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Introduction to Machine Learning and Deep Learning

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AgileFever’s Machine Learning and Deep Learning training is designed to help learners embrace a sought-after career in AI. This course includes key topics such as Python fundamentals, supervised and unsupervised machine learning algorithms, reinforcement learning, and natural language processing (NLP) basics.

40 hours of live online instructor-led training
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Interactive learning with hands-on exercises and lab activities
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Supercharge your career in AI

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Course Overview

Machine Learning and Deep Learning course from AgileFever is a 40-hour program delivered in the form of live instructor-led online classes. This training is tailor-made for software engineers, data analysts, project leads, economists, and anyone looking to kickstart their career in the field of AI.

Through this training, attendees will develop a comprehensive understanding of advanced NLP, Deep Learning, Machine Learning, and Python programming concepts. This training is also powered by hands-on exercises and lab activities to help learners acquire future-ready skills and become in-demand tech professionals experiencing massive demand across top MNCs.

Key Highlights

Gain a solid understanding of the basic Python programming concepts

Understand key Python libraries and their practical applications

Get introduced to the basic concepts of Machine Learning

Ace the essentials of Machine Learning and Deep Learning statistics

Master supervised and unsupervised Machine Learning methods

Gain a comprehensive understanding of the fundamentals of reinforcement learning

Gain an overview of neural networks and their functionality

Understand the essential elements of Natural Language Processing (NLP)

Introduction to Machine Learning and Deep Learning Course Content

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Module 1: Python Basics

Overview of Python
Python Basics – variables, identifiers, indentation
Data structures in Python – list, strings, sets, tuples, dictionary
Statements in Python – conditional, iterative, jump
Functions in Python
Lambda functions

Module 2: Introduction to NumPy, Pandas, and Matplotlib

Create arrays using NumPy
Perform various operations on arrays and manipulate them
Indexing, slicing and iterating
Reading and writing data from text/CSV files into arrays and vice-versa
Creating series and data frames in Pandas
Data structures and index operations in Pandas
Reading and writing data from Excel/CSV formats into Pandas
Creating simple plots in Matplotlib
Grids, axes, plots, markers, colors, fonts, and styling
Types of plots – bar graphs, pie charts, histograms contour plots
Choosing the right plot format for a problem at hand
Scaling and adding style to your plots

Module 3: Introduction to Machine Learning with Python

What is machine learning?
Introduction to machine learning
Types of machine learning
Basic Probability required for machine learning
Linear Algebra required for machine learning

Module 4: Basic Statistics

Measures of central tendency – Mean, Mode and Median
Measures of spread – IQR, variance, and standard deviation
Missing value treatment
Outlier treatment
Univariate and Multivariate analysis
Inferential Statistics
Hypothesis Testing – Type I and Type II errors
P-value
Level of Significance
Confidence Interval
Probability Basics and Conditional Probability
Exploratory Data Analysis(EDA) – Practical use case

Module 5: Supervised and Unsupervised Machine Learning Algorithms

Simple linear regression
R2 and RMSE
Logistic regression
Decision trees
Random forests
SVM
Naive Bayes
Confusion Matrix
Dimensionality reduction – PCA
Cluster algorithms
K-means Clustering
Agglomerative Clustering

Module 6: Reinforcement Learning

Understanding reinforcement learning
Algorithms associated with reinforcement learning
Q-learning Model

Module 7: Introduction to Artificial Neural Networks (ANN)

A Perceptron
Neural networks
Activation functions
Deep learning with Keras
Errors and Biases
Back propagation
Building your first neural network
Building artificial neural networks (ANN) with Python (Model creation using TF/Keras)
Computer vision – OpenCV
Introduction to OpenCV – working with images

Module 8: Fundamentals of Natural Language Processing

Basics of NLP (Natural Language Processing)
Removing Stop Words
Stemming and lemmatization
Parts of Speech Tagging
TFIDF Vectorizer
Sentiment Analysis
SMS Spam Classifier

Case Study - Practical

Aim: This scenario focuses on exploratory data analysis and the path to create a machine learning model of the recent pandemic COVID 19 which is threatening worldwide. This case study aims to understand the data, convert it into a data frame, and perform the analysis with the mentioned steps of the algorithm. Use the Python-centric packages that would be typically needed to develop a solution for the case above. Python 3.7+ recommended.

Write the steps involved and develop the code to convert the data from the dataset of the above case study into a data frame. The data given includes details of all patients who contracted with Pandemic from Nov 2019 and it is represented in the format of a .csv file. We must convert the file into a data frame to continue with further analysis.
Analyzing the features, creating a feature extraction analysis, and considering the columns important for EDA. Manipulate only those columns that are important for visualization and the threatening scenario of the pandemic.
Plot the data to understand the survival rate or mortality rate of the recent pandemic from the case study and the data given.

Schedules for Introduction to Machine Learning and Deep Learning

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Introduction to Machine Learning and Deep Learning Exam Details

Exam Details

Name of Exam: AgileFever Machine Learning and Deep Learning Examination

Exam details are as follows:

Practical Exams, Lab Assessments, and Projects at the end of every completed module.

Prerequisites

Learners need to have a basic knowledge of Python programming.

Introduction-to-Machine-Learning-and-Deep-Learning-certificate

Introduction to Machine Learning and Deep Learning is ideal for

Data Analysts
Software Engineers
Business Analysts
Product Owners
Project Managers
Tech Enthusiasts

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Companies that trust Us

Happy learners and successful teams, that’s how we measure our impact. Here are just a few of the many who’ve trusted AgileFever.

Benefits That Set You Apart

Ace NLP projects

Enhance productivity

Improve project quality

Faster implementation

Become proficient in Machine Learning and Deep Learning

Gain tech skills that will rule the next decades

Gain expertise to be in demand across multiple industries – Technology, Healthcare, Education, Hospitality, Finance, Entertainment, Sports, and more

Steps to Getting Certified

1 Step

2 Step

Attend and Complete the Training

3 Step

Complete the Capstone Project

4 Step

Receive Course Completion Certificate

Journeys that keep Inspiring ✨ everyone at AglieFever

The Machine Learning and Deep Learning course exceeded my expectations! It provided a perfect blend of theory and hands-on practice, covering everything from Python fundamentals to advanced topics like neural networks and reinforcement learning. The practical examples and well-structured modules made complex concepts easy to understand. This course from AgileFever has been instrumental in advancing my skills and confidence in the field. Highly recommended for anyone looking to dive into the world of AI and machine learning!

y-man-pic — **Sanjeev Rolyan**
NLP Engineer

This course on Machine Learning and Deep Learning is truly exceptional! The content is comprehensive, covering key topics like supervised and unsupervised learning, neural networks, and natural language processing, all explained in a clear and engaging manner. The hands-on projects helped me apply theoretical knowledge to real-world problems. Whether you’re a beginner or looking to deepen your expertise, this course is a game-changer!

Pete Johnson

AI Expert

The Machine Learning and Deep Learning course was a transformative learning experience. It broke down complex concepts like reinforcement learning and neural networks into simple, actionable steps. The instructors were knowledgeable and supportive, and the practical exercises gave me the confidence to apply these skills in real-world scenarios. A must-take course for anyone serious about a career in AI

man-pic — **Charlotte Deka**
Project Lead

Frequently Asked Questions

1. How will the Machine Learning and Deep Learning course help me?

The Machine Learning and Deep Learning course from AgileFever is designed to equip you with:

Strong Foundations: Learn the basics of Python, key libraries, and essential statistics needed for AI applications.
Comprehensive Knowledge: Gain an understanding of supervised, unsupervised, and reinforcement learning, as well as neural networks and natural language processing.
Practical Skills: Work on real-world projects to apply your knowledge to solve complex problems.
Career Advancement: Build expertise to stand out in fields like AI, data science, and machine learning.
Confidence: Develop the skills to tackle machine learning challenges in various industries effectively.

This course is designed to help you excel in the rapidly evolving world of AI and data-driven decision-making.

2. What are the job roles I can apply for after completing this course?

After completing the Machine Learning and Deep Learning course, you can apply for various job roles, including:

Machine Learning Engineer
Data Scientist
AI Specialist
Deep Learning Engineer
Data Analyst
Natural Language Processing (NLP) Engineer
Business Intelligence Developer
Computer Vision Engineer
AI Research Scientist
Big Data Engineer

3. Which industries need Machine Learning and Deep Learning?

Professionals who excel in Machine Learning and Deep Learning experience a huge demand across a variety of industries such as – IT, Retail, Manufacturing, Energy, Aerospace, Sports, Hospitality, and more.

4. What is the duration of this course?

The Machine Learning and Deep Learning course from AgileFever is available in the format of 40 hours of live training by renowned experts. The course comprises a power-packed curriculum, hands-on exercises, lab activities, and case studies to help learners acquire in-demand tech skills and build a rewarding career in AI.

5. Who are the instructors of the Machine Learning and Deep Learning course by AgileFever?

The instructors of this course are globally renowned AI experts.

6. Is there any prerequisite to attend the Machine Learning and Deep Learning course by AgileFever?

The attendees of this course must have a basic knowledge of Python programming.

7. What are the topics covered in this Machine Learning and Deep Learning course by AgileFever?

This course is powered by an industry-best curriculum that covers important topics such as:

Introduction to Python programming fundamentals
Core Python libraries and their real-world applications
Overview of machine learning principles
Key statistical concepts for machine learning and deep learning
Supervised and unsupervised learning techniques
Introduction to reinforcement learning basics
Overview of neural networks and their operations
Fundamentals of Natural Language Processing (NLP)

8. Who is this Machine Learning and Deep Learning course ideal for?

This course is ideal for:

Data Analysts
Business Analysts
Project Managers
Software Engineers
Product Owners
Tech Enthusiasts

This course helps you to gain a complete understanding of Python fundamentals and develop next-gen skills that give you a massive boost in your tech career.

9. What does corporates use Machine Learning and Deep Learning for?

Machine Learning and Deep Learning are widely used in daily work environments to streamline tasks and improve decision-making. Here are some examples:

Data Analysis: Automating data processing and pattern recognition for better insights.
Predictive Analytics: Forecasting trends, sales, or customer behaviour to guide strategic decisions.
Automation: Enhancing efficiency with intelligent systems for repetitive tasks like data entry or quality control.
Personalization: Tailoring product recommendations or marketing strategies using customer data.
Natural Language Processing: Using chatbots or voice assistants to handle customer queries and improve communication.
Fraud Detection: Identifying anomalies in financial transactions to prevent fraud.
Image and Video Processing: Automating tasks like object detection in surveillance or product defect identification.
Healthcare Applications: Supporting diagnosis through medical imaging analysis and patient data predictions.
Operational Efficiency: Optimizing supply chains, inventory, or resource allocation through intelligent algorithms.

These technologies enhance productivity, accuracy, and innovation across industries.

10. What is the future of ML and DL experts?

The future for Machine Learning (ML) and Deep Learning (DL) experts is incredibly promising, with growing demand across industries. Here’s what to expect:

High Demand: Industries like healthcare, finance, retail, and manufacturing are increasingly adopting AI technologies, leading to a surge in demand for ML and DL experts.
Diverse Opportunities: Professionals can explore roles in autonomous systems, natural language processing, computer vision, robotics, and predictive analytics.
Innovation Leadership: ML and DL experts will be at the forefront of developing cutting-edge solutions like generative AI, personalized medicine, and smart city technologies.
Competitive Salaries: With their specialized skills, ML and DL professionals command lucrative pay packages.
Continuous Learning: The evolving AI landscape ensures continuous upskilling in areas like advanced neural networks, ethical AI, and quantum computing.
Global Impact: Experts will contribute to solving critical global challenges, such as climate modelling, disease prediction, and resource optimization.

The field offers endless possibilities, making it an exciting and impactful career choice.

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Course Overview

Key Highlights

Introduction to Machine Learning and Deep Learning Course Content

Download Syllabus

Schedules for Introduction to Machine Learning and Deep Learning

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Pay Monthly EMI, as low as

Introduction to Machine Learning and Deep Learning Exam Details

Introduction to Machine Learning and Deep Learning is ideal for

Companies that trust Us

Benefits That Set You Apart

Steps to Getting Certified

Journeys that keep Inspiring ✨ everyone at AglieFever

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Introduction to Machine Learning and Deep Learning

Download Brochure

Get Free Consultation

Course Overview

Key Highlights

Introduction to Machine Learning and Deep Learning Course Content

Download Syllabus

Schedules for Introduction to Machine Learning and Deep Learning

Talk to a Learning Advisor

Pay Monthly EMI, as low as

Introduction to Machine Learning and Deep Learning Exam Details

Introduction to Machine Learning and Deep Learning is ideal for

Companies that trust Us

Benefits That Set You Apart

Steps to Getting Certified

Journeys that keep Inspiring ✨ everyone at AglieFever

Frequently Asked Questions

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