include hands-on labs.
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Yes, we do offer corporate training More details
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Yes, we also offer weekend classes.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
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You will receive the digital certificate post training completion via learning enhancement tool after registration.
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Dubai, Goa, Delhi, Bangalore.
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