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Machine Learning Certification Course Delivered by Senior Data Scientists with 10 Industry Projects
Machine Learning Course | Become a IABAC Certified Data Scientist
2000+ Ratings
3000+ Happy Learners
Machine Learning Videos
Skills Covered in Machine Learning Course
Supervised machine learning
Unsupervised machine learning
An in-depth discussion on time series modeling
Linear regressions
Logistic regression
An in-depth discussion on Kernel SVM
An in-depth discussion on KMeans clustering
Details about Naive Bayes
Details about decision tree
Details about random forest classifiers
Bagging techniques
Boosting techniques
Course Key Features
Access to state-of-the-art labs
Access to four 4 real-world industry projects
More than 25 hands-on projects are available
Applicants can get back 100% of their money
Mentoring sessions headed by industry experts are available
The course will span 58 hours in total and will mostly consist of applied learning
Our Alumni Working in
Machine Learning Course Reviews
Barun Mishra
Software Engineer
Excellent teaching. Trainer is very friendly and kind to us.. Best training for Machine Learning.
Guruprasad
Software Engineer
This is the institute for learning the courses properly. You will get the full knowledge about
the courses. The way of teaching is best.Thank you apponix
Harish
Software Engineer
It was a good learning experience with apponix. Faculty is good. Staff are also good and friendly
Vinod Kumar
Software Engineer
Good institute for Initial start to learn course with very good trainer and all terminologies are explained with examples and correlated to current company requirements.
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Classroom Training
Classroom Training
Fees & Training Options
Class room Training
₹
- Interactive Classroom Training Sessions
- 60+ Hrs Practical Learning
- Guaranteed 5 Interview Arrangements
- Excellent Classroom Infrastructure
- Assured Job Placement
- Delivered by Working Professionals
Online Training
₹
- All benefits available in self-paced learning training method along with 90 days access to online classes with flexible timings.
- All online classes will be live and they will be taken by veterans of Machine Learning who are actively associated with the sector.
Machine Learning Training Syllabus
Prerequisites
This course is best suited for –
- Fresh Graduates
- Analytics Managers
- Information Architects
- Business Analysts
- Software developers and
- Web developers.
With this course, the aforementioned will be able to become Data Scientists and secure a High-paying job in the Machine Learning sector easily. In terms of prerequisites, applicants would need to have a strong grip on statistics and advanced mathematics.
They would also need to be familiar with programming languages like Python.
Machine Learning Course Syllabus
Machine Learning
1. Course Introduction
- Course Introduction
2. Introduction to AI and Machine Learning
- Learning Objectives
- The emergence of Artificial Intelligence
- Artificial Intelligence in Practice
- Sci-Fi Movies with the concept of AI
- Recommender Systems
- Relationship Between Artificial Intelligence, Machine Learning, and Data Science - Part A
- Relationship Between Artificial Intelligence, Machine Learning, and Data Science - Part B
- Definition and Features of Machine Learning
- Machine Learning Approaches
- Machine Learning Techniques
- Applications of Machine Learning - Part A
- Applications of Machine Learning - Part B
- Key Takeaways
3. Data Preprocessing
- Learning Objectives
- Data Exploration: Loading Files
- Demo: Importing and Storing Data
- Practice: Automobile Data Exploration I
- Data Exploration Techniques: Part 1
- Data Exploration Techniques: Part 2
- Seaborn
- Demo: Correlation Analysis
- Practice: Automobile Data Exploration II
- Data Wrangling
- Missing Values in a Dataset
- Outlier Values in a Dataset
- Demo: Outlier and Missing Value Treatment
- Practice: Data Exploration III
- Data Manipulation
- Functionalities of Data Object in Python: Part A
- Functionalities of Data Object in Python: Part B
- Different Types of Joins
- Typecasting
- Demo: Labor Hours Comparison
- Practice: Data Manipulation
- Key Takeaways
- Lesson-end project: Storing Test Results
4. Supervised Learning
- Learning Objectives
- Supervised Learning
- Supervised Learning- Real-Life Scenario
- Understanding the Algorithm
- Supervised Learning Flow
- Types of Supervised Learning – Part A
- Types of Supervised Learning – Part B
- Types of Classification Algorithms
- Types of Regression Algorithms - Part A
- Regression Use Case
- Accuracy Metrics
- Cost Function
- Evaluating Coefficients
- Demo: Linear Regression
- Practice: Boston Homes I
- Challenges in Prediction
- Types of Regression Algorithms - Part B
- Demo: Bigmart
- Practice: Boston Homes II
- Logistic Regression - Part A
- Logistic Regression - Part B
- Sigmoid Probability
- Accuracy Matrix
- Demo: Survival of Titanic Passengers
- Practice: Iris Species
- Key Takeaways
- Lesson-end Project: Health Insurance Cost
5. Feature Engineering
- Learning Objectives
- Feature Selection
- Regression
- Factor Analysis
- Factor Analysis Process
- Principal Component Analysis (PCA)
- First Principal Component
- Eigenvalues and PCA
- Demo: Feature Reduction
- Practice: PCA Transformation
- Linear Discriminant Analysis
- Maximum Separable Line
- Find Maximum Separable Line
- Demo: Labeled Feature Reduction
- Practice: LDA Transformation
- Key Takeaways
- Lesson-end Project: Simplifying Cancer Treatment
6. Supervised Learning: Classification
- Overview of Classification
- Classification: A Supervised Learning Algorithm
- Use Cases
- Classification Algorithms
- Decision Tree Classifier
- Decision Tree: Examples
- Decision Tree Formation
- Learning Objectives
- Choosing the Classifier
- Overfitting of Decision Trees
- Random Forest Classifier- Bagging and Bootstrapping
- Decision Tree and Random Forest Classifier
- Performance Measures: Confusion Matrix
- Performance Measures: Cost Matrix
- Demo: Horse Survival
- Practice: Loan Risk Analysis
- Naive Bayes Classifier
- Steps to Calculate Posterior Probability: Part A
- Steps to Calculate Posterior Probability: Part B
- Support Vector Machines: Linear Separability
- Support Vector Machines: Classification Margin
- Linear SVM: Mathematical Representation
- Non-linear SVMs
- The Kernel Trick
- Demo: Voice Classification
- Practice: College Classification
- Key Takeaways
- Lesson-end Project: Classify Kinematic Data
7. Unsupervised Learning
- Learning Objectives
- Overview
- Example and Applications of Unsupervised Learning
- Clustering
- Hierarchical Clustering
- Hierarchical Clustering: Example
- Demo: Clustering Animals
- Practice: Customer Segmentation
- K-means Clustering
- Optimal Number of Clusters
- Demo: Cluster-Based Incentivization
- Practice: Image Segmentation
- Key Takeaways
- Lesson-end Project: Clustering Image Data
8. Time Series Modeling
- Learning Objectives
- Overview of Time Series Modeling
- Time Series Pattern Types Part A
- Time Series Pattern Types Part B
- White Noise
- Stationarity
- Removal of Non-Stationarity
- Demo: Air Passengers I
- Practice: Beer Production I
- Time Series Models Part A
- Time Series Models Part B
- Time Series Models Part C
- Steps in Time Series Forecasting
- Demo: Air Passengers II
- Practice: Beer Production II
- Key Takeaways
- Lesson-end Project: IMF Commodity Price Forecast
9. Ensemble Learning
- Learning Objectives
- Overview
- Ensemble Learning Methods Part A
- Ensemble Learning Methods Part B
- Working of AdaBoost
- AdaBoost Algorithm and Flowchart
- Gradient Boosting
- XGBoost
- XGBoost Parameters Part A
- XGBoost Parameters Part B
- Demo: Pima Indians Diabetes
- Practice: Linearly Separable Species
- Model Selection
- Common Splitting Strategies
- Demo: Cross-Validation
- Practice: Model Selection
- Key Takeaways
- Lesson-end Project: Tuning Classifier Model with XGBoost
10. Recommender Systems
- Learning Objectives
- Introduction
- Purposes of Recommender Systems
- Paradigms of Recommender Systems
- Collaborative Filtering Part A
- Collaborative Filtering Part B
- Association Rule Mining
- Association Rule Mining: Market Basket Analysis
- Association Rule Generation: Apriori Algorithm
- Apriori Algorithm Example: Part A
- Apriori Algorithm Example: Part B
- Apriori Algorithm: Rule Selection
- Demo: User-Movie Recommendation Model
- Practice: Movie-Movie recommendation
- Key Takeaways
- Lesson-end Project: Book Rental Recommendation
11. Text Mining
- Learning Objectives
- Overview of Text Mining
- Significance of Text Mining
- Applications of Text Mining
- Natural Language Toolkit Library
- Text Extraction and Preprocessing: Tokenization
- Text Extraction and Preprocessing: N-grams
- Text Extraction and Preprocessing: Stop Word Removal
- Text Extraction and Preprocessing: Stemming
- Text Extraction and Preprocessing: Lemmatization
- Text Extraction and Preprocessing: POS Tagging
- Text Extraction and Preprocessing: Named Entity Recognition
- NLP Process Workflow
- Demo: Processing Brown Corpus
- Practice: Wiki Corpus
- Structuring Sentences: Syntax
- Rendering Syntax Trees
- Structuring Sentences: Chunking and Chunk Parsing
- NP and VP Chunk and Parser
- Structuring Sentences: Chinking
- Context-Free Grammar (CFG)
- Demo: Twitter Sentiments
- Practice: Airline Sentiment
- Key Takeaways
- Lesson-end Project: FIFA World Cup
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- Migrating the current Linux environment to AWS/RHEL Linux environment and used ELB and Auto scaling feature.
Overview of Machine Learning Training Course
- Our Machine Learning certification training program provides comprehensive training in the field of machine learning, with a focus on real-world applications. Our experienced trainers will guide you through the fundamentals of machine learning, including data preprocessing, model selection, and evaluation.
- During the training, you'll gain hands-on experience with popular machine learning algorithms and tools, including TensorFlow, Keras, and sci-kit-learn. You'll also learn how to build and deploy machine learning models in cloud environments like Azure.
- Our training program is designed to help you develop the skills you need to succeed in a variety of industries, from healthcare to finance to retail. With a strong foundation in machine learning, you'll be well-positioned to take advantage of the many job opportunities in this rapidly-growing field.
Benefits of learning Machine Learning
- Our Machine Learning certification training program provides comprehensive training in the field of machine learning, with a focus on real-world applications. Our experienced trainers will guide you through the fundamentals of machine learning, including data preprocessing, model selection, and evaluation.
- During the training, you'll gain hands-on experience with popular machine learning algorithms and tools, including TensorFlow, Keras, and sci-kit-learn. You'll also learn how to build and deploy machine learning models in cloud environments like Azure.
- Our training program is designed to help you develop the skills you need to succeed in a variety of industries, from healthcare to finance to retail. With a strong foundation in machine learning, you'll be well-positioned to take advantage of the many job opportunities in this rapidly-growing field.
Related job roles
- Data Engineer
- Data Scientist
- Data Analyst
- Applied Machine Learning Engineer
- Data Science Lead Manager
- Natural Language Processing Scientist
Machine Learning
How to become a machine learning expert?
By applying for this course – it is as simple as that! With this course, you will be able to have a detailed insight into the various ML methodologies and intricacies.
Is it a good decision to pursue a career in machine learning?
Every jaw-dropping technology around you starting from your google assistant to your YouTube video recommendations runs on systems running machine learning frameworks and this is only the beginning. Hence, if you take this course now, chances are really good that you will be at the front and center when ML becomes the norm.
Machine Learning
What is the definition of machine learning?
When a system uses Artificial Intelligence to learn from past mistakes and take productive decisions in the present and future without taking any help from programmer-written source code then this is known as Machine Learning.
Do I need to know how to code if I want to learn machine learning?
Yes, you would need to be proficient in programming languages like Python if you want to ace this course.
