title
Complete Roadmap for Machine Learning | ML Roadmap for Beginners

description
Machine Learning Engineer Roadmap: Step by step 6 months learning roadmap for machine learning engineer career. Most of the resources mentioned in this roadmap are free resources. Please follow below steps for learning requires skills for machine learning engineer: https://github.com/codebasics/roadmaps/blob/master/machine-learning-engineer-roadmap-2021/ml_engineer_roadmap_2021.md ⭐️ Timestamps ⭐️ 0:00 Why machine learning? 0:45 Computer Science Fundamentals 1:39 Programming skills 2:22 Data Structure and Algorithms 5:13 Databases 7:51 Numpy, Pandas, matplotlib 11:06 Math and Statistics for ML 12:36 Machine learning 16:19 Deep learning 18:41 Use ML Lifecycle tools Extra Tips ========== * Discord server: Making group and buddies * Participate in kaggle competitions and solve problems Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. 🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. 🔖Hashtags🔖 #machinelearningroadmap #machinelearning #mlroadmap #mlengineer #roadmaptomachinelearning #completeroadmapformachinelearning #mlengineerroadmap 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/ 📸 Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/ 📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/ ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

detail
{'title': 'Complete Roadmap for Machine Learning | ML Roadmap for Beginners', 'heatmap': [{'end': 84.706, 'start': 38.454, 'weight': 1}, {'end': 997.559, 'start': 979.22, 'weight': 0.78}, {'end': 1199.151, 'start': 1181.138, 'weight': 0.773}], 'summary': 'Presents a six-month roadmap for becoming a machine learning engineer, highlighting potential earnings of $800-900k/year, foundational steps including computer science basics and python, advanced programming concepts, machine learning learning path, mlops tools, effective learning tips, and the importance of soft skills for success.', 'chapters': [{'end': 142.205, 'segs': [{'end': 84.706, 'src': 'heatmap', 'start': 0.009, 'weight': 0, 'content': [{'end': 8.254, 'text': 'you are ready to put four to five hours of study every day, then how can you build machine learning engineer skills in six months?', 'start': 0.009, 'duration': 8.245}, {'end': 11.857, 'text': 'we are going to discuss that step by step roadmap today.', 'start': 8.254, 'duration': 3.603}, {'end': 17.24, 'text': 'machine learning engineer is a very specialized skill and they get paid the most.', 'start': 11.857, 'duration': 5.383}, {'end': 23.004, 'text': "as of 2021, if you're working less in google, facebook or any top product companies,", 'start': 17.24, 'duration': 5.764}, {'end': 32.911, 'text': 'you can earn up to 800 to 900 thousand dollar a year as a machine learning engineer, and They are more than you know doctors or surgeons.', 'start': 23.004, 'duration': 9.907}, {'end': 38.134, 'text': 'So therefore, the effort that you need to put is going to be humongous here.', 'start': 33.291, 'duration': 4.843}, {'end': 45.778, 'text': 'But in six months you can develop a basic foundation and then after it becomes a lifelong learning journey.', 'start': 38.454, 'duration': 7.324}, {'end': 52.005, 'text': 'The diagram I have shown here quickly summarizes the exact steps in your machine learning journey.', 'start': 46.118, 'duration': 5.887}, {'end': 55.088, 'text': "So let's start with computer science fundamentals.", 'start': 52.405, 'duration': 2.683}, {'end': 60.374, 'text': "I am assuming that you don't know anything about computer science, you are starting from scratch.", 'start': 55.589, 'duration': 4.785}, {'end': 68.718, 'text': 'and the first thing you need to learn is computer science basics what is bits and bytes, how ram and cpu works,', 'start': 61.255, 'duration': 7.463}, {'end': 72.079, 'text': 'the basic fundamentals about internet and so on,', 'start': 68.718, 'duration': 3.361}, {'end': 80.743, 'text': 'and for that we have an excellent khan academy course where you need to follow the first four sections digital information,', 'start': 72.079, 'duration': 8.664}, {'end': 84.706, 'text': 'the internet programming and the algorithms.', 'start': 81.443, 'duration': 3.263}], 'summary': 'Learn machine learning engineer skills in 6 months for high-paying jobs, starting with computer science basics and khan academy courses.', 'duration': 68.709, 'max_score': 0.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY9.jpg'}], 'start': 0.009, 'title': 'Becoming a machine learning engineer', 'summary': 'Outlines a step-by-step roadmap to become a machine learning engineer in six months, emphasizing the potential earnings of up to 800 to 900 thousand dollars a year and the foundational steps including computer science basics and python programming language.', 'chapters': [{'end': 142.205, 'start': 0.009, 'title': 'Machine learning engineer in 6 months', 'summary': 'Outlines a step-by-step roadmap to become a machine learning engineer in six months, emphasizing the potential earnings of up to 800 to 900 thousand dollars a year and the foundational steps including computer science basics and python programming language.', 'duration': 142.196, 'highlights': ['The potential earnings of up to 800 to 900 thousand dollars a year as a machine learning engineer are emphasized, making it a highly paid and specialized skill. Machine learning engineers can earn up to 800 to 900 thousand dollars a year, positioning it as one of the highest paying and specialized skills in the industry.', 'The foundational steps for becoming a machine learning engineer are outlined, including learning computer science basics and Python programming language. The foundational steps for aspiring machine learning engineers include learning computer science basics such as bits and bytes, as well as mastering the Python programming language, which is essential for the role.', 'The importance of dedicating four to five hours of study every day to build machine learning engineer skills in six months is emphasized. It is emphasized that dedicating four to five hours of study every day is crucial to building machine learning engineer skills within a six-month timeframe.']}], 'duration': 142.196, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY9.jpg', 'highlights': ['Machine learning engineers can earn up to 800 to 900 thousand dollars a year, positioning it as one of the highest paying and specialized skills in the industry.', 'The foundational steps for aspiring machine learning engineers include learning computer science basics such as bits and bytes, as well as mastering the Python programming language, which is essential for the role.', 'It is emphasized that dedicating four to five hours of study every day is crucial to building machine learning engineer skills within a six-month timeframe.']}, {'end': 448.086, 'segs': [{'end': 220.009, 'src': 'embed', 'start': 142.905, 'weight': 0, 'content': [{'end': 150.71, 'text': 'A good software engineer has an important skill of a solid understanding of data structure and algorithms.', 'start': 142.905, 'duration': 7.805}, {'end': 159.196, 'text': 'In data structure algorithms, you need to know about basic data structures such as array, list, hash map, tree, graph, etc.', 'start': 151.431, 'duration': 7.765}, {'end': 169.303, 'text': 'And for algorithms you need to know about. you know bubble short, various shorting and searching algorithm, binary search selection short and so on.', 'start': 159.816, 'duration': 9.487}, {'end': 172.926, 'text': 'and you need to have understanding of big o notation.', 'start': 170.103, 'duration': 2.823}, {'end': 179.772, 'text': 'you know how much memory and and cpu resources a particular program uses in terms of big o complexity,', 'start': 172.926, 'duration': 6.846}, {'end': 184.376, 'text': 'because machine learning projects are very compute intensive.', 'start': 179.772, 'duration': 4.604}, {'end': 191.739, 'text': 'if you, if you have a good understanding of big o notation, is gonna be super useful have created this github page.', 'start': 184.376, 'duration': 7.363}, {'end': 194.622, 'text': 'the link of this you will find in video description below.', 'start': 191.739, 'duration': 2.883}, {'end': 203.992, 'text': 'so first week, first, two, third and fourth week in computer science, fundamental computer coding tutorials, python coding especially.', 'start': 194.622, 'duration': 9.37}, {'end': 208.537, 'text': 'if you look at this playlist, you need to follow first 14 tutorials at this stage.', 'start': 203.992, 'duration': 4.545}, {'end': 220.009, 'text': 'so first 14 tutorials will cover concepts such as Variables, numbers, you know strings and if and for dictionaries, functions working with JSON, etc.', 'start': 208.537, 'duration': 11.472}], 'summary': 'Software engineer needs solid understanding of data structures, algorithms, and big o notation, especially for compute-intensive machine learning projects.', 'duration': 77.104, 'max_score': 142.905, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY142905.jpg'}, {'end': 280.538, 'src': 'embed', 'start': 247.929, 'weight': 4, 'content': [{'end': 251.13, 'text': 'so actually, the exercises are rather here.', 'start': 247.929, 'duration': 3.201}, {'end': 252.471, 'text': "i'll update the link.", 'start': 251.13, 'duration': 1.341}, {'end': 264.134, 'text': 'so if you look at this exercise read and write file and here the exercise gives a description of what the exercise is about and there is a link of solution.', 'start': 252.471, 'duration': 11.663}, {'end': 271.276, 'text': 'so you can practice this problem on your own and then verify your answer by comparing your solution with my solution.', 'start': 264.134, 'duration': 7.142}, {'end': 274.516, 'text': 'and there are multiple exercises in many tutorials.', 'start': 271.276, 'duration': 3.24}, {'end': 280.538, 'text': "this allows you to practice the code or the concept that you're learning by watching these videos.", 'start': 274.516, 'duration': 6.022}], 'summary': 'Practice exercises with solutions provided for multiple tutorials.', 'duration': 32.609, 'max_score': 247.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY247929.jpg'}, {'end': 370.868, 'src': 'embed', 'start': 341.227, 'weight': 5, 'content': [{'end': 349.453, 'text': "it's a free course where you can practice certain problems on and You can just develop a basic understanding of SQL.", 'start': 341.227, 'duration': 8.226}, {'end': 353.196, 'text': 'Kuda Venkat YouTube channel also has a nice playlist.', 'start': 349.693, 'duration': 3.503}, {'end': 358.819, 'text': 'So you follow all of that in week 5 and 6 and develop a good understanding of SQL.', 'start': 353.236, 'duration': 5.583}, {'end': 370.868, 'text': 'Then you spend week 7 and 8 in learning some advanced or level 2 programming concepts such as exception, generators and so on.', 'start': 359.3, 'duration': 11.568}], 'summary': "Free sql course with basic and advanced concepts on kuda venkat's youtube channel.", 'duration': 29.641, 'max_score': 341.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY341227.jpg'}], 'start': 142.905, 'title': 'Computer science fundamentals and sql learning', 'summary': 'Emphasizes the significance of fundamental computer coding tutorials in python, including the need to follow the first 14 tutorials, and the importance of learning sql using khan academy and kuda venkat youtube channel in weeks 5-8.', 'chapters': [{'end': 194.622, 'start': 142.905, 'title': 'Importance of data structure & algorithms', 'summary': 'Emphasizes the significance of understanding data structure and algorithms, including knowledge of basic data structures like array, list, hash map, tree, and graph, as well as familiarity with sorting and searching algorithms, and the importance of big o notation in evaluating the computational complexity of programs for resource management in machine learning projects.', 'duration': 51.717, 'highlights': ['Understanding of basic data structures such as array, list, hash map, tree, graph, etc. is crucial for a good software engineer.', 'Familiarity with sorting and searching algorithms like bubble sort, various sorting and searching algorithms, binary search, selection sort, etc., is essential for a solid understanding of data structure algorithms.', 'Importance of big O notation in evaluating the computational complexity of programs for resource management in machine learning projects.']}, {'end': 448.086, 'start': 194.622, 'title': 'Computer science fundamentals and sql learning', 'summary': 'Covers fundamental computer coding tutorials in python, including the need to follow the first 14 tutorials, along with the importance of exercises and practical learning. additionally, it emphasizes the significance of learning sql using khan academy and kuda venkat youtube channel in weeks 5-8.', 'duration': 253.464, 'highlights': ['The importance of following the first 14 tutorials in Python coding for fundamental understanding The first 14 tutorials cover concepts such as Variables, numbers, strings, if and for dictionaries, functions working with JSON, etc.', 'Emphasis on the significance of exercises for practical learning and understanding Provides access to various exercises related to different tutorials for practicing and verifying solutions.', 'Importance of learning SQL using Khan Academy and Kuda Venkat YouTube channel in weeks 5-8 Recommendation to follow the Khan Academy course and Kuda Venkat YouTube channel for developing a good understanding of SQL in weeks 5-8.']}], 'duration': 305.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY142905.jpg', 'highlights': ['Understanding of basic data structures such as array, list, hash map, tree, graph, etc. is crucial for a good software engineer.', 'Familiarity with sorting and searching algorithms like bubble sort, various sorting and searching algorithms, binary search, selection sort, etc., is essential for a solid understanding of data structure algorithms.', 'Importance of big O notation in evaluating the computational complexity of programs for resource management in machine learning projects.', 'The importance of following the first 14 tutorials in Python coding for fundamental understanding The first 14 tutorials cover concepts such as Variables, numbers, strings, if and for dictionaries, functions working with JSON, etc.', 'Emphasis on the significance of exercises for practical learning and understanding Provides access to various exercises related to different tutorials for practicing and verifying solutions.', 'Importance of learning SQL using Khan Academy and Kuda Venkat YouTube channel in weeks 5-8 Recommendation to follow the Khan Academy course and Kuda Venkat YouTube channel for developing a good understanding of SQL in weeks 5-8.']}, {'end': 925.235, 'segs': [{'end': 519.969, 'src': 'embed', 'start': 471.393, 'weight': 0, 'content': [{'end': 478.536, 'text': 'At the end of week 8, you know about little bit advanced programming, you know about SQL, data structure algorithm.', 'start': 471.393, 'duration': 7.143}, {'end': 483.178, 'text': 'You have built a solid base for your software engineering skills.', 'start': 479.097, 'duration': 4.081}, {'end': 493.223, 'text': 'Now week 9 to 12, you need to spend in learning NumPy, Pandas and data visualization library which could be Matplotlib or Seaborn.', 'start': 483.739, 'duration': 9.484}, {'end': 497.567, 'text': 'numpy and pandas are numeric compute libraries.', 'start': 493.843, 'duration': 3.724}, {'end': 505.315, 'text': 'these are used heavily in data science for doing data cleaning or number crunching, and these are very fast libraries.', 'start': 497.567, 'duration': 7.748}, {'end': 513.164, 'text': 'so you need to have good understanding of these libraries and for data visualization you can either use matplotlib or seaborn.', 'start': 505.315, 'duration': 7.849}, {'end': 519.969, 'text': 'so here are some of the resources that you can use to learn numpy, pandas and data visualization libraries.', 'start': 513.563, 'duration': 6.406}], 'summary': 'Week 9-12 focus on learning numpy, pandas, and data visualization for data science, essential for fast data cleaning and analysis.', 'duration': 48.576, 'max_score': 471.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY471393.jpg'}, {'end': 738.533, 'src': 'embed', 'start': 691.75, 'weight': 3, 'content': [{'end': 698.454, 'text': 'There is another book called Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce.', 'start': 691.75, 'duration': 6.704}, {'end': 699.755, 'text': 'You can read that book.', 'start': 698.814, 'duration': 0.941}, {'end': 708.239, 'text': "Now, the reason I'm suggesting only two weeks for math and stats is because As I said, you can spend maybe two years.", 'start': 700.175, 'duration': 8.064}, {'end': 713.261, 'text': 'There are people who do PhD in statistics, so you can spend five years just studying math and statistics.', 'start': 708.379, 'duration': 4.882}, {'end': 718.644, 'text': "But you don't want to get yourself stuck into that ocean.", 'start': 713.321, 'duration': 5.323}, {'end': 725.447, 'text': 'So you just spend two weeks studying basic concepts, inferential and descriptive statistics.', 'start': 719.164, 'duration': 6.283}, {'end': 727.829, 'text': 'linear algebra, calculus.', 'start': 726.368, 'duration': 1.461}, {'end': 731.69, 'text': "And after that, as you're working on your real ML project,", 'start': 728.229, 'duration': 3.461}, {'end': 738.533, 'text': 'you can always come back here and refresh your concepts and learn new concepts of math and statistics.', 'start': 731.69, 'duration': 6.843}], 'summary': 'Spend two weeks on basic math and stats concepts before working on ml projects.', 'duration': 46.783, 'max_score': 691.75, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY691750.jpg'}, {'end': 790.719, 'src': 'embed', 'start': 764.141, 'weight': 4, 'content': [{'end': 768.662, 'text': "Now, when I say machine learning, of course I'm talking about statistical machine learning,", 'start': 764.141, 'duration': 4.521}, {'end': 772.343, 'text': 'where you will be using scikit-learn in Python for doing machine learning.', 'start': 768.662, 'duration': 3.681}, {'end': 775.939, 'text': 'You will be building classification and regression model.', 'start': 772.863, 'duration': 3.076}, {'end': 781.947, 'text': 'When I talk about classification, decision tree, random forest, there are so many concepts that you need to go through.', 'start': 775.959, 'duration': 5.988}, {'end': 790.719, 'text': 'So basically one month for machine learning should be good enough in this fast-paced six-month ML roadmap.', 'start': 782.227, 'duration': 8.492}], 'summary': '1 month for machine learning in 6-month ml roadmap with scikit-learn, classification, regression models', 'duration': 26.578, 'max_score': 764.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY764141.jpg'}], 'start': 448.086, 'title': 'Advanced programming, numpy, pandas, and data visualization', 'summary': 'Covers advanced programming concepts from week 15 to 27, including exception handling, class objects, inheritance, iterators, generators, decorators, multi-threading, and multi-processing. it emphasizes the importance of learning numpy and pandas for data science, providing resources for learning and practice. additionally, it outlines a six-month roadmap for aspiring machine learning engineers, emphasizing the importance of spending two weeks on mathematics and statistics, followed by a month on machine learning, and provides resources for learning and practice.', 'chapters': [{'end': 665.872, 'start': 448.086, 'title': 'Advanced programming, numpy, pandas, and data visualization', 'summary': 'Covers advanced programming concepts from week 15 to 27, including exception handling, class objects, inheritance, iterators, generators, decorators, multi-threading, and multi-processing. it also emphasizes the importance of learning numpy and pandas for data science, providing resources for learning and practice.', 'duration': 217.786, 'highlights': ['The chapter covers advanced programming concepts from week 15 to 27, including exception handling, class objects, inheritance, iterators, generators, decorators, multi-threading, and multi-processing. The advanced programming concepts covered from week 15 to 27 provide a solid base for software engineering skills and enable the writing of more advanced programs.', 'Emphasizes the importance of learning NumPy and Pandas for data science, providing resources for learning and practice. The emphasis on learning NumPy and Pandas highlights their significance in data science for data cleaning, number crunching, and data visualization, with suggestions for learning resources and real-life project applications.', 'Provides resources for learning NumPy, Pandas, and data visualization libraries, including simple playlists and tutorials for quick learning. The provided resources for learning NumPy, Pandas, and data visualization libraries, along with suggestions for using Kaggle for practice, offer a comprehensive approach to mastering these essential tools for data science.']}, {'end': 925.235, 'start': 666.072, 'title': 'Ml engineer skills roadmap', 'summary': 'Outlines a six-month roadmap for aspiring machine learning engineers, emphasizing the importance of spending two weeks on mathematics and statistics, followed by a month on machine learning, and provides resources for learning and practice.', 'duration': 259.163, 'highlights': ['Aspiring ML engineers should spend two weeks on mathematics and statistics, focusing on basic concepts such as inferential and descriptive statistics, linear algebra, and calculus. The chapter suggests dedicating two weeks to studying fundamental math and statistics concepts, such as inferential and descriptive statistics, linear algebra, and calculus, before delving into machine learning. This approach allows for a foundational understanding without getting stuck in an extensive study of math and statistics, aligning with the fast-paced six-month ML roadmap.', 'After the initial two weeks, aspiring ML engineers are advised to spend one month on learning machine learning, covering topics such as statistical machine learning, scikit-learn, and building classification and regression models. Following the foundational math and statistics study, the chapter recommends allocating one month to delve into machine learning, with a focus on statistical machine learning, utilizing scikit-learn in Python, and constructing classification and regression models. This structured approach facilitates a comprehensive understanding of machine learning concepts within the six-month roadmap.', "The chapter provides various resources for learning, including a YouTube playlist covering fundamental math and statistics concepts, a recommended book titled 'Practical Statistics for Data Scientists', and free resources on machine learning, such as videos and projects. The chapter furnishes multiple resources for learning, including a YouTube playlist concentrating on essential math and statistics topics, a recommended book titled 'Practical Statistics for Data Scientists' by Peter Bruce and Andrew Bruce, along with free resources on machine learning encompassing videos and projects. These resources offer comprehensive support for aspiring ML engineers to enhance their understanding and practical application of key concepts."]}], 'duration': 477.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY448086.jpg', 'highlights': ['The advanced programming concepts covered from week 15 to 27 provide a solid base for software engineering skills and enable the writing of more advanced programs.', 'The emphasis on learning NumPy and Pandas highlights their significance in data science for data cleaning, number crunching, and data visualization, with suggestions for learning resources and real-life project applications.', 'The provided resources for learning NumPy, Pandas, and data visualization libraries, along with suggestions for using Kaggle for practice, offer a comprehensive approach to mastering these essential tools for data science.', 'The chapter suggests dedicating two weeks to studying fundamental math and statistics concepts, such as inferential and descriptive statistics, linear algebra, and calculus, before delving into machine learning. This approach allows for a foundational understanding without getting stuck in an extensive study of math and statistics, aligning with the fast-paced six-month ML roadmap.', 'Following the foundational math and statistics study, the chapter recommends allocating one month to delve into machine learning, with a focus on statistical machine learning, utilizing scikit-learn in Python, and constructing classification and regression models.', "The chapter furnishes multiple resources for learning, including a YouTube playlist concentrating on essential math and statistics topics, a recommended book titled 'Practical Statistics for Data Scientists' by Peter Bruce and Andrew Bruce, along with free resources on machine learning encompassing videos and projects."]}, {'end': 1048.328, 'segs': [{'end': 1016.426, 'src': 'heatmap', 'start': 979.22, 'weight': 0, 'content': [{'end': 985.446, 'text': 'you can spend next one month, week 19 to 22, in deep learning.', 'start': 979.22, 'duration': 6.226}, {'end': 987.929, 'text': 'so deep learning is all about neural networks.', 'start': 985.446, 'duration': 2.483}, {'end': 997.559, 'text': 'you need to learn about computer vision or convolutional neural network models as well as NLP, which is using RNN transformer, etc.', 'start': 987.929, 'duration': 9.63}, {'end': 1001.602, 'text': 'and then week 23 to 24 is is about ml ops.', 'start': 997.559, 'duration': 4.043}, {'end': 1005.363, 'text': 'you need to learn one ml ops tool such as ml flow.', 'start': 1001.602, 'duration': 3.761}, {'end': 1006.583, 'text': 'ml flow is one example.', 'start': 1005.363, 'duration': 1.22}, {'end': 1008.304, 'text': 'there are other tools as well.', 'start': 1006.583, 'duration': 1.721}, {'end': 1016.426, 'text': 'so when you learn that, you get a full understanding of how you can use to that tool for the whole machine learning life cycle management.', 'start': 1008.304, 'duration': 8.122}], 'summary': 'Spend 1 month on deep learning, covering neural networks, computer vision, and nlp, then focus on ml ops for 2 weeks, learning tools like mlflow.', 'duration': 37.206, 'max_score': 979.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY979220.jpg'}], 'start': 925.235, 'title': 'Machine learning learning path', 'summary': 'Covers a machine learning learning path, including topics like linear regression, image classification, feature engineering, logistic regression, deep learning, neural networks, computer vision, nlp, and ml ops, with a focus on tools such as ml flow, tensorflow, and pytorch.', 'chapters': [{'end': 1048.328, 'start': 925.235, 'title': 'Machine learning learning path', 'summary': 'Covers a machine learning learning path, including topics like linear regression, image classification, feature engineering, logistic regression, deep learning, neural networks, computer vision, nlp, and ml ops, with a focus on tools such as ml flow, tensorflow, and pytorch.', 'duration': 123.093, 'highlights': ['The chapter covers a machine learning learning path, including topics like linear regression, image classification, feature engineering, logistic regression, deep learning, neural networks, computer vision, NLP, and ml ops. The learning path includes a wide range of machine learning topics, from linear regression to deep learning and ml ops.', 'Focus on tools such as ml flow, TensorFlow, and PyTorch. The chapter emphasizes the use of tools like ml flow, TensorFlow, and PyTorch for machine learning tasks.', 'The chapter emphasizes the importance of understanding neural networks, computer vision, NLP, and ml ops. It stresses the significance of comprehending neural networks, computer vision, NLP, and ml ops in the machine learning field.']}], 'duration': 123.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY925235.jpg', 'highlights': ['Covers a machine learning learning path, including topics like linear regression, image classification, feature engineering, logistic regression, deep learning, neural networks, computer vision, NLP, and ml ops.', 'The learning path includes a wide range of machine learning topics, from linear regression to deep learning and ml ops.', 'Focus on tools such as ml flow, TensorFlow, and PyTorch.', 'The chapter emphasizes the use of tools like ml flow, TensorFlow, and PyTorch for machine learning tasks.', 'It stresses the significance of comprehending neural networks, computer vision, NLP, and ml ops in the machine learning field.', 'The chapter emphasizes the importance of understanding neural networks, computer vision, NLP, and ml ops.']}, {'end': 1344.932, 'segs': [{'end': 1149.196, 'src': 'embed', 'start': 1126.303, 'weight': 1, 'content': [{'end': 1141.269, 'text': 'because many companies have humongous databases which are distributed in hadoop cluster and you need to use the distributed computing using pi spark docker and containerizations are important concept that you need to have knowledge on.', 'start': 1126.303, 'duration': 14.966}, {'end': 1145.032, 'text': 'CI-CD using Jenkins is one other thing.', 'start': 1141.949, 'duration': 3.083}, {'end': 1149.196, 'text': "that's an essential skill for any software engineer or ML engineer.", 'start': 1145.032, 'duration': 4.164}], 'summary': 'Distributed computing with hadoop, spark, docker, and ci-cd using jenkins are essential for software and ml engineers.', 'duration': 22.893, 'max_score': 1126.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY1126303.jpg'}, {'end': 1205.116, 'src': 'heatmap', 'start': 1181.138, 'weight': 0, 'content': [{'end': 1186.761, 'text': 'website, and here let me search for ml and ai engineer.', 'start': 1181.138, 'duration': 5.623}, {'end': 1193.127, 'text': 'okay, and here i am just sorting google.', 'start': 1186.761, 'duration': 6.366}, {'end': 1197.27, 'text': 'machine learning engineer elite earns one million dollar a year.', 'start': 1193.127, 'duration': 4.143}, {'end': 1199.151, 'text': 'one million dollar a year.', 'start': 1197.27, 'duration': 1.881}, {'end': 1200.933, 'text': "it's just too much money.", 'start': 1199.151, 'duration': 1.782}, {'end': 1205.116, 'text': 'so ml engineers get the highest salary in tech world.', 'start': 1200.933, 'duration': 4.183}], 'summary': 'Ml engineers earn $1 million/year, highest in tech world.', 'duration': 31.361, 'max_score': 1181.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY1181138.jpg'}, {'end': 1310.543, 'src': 'embed', 'start': 1228.604, 'weight': 2, 'content': [{'end': 1235.946, 'text': 'But the six month roadmap gives you a solid, strong base on which you can build additional skills.', 'start': 1228.604, 'duration': 7.342}, {'end': 1237.527, 'text': 'now becoming machine learning.', 'start': 1236.286, 'duration': 1.241}, {'end': 1240.21, 'text': 'engineer is not about technical skills.', 'start': 1237.527, 'duration': 2.683}, {'end': 1246.537, 'text': 'you need to know soft skills, and the first one is you need to understand the principles of effective learning.', 'start': 1240.21, 'duration': 6.327}, {'end': 1255.846, 'text': 'you have to learn so many things and there is less time, so you want to know how you can spend less amount of time and get maximum output.', 'start': 1246.537, 'duration': 9.309}, {'end': 1258.369, 'text': 'so here is a video on how to learn things effectively.', 'start': 1255.846, 'duration': 2.523}, {'end': 1267.411, 'text': "You know where you want to spend less time in your input, which is watching, let's say, tutorial videos, but more time in output, which is reflecting,", 'start': 1258.829, 'duration': 8.582}, {'end': 1268.732, 'text': 'implementing and sharing.', 'start': 1267.411, 'duration': 1.321}, {'end': 1273.113, 'text': 'I discuss various other things such as distraction free learning.', 'start': 1269.212, 'duration': 3.901}, {'end': 1277.974, 'text': 'For example, in your four to five hours of study, you will keep your mobile phone away.', 'start': 1273.133, 'duration': 4.841}, {'end': 1280.635, 'text': 'Mobile phone, cell phone is the biggest distraction.', 'start': 1278.254, 'duration': 2.381}, {'end': 1283.636, 'text': 'Another important tip is a group study.', 'start': 1281.395, 'duration': 2.241}, {'end': 1293.359, 'text': 'So if you click on this link and if you go on my Discord server here, you will find a partner and group finder chat.', 'start': 1283.776, 'duration': 9.583}, {'end': 1298.76, 'text': 'so in the discord server, if i go here partner and group finder here, you can make buddies.', 'start': 1293.359, 'duration': 5.401}, {'end': 1302.441, 'text': 'you know, you can form groups with people and do the study.', 'start': 1298.76, 'duration': 3.681}, {'end': 1303.701, 'text': "it's like going to a gym.", 'start': 1302.441, 'duration': 1.26}, {'end': 1310.543, 'text': 'if you go to gym alone, you will not be motivated, but if you go with your friends, you will be very much motivated.', 'start': 1303.701, 'duration': 6.842}], 'summary': 'A six-month roadmap for becoming a machine learning engineer emphasizes effective learning principles, including minimizing time spent on input and maximizing output through reflection, implementation, and sharing, as well as strategies like distraction-free learning and group study for motivation.', 'duration': 81.939, 'max_score': 1228.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY1228604.jpg'}, {'end': 1344.932, 'src': 'embed', 'start': 1332.789, 'weight': 6, 'content': [{'end': 1334.149, 'text': 'World is full of opportunity.', 'start': 1332.789, 'duration': 1.36}, {'end': 1336.53, 'text': 'The future of ML engineer is very, very bright.', 'start': 1334.229, 'duration': 2.301}, {'end': 1344.932, 'text': 'So I hope you can follow this roadmap and if you have any other question post in a comment below I wish you all the best.', 'start': 1336.87, 'duration': 8.062}], 'summary': 'The future of ml engineering looks very bright.', 'duration': 12.143, 'max_score': 1332.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY1332789.jpg'}], 'start': 1048.328, 'title': 'Ml engineer roadmap & effective learning tips', 'summary': 'Outlines a six-month roadmap to learn fundamental skills as an ml engineer, including rnn, cnn, mlops tools like mlflow, distributed computing with pi spark and hadoop, docker, ci-cd using jenkins, version control with git, fastapi, tensorflow serving, nosql databases, and emphasizes the importance of soft skills alongside the potential high salary of ml engineers. it also discusses effective learning tips, including spending less time on input and more on output, distraction-free learning, group study benefits, and the importance of discipline and perseverance for success in the field of ml engineering.', 'chapters': [{'end': 1246.537, 'start': 1048.328, 'title': 'Ml engineer roadmap & key skills', 'summary': 'Outlines a six-month roadmap to learn fundamental skills as an ml engineer, including rnn, cnn, mlops tools like mlflow, distributed computing with pi spark and hadoop, docker, ci-cd using jenkins, version control with git, fastapi, tensorflow serving, nosql databases, and emphasizes the importance of soft skills alongside the potential high salary of ml engineers.', 'duration': 198.209, 'highlights': ['Machine learning engineer elite earns one million dollar a year. Emphasizes the potential high salary of ML engineers.', 'MLOps tools like MLflow, distributed computing with Pi Spark and Hadoop, Docker, CI-CD using Jenkins, version control with Git, FastAPI, TensorFlow serving, NoSQL databases are essential skills for an ML engineer. Outlines the fundamental skills required for an ML engineer, including specific tools and technologies.', 'The six month roadmap gives you a solid, strong base on which you can build additional skills. Emphasizes the importance of the six-month roadmap as a foundation for further skill development.']}, {'end': 1344.932, 'start': 1246.537, 'title': 'Effective learning tips', 'summary': 'Discusses effective learning tips, including spending less time on input and more on output, distraction-free learning, group study benefits, and the importance of discipline and perseverance for success in the field of ml engineering.', 'duration': 98.395, 'highlights': ['The importance of spending less time on input, such as watching tutorial videos, and more time on output, including reflecting, implementing, and sharing, is emphasized for effective learning.', 'Distraction-free learning is recommended, such as keeping the mobile phone away during study hours, to enhance focus and productivity.', 'The benefits of group study are highlighted, with a comparison to going to a gym, emphasizing the motivational aspect of studying with friends and holding each other accountable.', 'The significance of discipline, hard work, and perseverance is emphasized, with a positive outlook on the abundant opportunities in the field of ML engineering.']}], 'duration': 296.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T4MLrtOKPjY/pics/T4MLrtOKPjY1048328.jpg', 'highlights': ['Machine learning engineer elite earns one million dollar a year. Emphasizes the potential high salary of ML engineers.', 'MLOps tools like MLflow, distributed computing with Pi Spark and Hadoop, Docker, CI-CD using Jenkins, version control with Git, FastAPI, TensorFlow serving, NoSQL databases are essential skills for an ML engineer. Outlines the fundamental skills required for an ML engineer, including specific tools and technologies.', 'The six month roadmap gives you a solid, strong base on which you can build additional skills. Emphasizes the importance of the six-month roadmap as a foundation for further skill development.', 'The importance of spending less time on input, such as watching tutorial videos, and more time on output, including reflecting, implementing, and sharing, is emphasized for effective learning.', 'Distraction-free learning is recommended, such as keeping the mobile phone away during study hours, to enhance focus and productivity.', 'The benefits of group study are highlighted, with a comparison to going to a gym, emphasizing the motivational aspect of studying with friends and holding each other accountable.', 'The significance of discipline, hard work, and perseverance is emphasized, with a positive outlook on the abundant opportunities in the field of ML engineering.']}], 'highlights': ['Machine learning engineers can earn up to 800 to 900 thousand dollars a year, positioning it as one of the highest paying and specialized skills in the industry.', 'The foundational steps for aspiring machine learning engineers include learning computer science basics such as bits and bytes, as well as mastering the Python programming language, which is essential for the role.', 'Understanding of basic data structures such as array, list, hash map, tree, graph, etc. is crucial for a good software engineer.', 'Importance of big O notation in evaluating the computational complexity of programs for resource management in machine learning projects.', 'The advanced programming concepts covered from week 15 to 27 provide a solid base for software engineering skills and enable the writing of more advanced programs.', 'The emphasis on learning NumPy and Pandas highlights their significance in data science for data cleaning, number crunching, and data visualization, with suggestions for learning resources and real-life project applications.', 'The chapter suggests dedicating two weeks to studying fundamental math and statistics concepts, such as inferential and descriptive statistics, linear algebra, and calculus, before delving into machine learning.', 'Covers a machine learning learning path, including topics like linear regression, image classification, feature engineering, logistic regression, deep learning, neural networks, computer vision, NLP, and ml ops.', 'MLOps tools like MLflow, distributed computing with Pi Spark and Hadoop, Docker, CI-CD using Jenkins, version control with Git, FastAPI, TensorFlow serving, NoSQL databases are essential skills for an ML engineer.', 'The six month roadmap gives you a solid, strong base on which you can build additional skills.', 'The importance of spending less time on input, such as watching tutorial videos, and more time on output, including reflecting, implementing, and sharing, is emphasized for effective learning.', 'Distraction-free learning is recommended, such as keeping the mobile phone away during study hours, to enhance focus and productivity.', 'The benefits of group study are highlighted, with a comparison to going to a gym, emphasizing the motivational aspect of studying with friends and holding each other accountable.', 'The significance of discipline, hard work, and perseverance is emphasized, with a positive outlook on the abundant opportunities in the field of ML engineering.']}