As part of this course, you will master below skills, and you will become a successful Machine learning Engineer:
Our ML training program is curated and designed for:
Everyone can take up this Machine Learning certification course regardless of their prior knowledge and experience.
In the world we live in today, ML has proved itself to be among the hottest and demanding technologies available out there.
Hence, by leveraging Intellipaat’s Machine Learning online training, you will be exposed to numerous high-paying job opportunities.
Through our Machine Learning training, you will master the key concepts of Machine learning such as Python programming, supervised and unsupervised learning, Naive Bayes, NLP, Deep Learning fundamentals, time series analysis, and more. Each session ends with assignments and tasks that you need to solve based on the available dataset. Further, you will work on many industry-specific projects that will solidify your skills and help you find a rewarding job! Also, we will help you in your career with our exclusive job support services.
Intellipaat offers one of the best online Machine Learning courses that will help you become proficient in the ML domain. Our expert instructors will make sure that you are familiar with the course modules. On top of that, you will be working on real-world projects that would further enhance your understanding.
1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression
1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set
3.1 Introduction to classification
3.2 Linear regression vs Logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate
4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-learn to build a decision tree and a random forest
3. Visualizing the tree and changing the hyper-parameters in the random forest
5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM
1. Using Python library Scikit-learn to build a Naïve Bayes classifier and a support vector classifier
6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA
1. Using Python library Scikit-learn to implement k-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset
7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification
1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive
8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of phenomenon
3. Forecasting the future values in the series
Project 01: Analyzing the Trends of COVID-19 with Python
Problem Statement: Understanding the trends of COVID-19 spread and checking if restrictions imposed by governments around the world have helped us curb COVID-19 cases and by what degree
Topics: In this project, we will use Data Science and Python and perform visualizations to better understand the data on COVID-19. We will also use time series analysis to make predictions about future cases.
Project 02: Customer Churn Classification
Topics: This is a real-world project that gives you hands-on experience in working with most of the Machine Learning algorithms.
Project 03: Creating a Recommendation System for Movies
Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project requires you to deeply understand information filtering, recommender systems, user ‘preference’, and more. You will exclusively work on data related to user details, movie details, and others.
Case Study 01: Decision Tree
Topics: Understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by using Scikit-learn
Case Study 02: Insurance Cost Prediction (Linear Regression)
Topics: Understand the structure of a medical insurance dataset, implement both simple and multiple linear regressions, and predict values.
Case Study 03: Diabetes Classification (Logistic Regression)
Topics: Understand the structure of a dataset (PIMA Indians Diabetes dataset); implement multiple logistic regressions and classify; fit your model on the test and train data for prediction; evaluate your model using confusion matrix, and then visualize it
Case Study 04: Random Forest
Topics: Create a model that would help in classifying whether a patient ‘is normal,’ ‘is suspected to have a disease,’ or in actuality ‘has the disease’ using the ‘Cardiotocography’ dataset
Case Study 05: Principal Component Analysis (PCA)
Topics: Read the sample Iris dataset; use PCA to figure out the number of most important principal features, and then reduce the number of features using PCA; train and test the random forest classifier algorithm to check if reducing the number of dimensions is causing the model to perform poorly, and figure out the most optimal number that produces good quality results and predicts accurately
Case Study 06: K-means Clustering
Topics: Analyze data; extract useful columns from the dataset; visualize the data; find out the appropriate number of groups or clusters for the data to be segmented into (using the elbow method); using k-means clustering, segment the data into k groups (k is found in the previous step); visualize a scatter plot of clusters, and a lot more
Intellipaat’s Machine Learning certification is well recognized across more than 500+ top MNCs. As part of this ML training, you will be engaged in various projects and assignments, which include real-world industry scenarios. This way, you can expedite your career effortlessly.
Intellipaat’s Course Completion Certificate will be issued once you successfully work on the project (after expert review) and score at least 60 percent in the quiz.
You would be glad to know that Intellipaat’s certification is recognized by more than 100 top MNCs, including Cisco, Ericsson, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered Bank, IBM, Infosys, Genpact, TCS, Hexaware, and more.
Our Alumni works at top 3000+ companies
Intellipaat provides comprehensive Machine Learning training through hands-on projects and case studies. A few of the many reasons for choosing Intellipaat ML course includes: