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Machine Learning with Python Course Details
Zx Academy's Machine Learning with Python Certification Course training helps a candidate attain expertise in many Machine Learning algorithms like clustering, regression, random forest, decision trees, Q-Learning, and Naive Bayes. This training ensures the candidate understands the concepts of Time Series, Statistics, and various classes of Machine Learning algorithms like unsupervised, supervised, and reinforcement algorithms.
The comprehensive Machine Learning (ML) and Python course explain the basic programming and statistics required to work on the problems of ML. The candidate will learn ML with Python by beginning with the packages required for Machine Learning. Also, it covers statistical distributions and explains various types of data. Machine Learning with Python course teaches the candidate a type of ML called reinforcement learning.
Highlights of Zx Academy Training:
- 24/7 Support throughout the training period
- Live online classes
- Training under industry experts
- Free study material
- Flexible timings to all our students
What will you learn in Machine Learning with Python Certification training?
After completion of the Machine Learning with Python certification training, you will learn:
- Python and its Libraries
- Filtering and Sorting
- Loops and Functions
- Filtering and Sorting
- Summarising Data
- Working on Filtering and Adding Columns
- Visualization Libraries
- Types of Data
- Descriptive Statistics
- Basics of Statistics
- Measures of Dispersion
- Measures of Central Tendency
- Q-Learning
- Framework of Reinforcement Learning
Who should take this Machine Learning with Python Certification training?
The Machine Learning with Python Certification training course is suited for:
- Developers aspiring to become a "Machine Learning Engineer"
- Business Analysts who want to know the techniques of Machine Learning
- Analytics Managers
- Python Professionals
What are the prerequisites for taking Machine Learning with Python Certification training?
The prerequisites for taking Machine Learning with Python certification training are:
- Fundamentals of Data Analysis
- Basics of Python Language
Why should you go for Machine Learning with Python Certification training?
The Machine Learning with Python certification course employs theories and techniques drawn from several fields within the areas of statistics, mathematics, computer science, and information science. This course exposes the candidate to various classes of Machine Learning algorithms such as unsupervised, supervised, and reinforcement algorithms. After completion of this training, you will be able to automate data analysis using Python, validate Machine Learning algorithms, and learn techniques for predictive modeling.
Salary Trends:
According to Glassdoor, the average salary of a Machine Learning Engineer is Rs.10,00,000 per annum.Are you excited about this?
Machine Learning with Python Curriculum
What does Data Science involve?
Era of Data Science
Business Intelligence vs Data Science
Life cycle of Data Science
Tools of Data Science
Introduction to Python
What is Data Extraction
Types of Data
Raw and Processed Data
Data Wrangling
Exploratory Data Analysis
Visualization of Data
What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Gradient descent
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
LDA
How Naïve Bayes works?
Implementing Naïve Bayes Classifier
What is Support Vector Machine?
Illustrate how Support Vector Machine works?
Hyperparameter optimization
Grid Search vs Random Search
Implementation of Support Vector Machine for Classification
What is K-means Clustering?
How K-means algorithm works?
How to do optimal clustering
What is C-means Clustering?
What is Hierarchical Clustering?
How Hierarchical Clustering works?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How Recommendation Engines work?
Collaborative Filtering
Content Based Filtering
Why Reinforcement Learning
Elements of Reinforcement Learning
Exploration vs Exploitation dilemma
Epsilon Greedy Algorithm
Markov Decision Process (MDP)
Q values and V values
Q – Learning
α values
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity
ACF & PACF
Need of Model Selection
Cross – Validation
What is Boosting?
How Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting
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Projects on Machine Learning with Python
Iris Flower Classification:
Project Description: The Iris dataset is an iconic example of classification data. With measurements for various iris flowers, its primary goal is to develop a machine learning model capable of classifying them according to their features. This project's aim is to classify these flowers accordingly.
Skills Acquired: Data preprocessing, feature selection, model selection (e.g. decision trees or k-nearest neighbors), model evaluation.
Resources: You can locate Iris dataset in popular Python libraries such as scikit-learn. Online tutorials and courses often cover this project due to its ease and educational value.
Predicting House Prices:
Project Description: In this project, you'll work with a dataset containing information about houses - features like bedrooms, square footage and location are key features - in order to develop a machine learning model capable of predicting house prices based on these features. Your aim will be to predict them with great precision!
Skills Gained: Data cleansing and preprocessing; regression modeling (e.g. linear regression); feature engineering; model evaluation.
Resources: When looking for real estate datasets, Kaggle is an excellent place to search, as are libraries such as pandas and scikit-learn. There are also numerous tutorials and courses covering similar projects online.
Project Resources
Machine Learning with Python Certification
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