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Machine Learning Essentials with Python (TTML5506-P)

4.6 out of 5 rating

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Duration

3 Days

18 CPD hours

About this course

This intermediate level course is geared for experienced programmers, data analysts, and aspiring data scientists new to AI and machine learning. Basic python experience is required.

Overview

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll learn how to:
- Master the Python Programming for Data Science: Gain an in-depth understanding of Python's role in data science and AI, including proficiency in using key Python data science libraries like Pandas, NumPy, and Matplotlib.
- Understand the Fundamentals of AI and Machine Learning: Develop a strong grasp of AI and Machine Learning concepts, their applications, and how to differentiate between AI, Machine Learning, and Deep Learning.
- Dive into Supervised and Unsupervised Learning Techniques: Acquire hands-on skills to conduct Regression Analysis, Binary Classification, and k-means Clustering - key methods in Supervised and Unsupervised Learning.
- Apply Data Wrangling and Preprocessing Techniques: Learn to handle missing data, outliers, and categorical data
effectively and perform feature scaling and normalization - crucial steps in Machine Learning projects.
- Create and Evaluate Machine Learning Models: Get a grip on the lifecycle of AI projects, including model creation,
evaluation, validation, and the application of Ensemble Learning techniques.
- Understand and implement crucial data preprocessing techniques in Python: Attendees will acquire the ability to handle missing data, outliers, and categorical data, essential for creating reliable machine learning models.
- Develop competency in creating and interpreting data visualizations: Students will learn how to leverage Python's
powerful libraries such as Matplotlib and Seaborn to create compelling visualizations and extract meaningful insights from data.
- Construct a machine learning pipeline for real-world applications: Participants will gain the practical know-how to carry a machine learning project from initial data collection through to final model deployment, using Python.
- (Optional / Bonus Topics): Implement AI into Real-World Applications: By the end of the course, you'll be able to build applications that integrate AI functionalities, using popular Python frameworks and modern AI technologies, like GPT-4.

Description

Dive into the fascinating world of AI and Machine Learning with our three-day, comprehensive course, "Machine Learning Essentials with Python". This course, perfect for basic Python developers, equips you with the skills to leverage Python for intelligent applications like data analysis, predictive modeling, automation, and chatbots, transforming your project capabilities. Participants will get hands-on experience with popular machine learning algorithms, exploring their potential applications and limitations.
Our highly-experienced instructors will share their practical expertise, guiding you through learning these new skills and
empowering you to confidently apply them in your job or role. Throughout the course you'll explore learning and using
Supervised and Unsupervised Learning techniques, Data Wrangling and Preprocessing, Ensemble Learning, and Model
Evaluation and Validation. Hands-on labs replicating real-world scenarios form a core part of the learning experience, ensuring you acquire practical, applicable skills. Each hands-on lab will provide you with practical experience using innovative skills with cutting edge tools, applied in a practical and meaningful way.
If time permits, you'll also explore innovative technologies such as Generative AI with GPT-4, as well as practical AI integration into applications, highlighting the tools and technologies transforming the AI landscape. By the end of the course, you will not only have gained a deep understanding of AI and Machine Learning concepts but also the ability to apply these in your work context, leading to more complex and impactful projects.

Prerequisites

This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into
AI and Machine Learning. It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on
understanding of AI's impact on product development and business strategy.

Getting Started
  • Installation: Getting Started and Overview
  • LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container)
  • Python Refresher
  • Introducing the Pandas, NumPy and Scikit-Learn Library
Statistics and Probability Refresher and Python Practice
  • Types of Data
  • Mean, Median, Mode
  • Using mean, median, and mode in Python
  • Variation and Standard Deviation
Probability Density Function; Probability Mass Function; Naive Bayes
  • Common Data Distributions
  • Percentiles and Moments
  • A Crash Course in matplotlib
  • Advanced Visualization with Seaborn
  • Covariance and Correlation
  • Conditional Probability
  • Naive Bayes: Concepts
  • Bayes Theorem
  • Naive Bayes
  • Spam Classifier with Naive Bayes
Predictive Models
  • Linear Regression
  • Polynomial Regression
  • Multiple Regression, and Predicting Car Prices
  • Logistic Regression
  • Logistic Regression
Machine Learning with Python
  • Supervised vs. Unsupervised Learning, and Train/Test
  • Using Train/Test to Prevent Overfitting
  • Understanding a Confusion Matrix
  • Measuring Classifiers (Precision, Recall, F1, AUC, ROC)
  • K-Means Clustering
  • K-Means: Clustering People Based on Age and Income
  • Measuring Entropy
  • LINUX: Installing GraphViz
  • Decision Trees: Concepts
  • Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • Using SVM to Cluster People using scikit-learn
Recommender Systems
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Similar Movie
  • Better Accuracy for Similar Movies
  • Recommending movies to People
  • Improving your recommendations
KNN and PCA
  • K-Nearest-Neighbors: Concepts
  • Using KNN to Predict a Rating for a Movie
  • Dimensionality Reduction; Principal Component Analysis (PCA)
  • PCA with the Iris Data Set
Reinforcement Learning
  • Reinforcement Learning with Q-Learning and Gym
Dealing with Real-World Data
  • Bias / Variance Tradeoff
  • K-Fold Cross-Validation
  • Data Cleaning and Normalization
  • Cleaning Web Log Data
  • Normalizing Numerical Data
  • Detecting Outliers
  • Feature Engineering and the Curse of Dimensionality
  • Imputation Techniques for Missing Data
  • Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
  • Binning, Transforming, Encoding, Scaling, and Shuffling
Experimental Design / ML in the Real World
  • Deploying Models to Real-Time Systems
  • A/B Testing Concepts
  • T-Tests and P-Values
  • Hands-on With T-Tests
  • Determining How Long to Run an Experiment
  • A/B Test Gotchas
Capstone Project
  • Group Project & Presentation or Review
Deep Learning and Neural Networks
  • Deep Learning Prerequisites
  • The History of Artificial Neural Networks
  • Deep Learning in the TensorFlow Playground
  • Deep Learning Details
  • Introducing TensorFlow
  • Using TensorFlow
  • Introducing Keras
  • Using Keras to Predict Political Affiliations
  • Convolutional Neural Networks (CNNs)
  • Using CNNs for Handwriting Recognition
  • Recurrent Neural Networks (RNNs)
  • Using an RNN for Sentiment Analysis
  • Transfer Learning
  • Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
  • Deep Learning Regularization with Dropout and Early Stopping
  • The Ethics of Deep Learning
  • Learning More about Deep Learning
Additional course details:

Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the ITS Data Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

What people say about us

Global Schedule

GTR = Guarenteed to Run

22 Jan 25
15:00 - 23:00Live Online2,395
31 Mar 25
15:00 - 23:00Live Online2,395

02 Jun 25
15:00 - 23:00Live Online2,395
18 Aug 25
15:00 - 23:00Live Online2,395
15 Oct 25
15:00 - 23:00Live Online2,395
01 Dec 25
15:00 - 23:00Live Online2,395

Find out more about this course

Interested in alternative dates? Would like to book a private session of this course for your company? Or for any other queries please simply fill out the form below.