Machine Learning Course
This comprehensive machine learning course in Coimbatore offers an in-depth exploration of basic concepts and advanced techniques in the field of artificial intelligence. This course at Edukators provides a basic introduction to machine learning, covering core algorithms and techniques required to understand and apply this field. Students will learn practical skills in data preparation, model selection, and performance optimization via a combination of lectures and hands-on activities. By the end of the program, students will have a solid understanding of machine-learning principles and be able to implement basic machine learning solutions to real-world problems.
Description
About Machine Learning Course
The Machine Learning Course at Edukators in Coimbatore offers practical training in applying algorithms and statistical models to analyze data and make informed decisions. However, the course emphasizes hands-on experience with industry-standard tools. For instance, students gain expertise in data preprocessing, model development, and evaluation. Above all, it’s led by experienced professionals who provide personalized guidance, ensuring students can confidently apply techniques in real-world scenarios. After completing the course, students are well-prepared to excel in using data for predictive analysis and problem-solving. Similarly, previous students have effectively applied their learning in various sectors, demonstrating the course’s practical value. For instance, they have achieved success across different industries, highlighting the course’s real-world relevance.
Know About Our Machine Learning Course Trainers
Our Machine Learning Course trainers at Edukators in Coimbatore are experienced professionals skilled in machine learning algorithms, data processing, and developing models.For example, they use AI techniques to solve real-world problems, making learning practical. Above all, this practical approach is emphasized. In addition, students can apply their skills similarly across various contexts. Most importantly, they are dedicated teachers focused on helping you excel in machine learning. Additionally, they provide personalized guidance and use industry-standard tools. After completing our course, you’ll confidently apply techniques in different areas. Therefore, their mentorship has, in other words, been key to many students’ success in mastering machine learning..
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Course Details
- What is Script?
- What is a program?
- Types of Scripts
- Difference between Script & Programming Languages
- Features of Scripting
- Limitation of Scripting
- Types of programming Language ParadigmsWhat is Automation testing
- Why we need automation testing
- Importance of automation Testing
- Difference between manual and automation testing
- Automation testing process
- Python Overview
- History of Python & Python Versions
- Python Features
- Types of python - CPython, jPython, PyPy
- Area of application of python
- Why do we need python?
- How python script works
- Python 2.7 and Python 3 difference
- What is PSF?
- What is pip? and how to use?
- What is IDE?
- Environment setup - Installation of Python
- Writing first script in python
- Interactive and Script Mode programming
- Compiler and interpreter difference
- How to make executable python file?
- What is syntax?
- What is variable?
- What is identifiers?
- What is keywords?
- What is comment and its types?
- Usage of Quatations
- How to use help and dir functions?
- Static typing and dynamic typing
- What is data type?
- String
- Integers
- List
- Tuple
- Dictionary
- Set & Frozen set
- Boolean data type
- Built in function of data types
- Mutable and immutable
- Arithmetic operators
- Comparison operators
- Assignment Operators
- Logical Operators
- Bitwise Operators
- Membership Operators
- Identity Operators
- Arithmetic Operators
- Ternary operator & nested ternary operator
- Grouping Statements: Indentation and Blocks
- If statement
- if else statement
- elif statement
- nested if, if else, elif statement
- one line if statement
- pass keyword
- for loop with else
- while loop with else
- continue and break
- range and xrange difference
- list, tuple, dict comprehension
- Built in function
- User defined function
- Nested function
- Recursive function
- *args and **kwargs function
- Global and nonlocal keywords usage
- Lambda function
- Reduce, map, filter functions
- Python closure
- Decorators
- Chaining Decorators
- Python Generators
- File handling in python
- Type of modes in file
- Example for writing a file
- Example for reading a file
- Example for reading and writing a image file
- What is exception?
- Try and Except Statement - Catching Exceptions
- Python Exceptions List
- Assertions in Python
- Try with Else Clause
- Finally Keyword in Python
- User-Defined Exceptions
- Handling multiple exception
- What is Modular Programming?
- What are Modules in Python?
- How to Import Modules in Python?
- Python import Statement
- Importing and also Renaming
- Python from...import Statement
- Locating Path of Modules
- Namespaces and Scoping
- Basic module writing example
- OOPs and Principles of object-oriented programming
- Object-oriented vs Procedure-oriented Programming languages
- Class
- Method
- Attributes types
- Object
- Parameter and Attributes difference
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
- Composition
- What is database
- Database vs. file system
- Why to Learn DBMS?
- Types of databases (relational, NoSQL, NewSQL)
- Types of DBMS Architecture
- Keys
- SQL Data Types overview
- Hierarchical model
- Network model
- Relational model
- Object-oriented model
- NoSQL models (Document, Key-Value, Column-Family, Graph)
- ACID properties
- Selecting specific columns
- Using DISTINCT to remove duplicates
- Renaming columns with aliases
- Filtering records using conditions
- Logical operators (AND, OR, NOT)
- Comparison operators (=, >, <, >=, <=, !=)
- Subqueries in SELECT, FROM, and WHERE clauses
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- FULL OUTER JOIN
- SELF JOIN
- CROSS JOIN
- Aggregate Functions - COUNT, SUM, AVG, MIN, MAX
- GROUP BY Clause
- HAVING Clause
- CASE Statements
- String Functions - CONCAT, LENGTH, SUBSTRING, REPLACE
- Date Functions - DATEPART, DATEADD, DATEDIFF, FORMAT
- Numeric Functions - ROUND, CEIL, FLOOR, ABS
- Ranking Functions - ROW_NUMBER, RANK, DENSE_RANK
- Aggregate Functions Over Partitions - SUM, AVG, MIN, MAX, COUNT with OVER() clause
- Analytic Functions - LEAD, LAG, FIRST_VALUE, LAST_VALUE
- Handling NULL Values - IS NULL, IS NOT NULL., COALESCE and NULLIF functions
- Removing Duplicates - DISTINCT, ROW_NUMBER
- Union Operations - UNION and UNION ALL
- Set Operations - INTERSECT, EXCEPT
- What is a Stored Procedure?
- Stored Procedure Syntax
- Execute a Stored Procedure
- User-Defined Functions - Scalar and table-valued functions.
- Using libraries like SQLAlchemy, pandas, and psycopg2 in Python
- Using RODBC or DBI in R
- What is Pandas?
- Why We Need Pandas?
- Data Structures: Series and DataFrame
- Differences between Series and DataFrame
- Creating Data Structures (Series and DataFrame)
- Indexing and Selecting Data
- Slicing Data
- Locating Rows and Columns using loc and iloc
- Handling Missing Data (Identifying, Filling, Dropping)
- Data Cleaning (Removing Duplicates, Renaming Columns, Changing Data Types)
- Applying Functions to Data (apply, map)
- String and Text Data Manipulation (String Methods, Handling Text Data in Columns)
- Grouping and Aggregation (groupby, Aggregation Functions)
- Pivot Tables and Cross-Tabulation
- Merging DataFrames (merge, concat, join)
- Data Reshaping (Melting, Pivoting, Stacking, Unstacking)
- Data Import and Export (CSV, JSON, Excel)
- Exploratory Data Analysis (Descriptive Statistics, Value Counts, Correlation and Covariance)/
- Handling Time Series Data (Date Range, Resampling, Shifting, Lagging)
- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms, Box Plots)
- Advanced Data Manipulation (Window Functions, MultiIndex, Efficient Data Structures)
- Performance Optimization (Memory Usage, Vectorization)
- Feature Engineering (Creating New Features, Handling Date and Time Features, Encoding Categorical Variables)
- Integration with Machine Learning Libraries (scikit-learn, Exporting Data)
- What is NumPy?
- Why use NumPy?
- Arrays
- Differences between NumPy arrays and Python lists
- Creating arrays from lists
- Using arange and linspace
- Using ones, zeros, and eye
- Creating arrays from existing data
- Creating random arrays
- Element-wise operations
- Array broadcasting
- Basic arithmetic operations
- Universal functions (ufuncs)
- Aggregation functions (sum, mean, std, var)
- Basic indexing and slicing
- Boolean indexing
- Fancy indexing
- Iterating over arrays
- Working with multidimensional arrays
- Reshaping arrays
- Concatenation and stacking
- Splitting arrays
- Transposing and swapping axes
- Broadcasting rules and advanced broadcasting
- Aggregation functions (sum, mean, std, var)
- Mathematical functions (sqrt, exp, log)
- Trigonometric functions
- Linear algebra functions (dot product, matrix multiplication)
- Random number generation and setting seeds
- Reading and writing arrays to/from files
- Loading and saving text files
- Loading and saving binary files
- Using loadtxt and savetxt
- Using save and load
- Vectorization
- Memory layout and data alignment
- Using numexpr for faster computations
- Profiling and optimizing NumPy code
- Understanding and avoiding pitfalls with NumPy performance
- What is Machine Learning?
- Why is Machine Learning Important?
- Applications of machine learning
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Key Terminology (Features, Labels, Model, Training, Testing, Overfitting, Underfitting)
- Understand what is machine leanrning workflow
- Linear Algebra - Vectors and Scalars
- Linear Algebra - Matrices
- Linear Algebra - Eigenvalues and Eigenvectors
- Calculus - Derivatives and Differentiation
- Calculus - Integrals and Integration
- Calculus - Partial Derivatives
- Probability - Basic Probability Concepts - Probability, Conditional Probability, Bayes' Theorem
- Probability - Probability Distributions - Normal, Binomial, Poisson
- Probability - Expected Value and Variance - Expected Value (Mean), Variance, Standard Deviation
- Statistics - Descriptive, Inferential, Correlation and Causation
- Optimization - Optimization Techniques
- Optimization - Convergence
- Optimization - Constraints and Regularization
- Problem Definition
- Data Collection
- Data Exploration and Preparation
- Feature Engineering
- Data Splitting (Train/Test Split)
- Model Selection
- Model Training
- Model Evaluation
- Model Tuning and Optimization
- Model Deployment
- Monitoring and Maintenance
- Cross-Validation Techniques (k-Fold, Leave-One-Out)
- Evaluation Metrics for Classification - Accuracy
- Evaluation Metrics for Classification - Precision
- Evaluation Metrics for Classification - Recall
- Evaluation Metrics for Classification - F1-Score
- Evaluation Metrics for Classification - ROC-AUC
- Evaluation Metrics for Regression - Mean Absolute Error (MAE)
- Evaluation Metrics for Regression - Mean Squared Error (MSE)
- Evaluation Metrics for Regression - R-squared
- Confusion Matrix
- Precision-Recall Curve
- Bias-Variance Tradeoff
- Ensemble Methods - Bagging
- Ensemble Methods - Boosting
- Ensemble Methods - Stacking
- Dimensionality Reduction - PCA
- Dimensionality Reduction - LDA
- Dimensionality Reduction - t-SNE
- Model Interpretability - SHAP
- Model Interpretability - LIME
- Hyperparameter Tuning - Grid Search
- Hyperparameter Tuning - Random Search
- Hyperparameter Tuning - Bayesian Optimization
- Transfer Learning
- What is Natural Language Processing?
- Applications of NLP
- Tokenization
- Stop Word Removal
- Stemming and Lemmatization
- Text Normalization (lowercasing, removing punctuation)
- Handling Special Characters and Numbers
- Bag of Words (BoW)
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word Embeddings (Word2Vec, GloVe, FastText)
- Sentence Embeddings
- N-grams
- Sentiment Analysis
- Spam Detection
- Topic Modeling (LDA, NMF)
- Part-of-Speech Tagging
- Named Entity Recognition (N
- Chunking (Shallow Parsing)
- Language Modeling
- Sequence-to-Sequence Models
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Gated Recurrent Units (GRUs)
- Attention Mechanisms
- Transformer Models (BERT, GPT, RoBERTa)
- Text Generation with RNNs and Transformers
- Text Summarization (Extractive and Abstractive Methods)
- Statistical Machine Translation (SMT)
- Neural Machine Translation (NMT)
- Sequence-to-Sequence Models
- Speech-to-Text
- Text-to-Speech
- Acoustic Models
- Language Models for Speech Recognition
- Precision, Recall, and F1-Score
- BLEU Score for Machine Translation
- ROUGE Score for Text Summarization
- Perplexity for Language Models
- NLTK (Natural Language Toolkit)
- spaCy
- Gensim
- Hugging Face Transformers
- OpenNLP
- What is Deep Learning?
- Applications of Deep Learning
- Structure of a Single Neuron
- Mathematical Representation of a Neuron
- Weights, Biases, and Outputs
- Sigmoid
- Tanh
- ReLU (Rectified Linear Unit)
- Leaky ReLU
- Softmax
- Derivatives of Activation Functions
- The XOR Problem
- Architecture of MLP: Input, Hidden, and Output Layers
- Importance of Non-Linearity in Hidden Layers
- Concept of Gradient Descent
- Types: Batch, Stochastic (SGD), Mini-Batch
- Learning Rate and its Impact
- Purpose and Importance
- Step-by-Step Explanation
- Chain Rule in Backpropagation
- Definition and Purpose
- Forward and Backward Passes
- Building and Using Computational Graphs
- Forward and Backward Passes
- Gradient Computation and Weight Updates
- Importance of Initialization
- Methods: Uniform, Normal, Xavier, He Initialization
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Gated Recurrent Units (GRUs)
- Generative Adversarial Networks (GANs)
- Dropout Batch Normalization Weight Regularization (L1, L2)
- Adam
- RMSprop
- AdaGrad
- Concept and Applications
- Pre-trained Models and Fine-Tuning
Benefits of taking course with us!
Taking a machine learning course can provide several benefits. Firstly, it provides deep knowledge of machine learning concepts, allowing you to use data-driven insights to solve real-world challenges. Above all, this foundational understanding is crucial. In addition, you’ll be able to apply these skills effectively across various scenarios. Through hands-on experience and practical exercises, you’ll learn how to use popular machine learning algorithms and tools, improving your technical abilities and employability in a quickly changing in job market. Furthermore, completing a machine learning course can boost your resume and open new career opportunities in data science, AI, and predictive analytics. Above all, it provides high-demand skills and knowledge, preparing you for growth in these fields.
Requirements
Job Opportunities For Machine Learning Course
Completing a machine learning course opens up a wide range of job opportunities across various industries. Here are some common job roles in the field of machine learning:
- Data Scientist
- Machine Learning Engineer
- AI Research Scientist
- Data Analyst
- Business Intelligence Analyst
- AI / Machine Learning Consultant
- Software Engineer (with ML Specialization)
- Research Scientist
- Computer Vision Engineer
- Natural Language Processing (NLP) Engineer
FAQ's about Machine Learning Course at Edukators
Python and R are the most popular programming languages for machine learning because of their huge libraries and community support. Python is especially popular because of libraries like TensorFlow, Scikit-learn, and PyTorch.
A machine learning course often includes core topics including supervised and unsupervised learning, regression, classification, clustering, dimensionality reduction, model assessment, and practical application using popular libraries and frameworks.
The time it takes to become proficient in machine learning varies depending on individual learning pace, prior experience, and the depth of knowledge desired.
You may improve your machine learning abilities by doing coding exercises, experimenting with datasets, building projects, and collaborating with others in the machine learning community.
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