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Machine Learning and Deep Learning Training

The Machine Learning and Deep Learning training by AgileFever 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 the basics of natural language processing (NLP).

  • 42 hours of live online instructor-led training
  • Get trained by globally renowned AI experts
  • Interactive learning with hands-on exercises and lab activities
  • 24/7 dedicated support
  • Supercharge your career in AI
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Machine Learning and Deep Learning Course Overview

Machine Learning and Deep Learning course from AgileFever is a 42-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 yo help learners acquire future-ready skills and become in-demand tech professionals experiencing massive demand across top MNCs.

Machine Learning and Deep Learning Training 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)

Curriculum

  • 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
  • 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
  • What is machine learning?
  • Introduction to machine learning
  • Types of machine learning
  • Basic Probability required for machine learning
  • Linear Algebra required for machine learning
  • 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
  • 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
  • Understanding reinforcement learning
  • Algorithms associated with reinforcement learning
  • Q-learning Model
  • 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
  • Basics of NLP (Natural Language Processing)
  • Removing Stop Words
  • Stemming and lemmatization
  • Parts of Speech Tagging
  • TFIDF Vectorizer
  • Sentiment Analysis
  • SMS Spam Classifier

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.

  1. 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.
  2. 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.
  3. Plot the data to understand the survival rate or mortality rate of the recent pandemic from the case study and the data given.

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Who can attend the Machine Learning and Deep Learning course?

AgileFever’s Machine Learning and Deep Learning course is ideal for:

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

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Machine Learning and Deep Learning 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.

Learners need to have a basic knowledge of Python programming.

Benefits

Corporate Benefits

  • Ace NLP projects  
  • Enhance productivity 
  • Improve project quality 
  • Faster implementation

Individual Benefits

  • 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

Certification Process

Step 1: Register for the Machine Learning and Deep Learning course with AgileFever

Step 2: Attend AgileFever’s 42 hours of live instructor-led classes

Step 3: Upon successful completion of the course, you will receive certification from AgileFever

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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.

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

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.

The Machine Learning and Deep Learning course from AgileFever is available in the format of 42 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.

The instructors of this course are globally renowned AI experts.

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

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)

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 gives you a massive boost in your tech career.

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 behavior 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.

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 modeling, disease prediction, and resource optimization.

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

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