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Machine Learning with Python Certification Course
Machine Learning algorithms are amazing in the way they utilize the data that machines consume, to educate the very same machines to function on their own, or in other words, make intelligent decisions. The ultimate goal of Machine Learning with Python, which is indeed ideal in its nature, is to achieve zero error in the functionality of machines. But algorithms do need a language to be written in, right? Among all the computer languages, Python is considered the most suitable language to code Machine Learning algorithms.
Therefore, we bring you machine learning online course that combines the power of one of the most popular computer languages of all times – Python – with Machine Learning algorithms, to help you learn smart automation, with higher degree of code reuse and better modularity for applications.
What would you learn in certification course for Machine Learning with Python in Dallas?
Machine Learning with Python certification course has literally infinite applications in the near future, as well as the very present. Some of the key things you will learn through our machine learning certification course are:
– Trending skills such as regression, classification, clustering, sci-kit learn, SciPy, and much more.
– Real-world applications of Python in different sectors.
– Supervised and Unsupervised Learning Algorithms
– To accomplish various data mining and data wrangling in Python
– Building deep learning and reinforcement learning in machines.
Learning Objectives
For Individuals
- Access to self-paced, high-quality learning content and experiences that have been developed by machine learning experts in the machine learning field.
For Businesses
- They would be taught with well-blended models
- Machine Learning course would offer them high-quality teaching and assistance
360EduKraft's Training Key Features:
Machine Learning with Python Course Description
Machine Learning has numerous applications in different sphere of technology, especially the ones where automation is imminent. Machine Learning with Python is already in great demand by most of the enterprises transitioning from manual to automated processes. Hence, to be future-ready, this course is vital for you; no more, no less.
– Anyone with basic knowledge of Python
– Any Python developer looking to expand their skillset.
– Learners familiar with concepts of Computer Science
Some of the notable advantages that you would gain by mastering Machine Learning with Python are:
– Strong fundamentals of Machine Learning.
– Advanced knowledge of Python
– Learn to combine Machine Learning with Python to accomplish automation tasks.
– Certified expertise in writing Machine Learning algorithms with Python.
Here are some of the perquisites for our certification course in Machine Learning in Python:
– Strong understanding of Mathematics
– A PC with Windows 7 and above/Mac OS X 10 and above, Intel i5 processor, minimum 4GB of RAM, and 3-5 GB of disk space.
Machine Learning Online Training Curriculum
Machine Learning Course Topics
>> Introduction to Machine Learning
>> Introduction to Data Science
>> Introduction to Python
>> Iterative Operations & Functions in Python
>> Data summary & visualization in Python
>> Getting Started with Machine Learning
>> Back to Basics (Maths with Statistics)
>> Data Processing for Machine Learning
>> Advanced Machine Learning Algorithm
>> Case Study & Projects
>> Assignments
Topics:
- Key Elements of Data Science
- Data Warehousing
- Business Intelligence
- Data Visualization
- Data Mining
- Machine Learning
- Artificial Intelligence
- Cloud Computing
- Big Data
Topics:
- What is Artificial Intelligence & its importance
- Artificial Intelligence vs Machine Learning
Topics:
- What is Machine Learning (ML)?
- How machines learn
- Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement.
- Basics of Classification, Regression and Clustering algorithms
- Creating your first Prediction Model
- Training & Model evaluation
- Choosing Machine Learning algorithm
Topics:
- Linear Algebra (Vectors, Matrix, Eigen Values)
- Probability and Statistics
- Hypothesis testing
- Optimization
Topics:
- What is Python
- Working with Python
- Basic scripts on
- Read, write, data handling
- Loops
- Conditions (if-else)
- Function
- Code modularization
- Scikit-Learn package
- Basic visualization
Topics:
- Data Collection & Preparation
- Data Mugging
- Outlier Analysis
- Missing value treatment
- Feature Engineering
- Data Transformation
- Normalization vs Standardization
- Creating Dummies
- Dimensionality Reduction
- Principal Component Analysis
Topics:
- Supervised Machine Learning algorithms
- Linear Regression
- Logistic Regression
- Decision/Classification Tree
- Ensemble Models
- Bagging
- Boosting
- Random Forest
- K-Nearest Neighbours (KNN)
- Naive Bayes
- Neural Network (Deep Learning)
- Support Vector Machine
- Unsupervised Machine Learning algorithms
- Clustering with K-means Clustering
- Bias-Variance Trade off
- Regularization
- Parameter tuning & grid search optimization
Real life cases with Python
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