[PT2021] ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TRAINING
workshops at datastat.co.ke
workshops at datastat.co.ke
Fri Aug 30 23:32:12 EDT 2024
Datastat
Training Center invites You for Training Workshops:
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Links Below for Upcoming Training Workshops:
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ICT
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Check
all our Courses HERE:
5
Days Course Fee= 1100 USD
10
Days Course Fee=2200 USD
Online
Training Fee=700USD
Contact
us Via:
Mobile
No: +254724527104(Whatsapp
and Calls)
Email: info at datastatresearch.org
See
our website
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www.datastatresearch.org
ARTIFICIAL
INTELLIGENCE AND MACHINE LEARNING TRAINING
This
training course aims to provide a comprehensive understanding of the core
concepts, principles, and applications of AI and ML. This training is designed
for participants who have a basic understanding of programming and statistics
but want to dive deeper into AI and machine learning concepts and
applications.
Duration
10
days
REGISTER
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HERE
Objectives
Understanding
the Fundamentals of AI and ML
Exploring
Core Concepts and Algorithms
Mastering
Data Preprocessing and Feature Engineering Techniques
Evaluating
and Optimizing Models
Applying
AI and ML in Real-World Scenarios
Addressing
Ethical Concerns in AI
Exploring
Future Trends and Innovations
Identifying
Challenges and Opportunities
Day
1: Introduction to AI and ML
Overview
of AI and ML:
Definitions, history, and applications
Types
of AI:
Narrow AI, General AI, and Superintelligent AI
Types
of ML:
Supervised, Unsupervised, and Reinforcement Learning
Mathematics
for ML:
Linear algebra, calculus, probability, and statistics basics
Tools
and Environments:
Introduction to Python, Jupyter Notebooks, and libraries like NumPy and
Pandas
Day
2: Data Preprocessing and Exploration
Data
Collection:
Sources and methods
Data
Cleaning:
Handling missing data, outliers, and normalization
Data
Visualization:
Using Matplotlib and Seaborn for exploratory data analysis
Feature
Engineering:
Creating meaningful features from raw data
Dimensionality
Reduction:
PCA, t-SNE, and other techniques
Day
3: Supervised Learning - Regression
Introduction
to Supervised Learning:
Concepts and algorithms
Linear
Regression:
Simple and multiple linear regression
Evaluation
Metrics:
MSE, RMSE, and R²
Polynomial
Regression:
When and how to use it
Hands-on
Exercise:
Building and evaluating regression models
Day
4: Supervised Learning - Classification
Introduction
to Classification:
Binary and multiclass classification
Logistic
Regression:
Understanding and applying it
Decision
Trees:
Concepts and implementation
Evaluation
Metrics:
Accuracy, precision, recall, F1-score, ROC-AUC
Hands-on
Exercise:
Building and evaluating classification models
Day
5: Unsupervised Learning - Clustering
Introduction
to Unsupervised Learning:
Concepts and importance
K-Means
Clustering:
Algorithm, distance metrics, and optimization
Hierarchical
Clustering:
Concepts and dendrograms
DBSCAN:
Density-based clustering
Hands-on
Exercise:
Applying clustering techniques to datasets
Day
6: Unsupervised Learning - Association and Anomaly Detection
Association
Rule Mining:
Apriori algorithm and market basket analysis
Anomaly
Detection:
Techniques and applications in fraud detection
Principal
Component Analysis (PCA):
Dimensionality reduction for unsupervised learning
Hands-on
Exercise:
Implementing association rules and anomaly detection
Day
7: Neural Networks and Deep Learning - Basics
Introduction
to Neural Networks:
Perceptron and multilayer perceptron
Activation
Functions:
Sigmoid, ReLU, Tanh, etc.
Backpropagation:
Understanding and implementing
Introduction
to TensorFlow/Keras:
Setting up and basic operations
Hands-on
Exercise:
Building a simple neural network
Day
8: Deep Learning - Advanced Topics
Convolutional
Neural Networks (CNNs):
Architecture and applications in image processing
Recurrent
Neural Networks (RNNs):
Understanding sequences and time series data
Transfer
Learning:
Using pre-trained models
Hands-on
Exercise:
Building and training CNNs and RNNs
Day
9: Reinforcement Learning
Introduction
to Reinforcement Learning:
Key concepts and terminology
Markov
Decision Processes (MDPs):
Framework for RL
Q-Learning
and Deep Q-Networks (DQNs):
Algorithms and applications
Hands-on
Exercise:
Implementing a simple reinforcement learning algorithm
Day
10: AI/ML Applications and Future Trends
Natural
Language Processing (NLP):
Basics and applications like sentiment analysis
AI
Ethics and Fairness:
Understanding bias and ethical considerations in AI
AI
in Industry:
Case studies from healthcare, finance, autonomous systems, etc.
Capstone
Project:
Apply the learned concepts to a real-world problem
Training
Methodology
The
training approach comprises many methods to facilitate deep understanding and
practical proficiency in artificial intelligence (AI) and machine learning (ML).
Participants will experience complete immersion in a dynamic learning
environment that includes interactive lectures, hands-on coding laboratories,
real-world case studies, and group discussions. During the training program,
participants have the opportunity to demonstrate their skills and receive
customized feedback through practical projects and assessments. This prepares
them to effectively apply the principles of Artificial Intelligence (AI) and
Machine Learning (ML) in real-world scenarios
Key
Notes
i. The
participant must be conversant with English.
ii. Upon
completion of training the participant will be issued with an Authorized
Training Certificate
iii. Course
duration is flexible and the contents can be modified to fit any number of
days.
iv. One-year
post-training support Consultation and Coaching provided after the
course.
v. Payment
should be done at least a week before commence of the training, to DATASTAT
CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare
better for you
Contact
us Via:
Mobile
No: +254724527104(Whatsapp
and Calls)
Email:
<mailto:info at datastatresearch.org>
info at datastatresearch.org
See
our website
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www.datastatresearch.org
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