Machine Learning With Python Training | Programming Training in Columbia

Machine Learning with Python Training Course Outline

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Python for Machine Learning
• AI vs Machine Learning
• Classification of Machine Learning
• Supervised vs Unsupervised Learning
• Reinforcement Learning
• Datasets for ML
• Popular Sources of ML Datasets
• Kaggle Datasets
• UCI Machine Learning Repository
• Datasets via AWS
• Google’s Datasets Search Engine
• Microsoft Datasets
• Computer Vision Datasets
• Scikit-learn Datasets
• Application of Machine Learning
• Virtual Process Assistance
• Email Spam and Malware Filtering
• Traffic Prediction
• Image Recognition
• Speech Recognition
• Product Recommendation
• Self-Driving Car
• Detection Online Frauds
• Python libraries for Machine Learning
• Numpy
• Pandas
• Matplotib
• Scikit-learn
• Scipy
• Tensorflow
• Pytorch
• Keras

Module 2: Regression

• Introduction to Regression
• Why do we use Regression Analysis?
• Regression Analysis-Related Terminologies
• Types of Regression
• Linear Regression
• Linear Regression Formula
• Types of Linear Regression
• Linear Regression Line
• Polynomial Regression
• Non-Linear Regression
• Model Evaluation Process
• Cross Validation in ML
• Methods Used for Cross-Validation
• Types of Predictive Model
• Confusion Matrix
• Area Under the ROC Curve (AUC-ROC)
• ROC Curve
• AUC Curve
• Application of AUC-ROC Curve
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• K-fold Cross Validation
• Hands-On Linear Regression

Module 3: Classification

• Introduction to Classification
• Classifier
• K-Nearest Neighbours
• How KNN works?
• Decision Tree
• Why to Use Decision Tree?
• Decision Tree Terminologies
• Decision Tree Steps
• Advantages and Disadvantages (Decision Tree)
• Logistic Regression
• Logistic Function (Sigmoid Function)
• Equation of Logistic Regression
• Types of Logistic Regression
• Support Vector Machine (SVM)
• Why it is called Naive Bayes?
• Bayes Theorem
• Advantages and Disadvantages of NB classifier
• Types of Naive Bayes Model
• Random Forest Classification
• Why Random Forest
• Application of Random Forest Classification
• Advantages and Disadvantages of RF
• Hands-On Logistic Regression

Module 4: Unsupervised Learning

• Introduction to Unsupervised Learning
• Types of Unsupervised Algorithm
• Advantages and Disadvantages of UL
• Unsupervised Learning Algorithms
• K-Means Clustering
• Steps for K-means Clustering
• Elbow Method
• Hierarchical Clustering
• Why Hierarchical Clustering
• Density Based Clustering (DBSCAN)
• Apriori Algorithm
• Components of Apriori Algorithm
• Hands-On Clustering

Module 5: Dimensionality Reduction

• Dimensionality Reduction
• Need of Dimensionality Reduction
• Types of Dimensionality Reduction
• Principal Component Analysis (PCA)
• Steps for PCA Algorithm
• What is Variance?
• What is Covariance?
• What is Correlation?
• Application of PCA
• What is P-Value?
• Hypothesis Testing
• Hypothesis in Statistics
• Critical Values
• Z Test
• Chi-Square Test
• ANOVA
• Normal Distribution
• Statistical Significance
• Errors in P-value
• Linear Discriminant Analysis (LDA)
• Working of Linear Discriminant Analysis
• How to Prepare Data for LDA
• Real World Application of LDA
• Difference Between PCA and LDA
• Overfitting and Underfitting in ML
• How to Avoid the Overfitting in the Model
• Hands-On PCA

Module 6: Deep Learning

• Introduction to Deep Learning
• Importance of Deep Learning
• Neural Network Architecture
• Neural Network Components
• Neural Network Algorithms
• Convolutional Neural Networks (CNNs)
• Long Short Memory Network (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFN)
• Multilayer Perceptrons (MLPs)
• Self-Organizing Maps (SOMs)
• Deep Belief Networks (DBNs)
• Restricted Boltzmann Machine (RBMs)
• Artificial Neural Networks (ANNs)
• Feed Forward Neural Network
• Autoencoders
• MNIST
• Deep Learning Applications

Who should attend this Machine Learning with Python Training Course?

The Machine Learning with Python Course in Columbia is highly regarded as one of the most sought-after Programming Courses available and is designed for those who want to get better at understanding the intricacies surrounding Machine Learning through Python for complex algorithm development. The Python with Machine Learning Training Course can benefit individuals such as:

• Data Scientists
• Software Developers
• Data Analysts
• Business Analysts
• Statisticians
• Ethical Hackers
• Cyber Security Professionals 

Prerequisites of the Machine Learning with Python Training Course

There are no formal prerequisites for this Python with Machine Learning Training Course.

Machine Learning with Python Training Course Overview

This course in Machine Learning with Python in Columbia offers an immersive introduction to one of the most groundbreaking and rapidly advancing fields in technology. This course focuses on the intersection of Machine Learning and Python, a powerful programming language that has become a cornerstone in data analysis and machine learning. Understanding these concepts is crucial in today's data-driven world, where automation and predictive analytics are transforming industries.

Grasping the fundamentals of this course is essential for data scientists, analysts, and programmers who aspire to implement machine learning techniques in their work in Columbia. As industries increasingly rely on data for decision-making, professionals equipped with skills from a Programming Training Course in machine learning and Python will find themselves at the forefront of innovation and problem-solving, making them invaluable assets in various technological and scientific fields.

The Knowledge Academy's 2-day course in Machine Learning with Python in Columbia is designed to equip delegates with practical skills and theoretical knowledge. The course seamlessly integrates Python programming with Machine Learning principles, ensuring that participants not only understand the concepts but are also able to apply them in real-world scenarios. This intensive training is a steppingstone towards mastery in a field that is shaping the future of technology.

 Course Objectives

• To provide an in-depth understanding of machine learning concepts and their applications
• To teach practical skills in Python programming specific to machine learning tasks
• To demonstrate the integration of machine learning algorithms with Python code
• To enhance problem-solving skills in data analysis and predictive modeling
• To prepare participants for advanced studies and career opportunities in machine learning

Upon completion of this course in Machine Learning with Python in Columbia, delegates will have gained not only theoretical knowledge but also practical expertise in applying Python to machine learning projects. They will be well-equipped to tackle real-world data challenges and contribute significantly to technological advancements in their respective fields.

What’s included in this Introduction to Machine Learning with Python Training Course?

• World-Class Training Sessions from Experienced Instructors
• Python with Machine Learning Certificate
• Digital Delegate Pack