TALENT LEARNING TEAM
Fundamentals of AI & ML Learning Path
Overview
Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological
innovation, driving advancements in diverse fields. AI refers to the development of intelligent
systems that can perform tasks requiring human-like intelligence, while ML focuses on creating
algorithms that enable computers to learn and make predictions from data.
This comprehensive self-paced learning path is designed to provide learners with a deep
understanding of Artificial Intelligence (AI) and Machine Learning (ML) from fundamental
principles to advanced practical applications. The path covers a wide range of topics to equip
learners with the knowledge and skills needed to excel in the AI and ML fields.
In this Learning path, you will begin by grasping the fundamental principles and versatile
applications of AI, from content generation to healthcare, and understand its core technologies
such as Computer Vision, Deep Learning, and Natural Language Processing (NLP).
As you progress, dive into hands-on experiences across diverse domains, leveraging AI's
potential in Emotion AI, healthcare diagnostics, business analytics, and even food recognition.
Master Machine Learning techniques, including regression analysis and classification methods,
enabling you to extract valuable insights from data.
Explore advanced topics like clustering and association rule learning to uncover intricate patterns
within datasets. Immerse yourself in Reinforcement Learning and unravel the complexities of
Natural Language Processing, bolstering your AI capabilities.
Delve deeper into the world of Deep Learning and Convolutional Neural Networks (CNNs), and
understand how to harness their power for various applications. Streamline data and enhance
model efficiency through dimensionality reduction techniques like PCA, LDA, and Kernel PCA.
The journey culminates in the mastery of model selection strategies, including k-Fold Cross
Validation, Grid Search, and the utilization of XGBoost in both Python and R. By the end of this
learning path, you'll be well-equipped to tackle real-world AI and ML challenges, making a
significant impact in your career and beyond. Join us in exploring the limitless possibilities of AI
and ML.
Objectives
●
Understand the fundamental principles and applications of Artificial Intelligence.
●
Explore various AI technologies, including Computer Vision, Deep Learning, and Natural
Language Processing (NLP).
●
Gain hands-on experience in real-world AI applications across diverse domains.
●
Master Machine Learning techniques, including regression analysis and classification
methods.
●
Apply clustering and association rule learning to uncover patterns in data.
●
Implement Reinforcement Learning algorithms and gain insights into Natural Language
Processing.
●
Dive deep into Deep Learning and Convolutional Neural Networks (CNNs).
●
Enhance your machine learning toolkit with dimensionality reduction techniques and
model selection strategies.
●
Fine-tune models using k-Fold Cross Validation, Grid Search, and XGBoost in Python and
R.
Estimated Time
2 Months at 5-6 hrs per Topic
*Note: you may skip sections that do not apply to you to shorten the time duration.
Learning Pathway on Udemy
Thoughtfully designed Udemy based learning pathway with handpicked & top-rated courses
trusted by businesses worldwide with a complete guide to learn fundamentals of AI and Machine
Learning.
Week 1: Navigating the AI Landscape - Principles and
Applications
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
AI Simplified:
Everyday
Applications
and Insights
This module introduces Artificial
Intelligence and GPT-3, focusing on
practical, non-technical
applications.Explore AI's use in content
writing, music, teaching, legal tasks, and
project management, along with how to
generate images from text. The course
also discusses AI's limitations and future
prospects, making it ideal for those new
to AI.
​
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1hr 15min
Self-paced
learning on
Udemy
AI in Action:
Exploring Key
Technologies
and Real-World
Applications
This module offers a dynamic
introduction to foundational AI
concepts, including Computer Vision,
Deep Learning, Reinforcement Learning,
Natural Language Processing (NLP),
Robotic Process Automation (RPA), and
Machine Learning. It delves into how
these technologies work, their specific
applications, and real-world use cases.
Provides a comprehensive look at AI's
diverse applications in today's world,
including business implications,
benefits, and risks.
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2hr 09min
Self-paced
learning on
Udemy
AI in
Healthcare
This module provides an in-depth
exploration of Artificial Intelligence,
starting with fundamental concepts and
leading to its application in healthcare. It
covers the essentials of AI, including the
differences between supervised and
unsupervised training, and dives into
the workings of artificial neural
networks. The module offers practical
knowledge with Google Teachable
Machines, explains key AI terminology,
and examines convolutional neural
networks (CNNs) with a focus on their
use in Covid-Net, a healthcare
application.
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2hr 58min
Week 2: AI Applied - Diverse Domains and Real-World Solutions"
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Exploring Media
Analytics with
Google's
Teachable
Machine
This module offers an insightful
exploration of Emotion AI, focusing
on its application in media analytics
using Google's Teachable Machine. It
covers the entire process from data
collection to model deployment,
highlighting key performance
indicators for classification models
and introducing advanced concepts
like transfer learning and off-the-shelf
networks.
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1hr 35min
Self-paced
learning on
Udemy
AI Applications
with DataRobot
Explore the use of AI in healthcare
through a focused case study on
heart disease detection using
DataRobot. This module guides you
from data handling to model
deployment, emphasizing key
performance metrics and model
evaluation techniques. Additional
content explores advanced AI
concepts like XG-Boost and
ensemble learning, enhancing your
understanding of AI in medical
diagnostics.
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2hr 11mins
Self-paced
learning on
Udemy
AI in Business:
Insurance
Premium
Prediction
Dive into using AI for business
analytics with a focus on insurance
premium prediction using AWS AI
AutoPilot. This module covers the
essentials of AWS, including
SageMaker, and guides learners
through regression techniques and
metrics. Interactive demos provide
hands-on experience in leveraging AI
for practical business solutions.
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1hr 31mins
Self-paced
learning on
Udemy
Culinary AI: Food
Recognition and
Explainability
with DataRobot
This module explores the use of AI in
the food industry, focusing on
recognizing different food types
using DataRobot AI. It includes
practical demonstrations on
uploading and analyzing datasets,
and training AI models for food
recognition. The course also offers
optional deeper insights into logistic
regression, the bias-variance tradeoff,
and L1 & L2 regularization, providing
a comprehensive understanding of
AI's capabilities and limitations in a
culinary context.
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1hr 18min
Week 3: Comprehensive Guide to Machine Learning: Data
Handling and Regression Analysis
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Kickstarting ML:
Data
Preprocessing
Fundamentals
This module offers an introductory
challenge to Machine Learning (ML),
focusing on data preprocessing. It
begins with Google Colab, and
includes instructions for installing R
and R Studio across different
operating systems. An extra segment
highlights how ChatGPT can enhance
ML skills. The core of the module is
dedicated to the basics of data
preprocessing, including splitting
data into training and test sets and
feature scaling, providing a strong
foundation for anyone starting their
journey in ML.
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29min
Self-paced
learning on
Udemy
ML Data
Preprocessing
Techniques
This module delves into essential
data preprocessing methods for
Machine Learning. It covers importing
datasets and libraries, handling
missing data, encoding categorical
variables, and feature scaling, along
with practical coding exercises. The
course provides a step-by-step
approach to ensure learners are
well-versed in preparing data for ML
models.
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2hr 14min
Self-paced
learning on
Udemy
Regression
Analysis in ML
This module introduces regression
analysis in Machine Learning,
covering both simple and multiple
linear regression. It provides
step-by-step guidance on
implementing these techniques in
Python and R, complete with practical
examples. The course also delves
into understanding key concepts like
p-values and backward elimination,
ensuring a thorough grasp of
regression methods and their
applications in solving real-world
business problems.
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3hr 29min
Self-paced
learning on
Udemy
Advanced
Regression
Techniques in
Machine Learning
This module delves into advanced
regression methods in Machine
Learning, covering polynomial
regression, Support Vector
Regression (SVR), decision tree
regression, and random forest
regression. It provides detailed
instructions for implementing these
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4hr 11min
techniques in both Python and R, with
step-by-step demonstrations for each
method.
Self-paced
learning on
Udemy
Optimizing
Regression
Models:
Evaluation and
Application
This module advances understanding
of regression in Machine Learning,
emphasizing model evaluation and
optimization. It covers the adjusted
R-squared metric, includes a practical
demonstration of regression code
templates, and concludes with
insights into interpreting and applying
regression models effectively.
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59min
Week 4: Mastering ML Classification - From Logistic Regression
to Random Forests
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Exploring
Classification:
Logistic
Regression
This module provides a
comprehensive look at classification
in Machine Learning, specifically
focusing on logistic regression. It
starts with the basics of what
classification is and the principle of
maximum likelihood. The module
then guides learners through a
detailed, step-by-step process of
implementing logistic regression in
both Python and R.
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1hr 58min
Self-paced
learning on
Udemy
Deep Dive into
Classification:
K-NN and SVM
Techniques
This module explores key
classification algorithms in Machine
Learning: K-Nearest Neighbors (K-NN)
and Support Vector Machine (SVM),
including Kernel SVM. Starting with
an intuitive understanding of K-NN
and SVM, it progresses to detailed
steps for implementing these
algorithms in Python and R. The
module also introduces the kernel
trick and various kernel functions,
delving into non-linear applications
with Kernel SVM.
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2hr 20min
Self-paced
learning on
Udemy
Classification
Mastery: Naive
Bayes to Random
Forest Techniques
This module covers essential
classification algorithms in Machine
Learning: Naive Bayes, Decision
Trees, and Random Forests. It
provides intuitive insights and
practical implementation steps for
each method in Python and R. The
module emphasizes hands-on
learning with quizzes and detailed
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3hr 23min
demonstrations, equipping learners
to master these classification
techniques in ML efficiently.
Week 5: ML Techniques in ML - Clustering and Association Rule
Learning
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Clustering in ML:
K-Means and
Hierarchical
Methods
This module delves into clustering
in Machine Learning, specifically
focusing on K-Means and
Hierarchical Clustering. It covers
the basics of K-Means, including
the Elbow Method and K-Means++,
and provides practical guidance for
implementing these techniques in
Python and R. The module also
examines Hierarchical Clustering,
emphasizing the use of
dendrograms. Interactive quizzes
help consolidate understanding,
offering a solid foundation in these
essential clustering methods.
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2hr 46min
Self-paced
learning on
Udemy
Decoding
Association Rule
Learning
This module delves into
Association Rule Learning in
Machine Learning, focusing on the
Apriori and Eclat algorithms. It
begins with an in-depth look at the
intuition behind Apriori, followed by
comprehensive steps for its
implementation in both Python and
R. The module then explores the
Eclat algorithm, providing practical
guidance for its application.
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2hr 39min
Week 6 : Reinforcement Learning and NLP
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Reinforcem
ent
Learning:
Exploring
UCB and
Thompson
Sampling
This module covers key Reinforcement
Learning algorithms: Upper Confidence
Bound (UCB) and Thompson Sampling. It
offers detailed guidance on
implementing these techniques in Python
and R and includes a comparison of the
two methods.
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3hr 53min
Self-paced
learning on
Udemy
NLP
Essentials:
Techniques
This module offers a comprehensive
introduction to Natural Language
Processing (NLP), covering both classical
Click here
3hr 06min
and
Applications
and deep learning models. It starts with
an overview of NLP types and the
Bag-of-Words model. The module then
provides a thorough walkthrough of NLP
techniques in Python and R, covering
multiple steps from basic processing to
advanced applications. This module is
designed for learners to gain practical
skills in NLP, crucial for text analysis and
language-based data interpretation.
Week 7 : Deep Dive into Deep Learning and CNNs
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
Deep Learning
This module delves into the world of
Deep Learning, covering key concepts
such as neurons, activation functions,
neural network operation, learning
processes, and practical
implementation of Artificial Neural
Networks (ANN) in Python and R.
Click here
3hr 39min
Self-paced
learning on
Udemy
Convolutional
Neural
Networks
(CNN)
This module provides a comprehensive
exploration of Convolutional Neural
Networks (CNNs), starting with the plan
of attack for understanding CNNs and
gradually covering topics like
convolution operations, ReLU layers,
pooling, flattening, full connections,
and the Softmax & Cross-Entropy
concepts. It culminates in practical
implementation steps of CNNs in
Python, including a final demonstration.
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3hr 14min
Week 8 : Dimensionality Reduction & Model Selection in ML
Module
Topic
Description
Link
Duration
Self-paced
learning on
Udemy
PCA, LDA, and
Kernel PCA
Explore dimensionality reduction
techniques. Firstly, gain an intuitive
understanding of Principal
Component Analysis (PCA) and
implement it in Python and R. Then,
dive into Linear Discriminant Analysis
(LDA) with practical examples and
follow it up with Kernel PCA.
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2hr 14min
Self-paced
learning on
Udemy
Model Selection
& Boosting
Techniques
Learn essential aspects of model
selection and boosting in machine
learning. Learn the intricacies of
k-Fold Cross Validation and Grid
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2hr 8min
Search in both Python and R to
fine-tune your models effectively.
Explore the power of XGBoost with
practical implementations in Python
and R. Gain insights into logistic
regression and enrich your machine
learning toolkit.
Frequently Asked Questions
Do I have to watch every assigned video? Can I skip some if I feel ready?
If you are familiar with some parts of the classwork, you may skip the videos covering any topics
you’re already familiar with.
Will I receive a Udemy completion certificate when I finish the course?
Obtaining a certificate from Udemy requires you to complete the entire duration of courses which
have been used to curate each learning path. In order to receive a certificate you will need to
complete the entire course and not just the sections that we have assigned to you.
Can I take other learning paths at the same time while I am completing this learning path?
You can, but please make sure to keep the learning path is your main focus. We have a limited
amount of available seats and want to make sure each one is taken up by someone who is
actively completing course content.
How do I sign up for the learning path?
Please complete this form to register:
https://talent.toptal.com/portal/talent-programs/learning-programs?programme-registration=show