Knowledgehut INC

Machine learning with Python

Knowledgehut INC
En Ligne
  • Knowledgehut INC

*Prix indicatif
Montant original en GBP :
£GB 2049

Infos importantes

Typologie Formation
Niveau Intermediate
Méthodologie En ligne
Heures de classe 50h
Début Dates au choix
Campus virtuel Oui
Envoi de matériel d'apprentissage Oui
Service d'information Oui
Classes virtuelles Oui
  • Formation
  • Intermediate
  • En ligne
  • 50h
  • Début:
    Dates au choix
  • Campus virtuel
  • Envoi de matériel d'apprentissage
  • Service d'information
  • Classes virtuelles

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Infos importantes
Quels sont les objectifs de la formation?

1. Statistical Learning
2. Python for Machine Learning
3. Fundamentals of Machine Learning
4. Optimization Techniques
5. Machine Learning Algorithms
6. Dimensionality Reduction
7. Neural Networks
8. Ensemble Learning

Cette formation est-elle faite pour moi?

1. if you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem if you are a software or data engineer interested in learning the fundamentals of quantitative analysis and machine learning

Quel est le processus d'inscription?

An Expert from Knowledgehut will take it forward.

Conditions: For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.





Dates au choix Inscription ouverte

Qu'apprend-on avec cette formation ?

Scikit Learn


1. statistical Learning : Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
2. Python for Machine Learning : Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn
3. Introduction to Machine learning : Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
4. Optimization: Maxima and Minimal
  • Cost Function
  • Learning Rate
  • Optimization Techniques
5. Supervised Learning : Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
6. Unsupervised learning : Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
7. Ensemble Techniques: Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
8. Recommendation systems: Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study